Smart Life and Smart Life Engineering

Elena Kornyshova, Rébecca Deneckère, Sjaak Brinkkemper

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Elena Kornyshova, Rébecca Deneckère, Sjaak Brinkkemper. Smart Life and Smart Life Engineering. Springer Nature Switzerland, 2025, 978-3-031-75887-4. 10.1007/978-3-031-75887-4. hal-04962610

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<figure><img src="image_1.png" /></figure>
Elena Kornyshova
Rébecca Deneckère

Sjaak Brinkkemper Editors

Smart Life and
Smart Life
Engineering

Current State and Future Vision

OPEN ACCESS

<figure><img src="image_2.png" /></figure>

Springer
Smart Life and
Smart Life
Engineering
Elena Kornyshova • Rébecca Deneckère •
Sjaak Brinkkemper

Editors

Smart Life and
Smart Life
Engineering

Current State and Future Vision

<figure><img src="image_3.png" /></figure>

Springer
Editors

Elena Kornyshova
Centre d'Etudes et de Recherche en
Informatique et Communications
Conservatoire National des Arts et Métiers
Paris, France

Rébecca Deneckère
Centre de Recherche en Informatique
Université Paris I Panthéon-Sorbonne
Paris, France

Sjaak Brinkkemper
Department of Information and Computing
Sciences
Utrecht University
Utrecht, The Netherlands

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ISBN 978-3-031-75886-7

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# Preface

Welcome to the world of Smart Life Engineering! In this book, we look into the fascinating domain of Smart technologies and their impact on our daily lives. From Smart homes to Smart cities, from advanced algorithms to ethical considerations, this book explores the multifaceted dimensions of Smart Life Engineering.

Our main motivation for writing this book on Smart Life Engineering is to identify and define the field of Smart Life, providing a clear scope of what constitutes “Smart” in the context of digital technologies. We aim to develop a cross-field perspective, provide insights on various disciplines within Smart Life, and offer illustrative examples of existing works in the field.

This book results from the scientific collaborations created during a serie of three workshops called “Information System Engineering for Smarter Life” (ISESL) collocated with the International Conference on Advanced Information Systems Engineering (CAiSE). This workshop, initiated in 2020, aimed at bringing together researchers and practitioners interested in the application of disruptive technologies to Smarter Life. During three editions in 2020, 2022, and 2023, the participants of this workshop worked on topics emerging from the application of the expanding digital technologies to different fields of life through the perspective of information systems. The last edition of this workshop was focused on the elaboration of the different chapters of the given book.

The book contains 13 chapters divided into 4 parts, including (I) Fundamentals of Smart Life and Smart Life Engineering, (II) Conceptual Contributions to Smart Life, (III) Smart Life Applications, and (IV) Experience Reports on Smart Life Applications.

## Fundamentals of Smart Life and Smart Life Engineering

The first part of this book focuses on the foundational aspects of Smart Life and Smart Life Engineering. This part begins with an exploration of the concept of Smart Life, examining its various dimensions and implications (Chap. 1). Then a
taxonomy of Smart applications (Chap. 2) and evolution of the scientific domain of Smart Life (Chap. 3) reveal its development and key areas of research. Additionally, a comprehensive review of social, behavioral, and ethical considerations for Smart Life Engineering, highlighting the importance of responsible and ethical innovation in this field, is conducted in Chap. 4.

## Conceptual Contributions to Smart Life

The second part of the book investigates conceptual contributions to Smart Life, exploring innovative ideas and approaches that shape the future of Smart technologies. The implications of Smart environments for environmental governance are examined in Chap. 5 with a discussion on how these technologies can contribute to sustainability efforts. Common misconceptions about Smart homes are addressed in Chap. 6. Chapter 7 explores the stakeholders involved in Smart city standardization, shedding light on the complex ecosystem of Smart urban development. In Chap. 8, the concept of Smart tourism and Smart tourism tools are discussed.

## Smart Life Applications

The third part focuses on reviewing three Smart Life applications, examining real-world implementations and their impact on various domains. The application of deep learning in Smart viticulture and recent advancements in yield prediction are explored in Chap. 9. Passengers' in-cabin behavior in Smart elevators is analyzed in Chap. 10. Finally, a wireless crowd detection technique for Smart overtourism mitigation is highlighted in Chap. 11.

## Experience Reports on Smart Life Applications

Finally, we look into real-world experience reports of Smart Life applications in this part. Chapter 12 focuses on the Smart city experience of Leuven. Another case concerns the case study of Monserrate, where immersive technologies and gaming are utilized to enhance the visitor experience (Chap. 13).

Through these themes and chapters, this book provides a comprehensive overview of Smart Life Engineering, from its foundational principles to its practical applications and theoretical advancements. Whether you are a researcher, practitioner, or enthusiast, we invite you to embark on this journey into the exciting world of Smart technologies and their impact on our lives.
# Acknowledgments

We would like to express our deepest gratitude to the participants of the ISESL workshops. It is thanks to their commitment, innovative ideas, and enthusiasm that this book became a reality. Their contributions were essential in enriching the discussions and guiding the reflections presented in these pages.

We also wish to thank the organizers of the three editions of the CAISE conference (2020, 2022, 2023) for making these workshops possible. Their logistical support was crucial in creating an environment beneficial to exchange and collaboration.

Finally, we extend our sincerest thanks to the authors and reviewers of this book. Twenty-eight authors, spread across 14 chapters, have generously shared their expertise and knowledge. The reviewers, with their rigor and insightful advice, have helped enhance the quality and coherence of the entire work. Without their hard work and dedication, this book would not have achieved the level of excellence we hoped for.

Thank you all for your invaluable contributions.

Paris, France

Paris, France

Utrecht, The Netherlands

Elena Kornyshova

Rébecca Deneckère

Sjaak Brinkkemper
# Contents

## Part I Fundamentals of Smart Life and Smart Life Engineering

1 Exploring Smart Life.................................................. 3
Elena Kornyshova, Rébecca Deneckère,
Kamal Mustapha Benramdane, and Sjaak Brinkkemper

2 Taxonomy of the Scientific Domain of Smart Life.................. 23
Rébecca Deneckère, Elena Kornyshova, Kaoutar Sadouki,
and Sjaak Brinkkemper

3 Evolution of the Scientific Domain of Smart Life.................. 57
Rébecca Deneckère, Elena Kornyshova, and Sjaak Brinkkemper

4 A Review of Social, Behavioral, and Ethical Considerations
for Smart Life Engineering............................................ 69
John Murray

## Part II Conceptual Contributions to Smart Life

5 Smart Environments: Implications for Environmental Governance... 91
Karen Bakker and Max Ritts

6 I'm Afraid HAL Can't Do That: Your Smart Home Is Not
That Kind of Existential Threat.................................... 113
Robin L. Zebrowski

7 Stakeholders in Smart City Standardization........................ 131
Kai Jakobs

8 Toward a Consensual Definition for Smart Tourism and
Smart Tourism Tools.................................................... 153
António Galvão, Fernando Brito e Abreu, and João Joanaz de Melo
## Part III Smart Life Applications

9 **Smart-Viticulture and Deep Learning: Challenges and Recent Developments on Yield Prediction.** 187
Lucas Mohimont, Lilian Hollard, and Luiz Angelo Steffenel

10 **How Do You Ride an Elevator? Passenger In-Cabin Behavior Analysis on a Smart-Elevator Platform.** 209
Tarmo Robal and Uljana Reinsalu

11 **Wireless Crowd Detection for Smart Overtourism Mitigation.** 237
Tomás Mestre dos Santos, Rui Neto Marinheiro, and Fernando Brito e Abreu

## Part IV Experience Reports of Smart Life Applications

12 **Leuven: A Smart City Experience.** 261
Lieve Heyrman, Tim Guily, Rébecca Deneckère, Elena Kornyshova, and Ramona Elali

13 **Enhancing the Visitor Experience with Immersive Technologies and Gaming: The Monserrate Use Case.** 273
Carlos Portugal, David Vaz, Miguel Sales Dias, Pedro Trocado, Alcina Prata, and Fernando Brito e Abreu

**Conclusion.** 293
Part I

# Fundamentals of Smart Life and Smart Life Engineering
# Chapter 1
## Exploring Smart Life

<figure><img src="image_5.png" /></figure>

Elena Kornyshova, Rébecca Deneckère, Kamal Mustapha Benramdane, and Sjaak Brinkkemper

**Abstract** In the contemporary landscape of technological advancement, the concept of Smart Life has emerged as a multifaceted domain, encapsulating a myriad of interconnected elements that deeply influence our daily experience. A lot of research works are published on this topic each month, and new devices continue to appear. However, after examining the existing literature, we found out the need for a consistent definition of Smart Life. This chapter addresses this limitation by providing a more precise definition of the Smart Life concept. We present various terms related to “Smart,” characterize in detail the concept of Smart Life, present a systemic view on this topic, and, finally, introduce the basic elements for Smart Life governance.

**Keywords** Smart Life · Smart artifact · Smart application · Smart domain · Governance

### 1.1 Introduction

The term *Smart* has its roots in Old English, dating back to the thirteenth to fourteenth century, originally connoting quick, active, and clever in relation to sharp humor or words (Online Etymology Dictionary 2024). Over time, it evolved to become synonymous with intelligent, quick, and fashionable (Oxford Dictionary

<footnote>E. Kornyshova (✉) · K. M. Benramdane
Centre d'Etudes et de Recherche en Informatique et Communications, Conservatoire National des Arts et Métiers, Paris, France
e-mail: elena.kornyshova@cnam.fr; mustapha-kamal.benramdane@lecnam.net</footnote>

<footnote>R. Deneckère
Centre de Recherche en Informatique, Université Paris 1 Panthéon-Sorbonne, Paris, France
e-mail: rebecca.deneckere@univ-paris1.fr</footnote>

<footnote>S. Brinkkemper
Department of Information and Computing Sciences, Utrecht University, Utrecht, the Netherlands
e-mail: s.brinkkemper@uu.nl</footnote>
2024). In the early 1970s, the term “Smart” took on a new dimension, referring to devices behaving as they were guided by intelligence. With the widespread adoption of Internet of Things (IoT), the concept of smartification arises in many domains (Schuh et al. 2019), where both devices and human activities incorporate computational and intelligent features. This is peculiar to a lot of areas, such as Smart cities, Smart energy, Smart homes, Smart manufacturing, etc.

Various Smart applications take more importance continuously. However, there is still a lack of understanding of what is Smart Life. Even the term “Smart” is to some extend ambiguous:

*   *Subjectivity of intelligence:* The term “Smart” can mean different things to different people. What one considers as Smart might not align with someone else’s perspective. This subjectivity can lead to debates and disagreements, particularly when applied to technologies and systems in different user circumstances.

*   *Ambiguity in technology:* In the context of technology, there might be ambiguity about what features or capabilities make a device or system truly “Smart.” This lack of clarity can lead to misunderstandings and misinterpretations.

*   *Rapid technological evolution:* As technology evolves rapidly, what was considered Smart yesterday might be outdated or commonplace today. This fast-paced evolution can make it challenging to establish a fixed definition.

*   *Overuse and marketing:* “Smart” has been widely used in marketing to promote various products and technologies, usually without a clear definition or standard. This overuse has diluted its meaning and contribute to skepticism or confusion about what truly qualifies as “Smart.”

In summary, the term “Smart” is controversial due to its subjective nature, ambiguity in technology, the rapid evolution of technology, and its overuse in marketing. It is therefore essential to lead scientific debates around “Smart” to establish a set of clear definitions, which will serve for a uniform understanding.

In our previous works (Kornyshova et al. 2022a, b), we started to develop and formalize the concept of Smart Life. This chapter gives a more detailed vision by detailing the layers of Smart Life and its core components—Smart artifacts, Smart applications, Smart domains, Smart Life Engineering, and other related concepts—and unveiling the systemic nature that underpins this paradigm.

Section 1.2 overviews the concept “Smart” and other similar terms. Our detailed vision of the concept “Smart” is explained in Sect. 1.3. In Sect. 1.4, we present our systemic vision of Smart Life. We discuss the aspects related to Smart Life governance in Sect. 1.5. We conclude this chapter in Sect. 1.6.

## 1.2 Concept of “Smart”

The idea of Smart Life is often associated with the Internet of Things in the existing literature, especially within various Smart devices. IoT has brought significant changes in human life by enabling the exchange of information in various appli-
cations such as Smart cities, Smart homes, Smart health, Smart transport, etc. In Lee et al. (2021), the authors characterized Smart Life applications based on IoT as a new lifestyle. Smart Life connects all IoT microdevices and microsensors through wireless communication grids, as described in Cho et al. (2018). This concept of connecting all embedded devices to the Internet has the potential to change our lives and the world, making it possible for us to live a Smart Life in a Smart world, as stated in Mizintseva (2021). IoT is an essential technology to enhance the quality of human life (Ho et al. 2015; Sharma and Tayal 2019). IoT objects and devices collect user data, and their final goal is an intelligent use of these data to provide meaningful support to the user (Fu 2016). These intelligent devices are leading the way to Smart Life, pointing toward a fresh and innovative direction for the Internet (Gonzales Garcia et al. 2017).

However, Smart Life is not restricted to the concept of IoT. The main idea behind Smart Life is using and processing data to improve the conditions of our lives. Any artifact that processes data in an autonomous way can be considered as “Smart,” independently of its connectivity status. In addition, the understanding of the term “Smart” varies not only between sub-fields but also within specific fields. For example, Liu and Baiocchi (2016) and Thagard (1993) present different perspectives on the concept of Smart objects, but a unanimous agreement on the definition of a Smart object is absent.

Many terms exist that are closely related to this concept. Most notably, in scientific literature, this is the case for “intelligent,” “autonomous,” “stand-alone,” and “sentient.”

**Intelligent** Merriam-Webster (2024a) gives several definitions of the term “intelligent.” The definition, the most adapted to our context, is “something guided or controlled by a computer.” Morris and Langari (2016) propose to define the term “intelligent device” as “a package containing either a complete measurement system, or else a component within a measurement system, which incorporates a digital processor.”

**Autonomous** Merriam-Webster (2024b) defines autonomous as “(a) having the right or power of self-government (b) is undertaken or carried on without outside control (c) existing or capable of existing independently (d) responding, reacting, or developing independently of the whole.” An example of Smart systems that are defined as autonomous represents the autonomous vehicles. Faisal et al. (2019) propose a state of the art on autonomous vehicles, aka automated car, self-driving car, or driverless car (Spyropoulou et al. 2008; Chong et al. 2013; Olaverri-Monreal 2016).

**Stand-alone** Merriam-Webster (2024c) defines stand-alone as “intended, designed, or able to be used or to function alone or separately : not connected to or requiring connection to something else in order to be used or to function.” Nawandar et al. (2021) describe a stand-alone device as “having numerous capabilities: (i) interaction with user, (ii) required application selection, (iii) data sensing, (iv) data publish, and (v) decision making and actuation.”
**Sentient** Merriam-Webster (2024d) gives a definition of “sentient” as being “capable of sensing or feeling: conscious of or responsive to the sensations of seeing, hearing, feeling, tasting, or smelling.” Applied to the context of artificial intelligence (AI), A. Husain (2017) defines the term of “sentient machine.” He promotes sentience as the path from artificial narrow intelligence (e.g., drones) to artificial generalized intelligence (achievement of machine consciousness). In Kornyshova and Gressier-Soudan (2021), this term is used to introduce sentient requirements for ICT. These requirements aim to improve and protect the well-being of ICT users and to contribute to the maintenance of the human sovereignty with regard to digital technologies.

All these definitions are tightly linked and represent nearly the same ideas. Our definition of Smart also introduces some different degrees, called “smartness degrees,” detailed in the following section.

## 1.3 Characterization of Smart Life

To gain a deeper understanding of the Smart Life domain, we established a two-dimensional framework that we explain through the notion of an artifact. The first dimension encompasses the different levels of smartness, and the second dimension considers the connectivity of devices.

We identify three degrees of smartness: *Smart*, *intelligent*, and *sentient*.

* *Degree 1 of Smartness—Smart:* is the ability of an artifact to process data and adapt its functioning accordingly. The device should be able to trigger further action depending on the context characteristics.

* *Degree 2 of Smartness—Intelligent:* is the ability of an artifact to process data and adapt its functioning using artificial intelligence. At this degree, AI algorithms are involved in deciding about the next action.

* *Degree 3 of Smartness—Sentient:* is the ability of an artifact to process data derived from users, about their feelings, emotions, physical state, etc., and adapt its functioning to enhance the well-being of users.

These three smartness degrees are orthogonal to the three different degrees of connectivity used.

* *Degree 1 of Connectivity—Stand-alone:* an artifact is working on its own, without the need to be connected to anything else.

* *Degree 2 of Connectivity—Connected:* an artifact is working on its own but is connected to the Internet to get and share data with other artifacts or applications.

* *Degree 3 of Connectivity—Federated:* an artifact is working on its own but also together with other artifacts or applications for a common goal.
<figure><img src="image_6.png" /><figcaption>**Fig. 1.1** Examples of the characterization of Smart Life</figcaption></figure>

The orthogonality of smartness and connectivity implies that we can characterize 3 × 3 = 9 types of Smart Life. This characterization with illustrative examples is shown in Fig. 1.1.

**Smart-Stand-alone** An artifact that is able to adapt its behavior without external data input and/or action. For instance, a Smart washing machine selects the program depending on the laundry weight, color, etc.

**Smart-Connected** A connected to a network artifact that can adapt its functioning. An example is the Smart refrigerator that can order grocery foods when it detects lack of certain goods.

**Smart-Federated** A set of connected Smart artifacts, working together for the same goal and defining together the next action. A Smart terrarium is a good example. It is composed of several sensors (temperature, humidity, etc.), and all the collected data helps define the terrarium parameters to offer optimal conditions to animals.

**Intelligent-Stand-alone** A Smart artifact that uses artificial intelligence tools to modify its behavior following the context. For instance, a Smart air conditioner can adapt the temperature of the room depending on the people inside and the
environment context (weather, outside temperature, etc.) using machine learning algorithms.

**Intelligent-Connected** An intelligent artifact connected to a network and adapting its functioning depending on the environment, for instance, a Smart home energy device connected to the distributed grid storing electricity, optimizing the consumption of the residents.

**Intelligent-Federated** A set of intelligent artifacts working together as a whole for a common goal. An autonomous greenhouse is a good example, as it includes a set of control devices (humidity, temperature, etc.), and it uses machine learning algorithms to interpret all the collected data to trigger automatic harvesting when plants are identified as mature enough.

**Sentient-Stand-alone** An independent Smart artifact dealing with well-being-oriented data of users, for example, a robot assisting elderly people, interpreting their needs and reacting accordingly.

**Sentient-Connected** A sentient device connected to a network, for instance, a Smart wearable device analyzing sleep quality coupled with a coaching application interpreting the gathered data to improve personal training.

**Sentient-Federated** A set of sentient artifacts able to work together and adapt themselves to provide well-being of users. We can cite a Smart home care application including wellness, safety, and emergency monitoring as an example.

## 1.4 Systemic Vision of Smart Life

We study Smart Life using the systemic approach (Nikolaev and Fortin 2020). At the heart of the Smart Life domain is a fundamental component that we call *Smart artifact*—a term deliberately chosen to avoid confusion with established terms like Smart object or Smart thing. As detailed above, Smart artifacts correspond to independent devices including the ability to capture, store, and process data to predict the next action.

Smart artifacts with comparable functional purposes within a certain scope constitute a *Smart application*. Smart applications can be identified at various levels of granularity, ranging from specific and detailed instances (e.g., a Smart lighting system incorporating multiple sensors for home and occupant monitoring) to broader applications (like a Smart home system encompassing diverse functions such as lighting, heating, and window management). Smart artifacts may be part of several Smart applications; for instance, Smart lighting systems could be present in Smart homes, Smart factories, or Smart cities. Each Smart application functions as a system composed of Smart artifacts, implying that it should have at least one emergent property. Emergent properties are features inherent to the entire
<figure><img src="image_7.png" /><figcaption>**Fig. 1.2** System-oriented vision of Smart Life</figcaption></figure>

system that individual elements lack. For example, all components of a car must be assembled in a specific way for the car to function.

Similarly, *Smart Life* is composed of various Smart applications, making it a system of systems. At its most basic level, Smart Life is a combination of at least two Smart applications, necessitating coordination based on the input and analysis of emergent properties. This system-oriented perspective of Smart Life is shown in Fig. 1.2. The corresponding concepts are detailed in this section, but, before that, we present a scenario to illustrate the systemic vision of Smart Life. This scenario describes the case of a citizen living in a Smart environment.

**Illustrative Scenario: “A Citizen Living in a Smart Environment”**

Diana, a modern urban dweller, begins her day in her Smart apartment. As she finishes the last drop of fresh milk, her Smart refrigerator detects the need for replenishment and automatically places an order at the local grocery store for a delivery within the hour.

While waiting for the delivery, Diana gets ready for the day. She checks her health app on her connected Smartphone to review her vital signs and adjust her dietary plan. The Smart thermostat automatically adjusts the temperature in her apartment according to her preferences.

Once ready, Diana heads to the parking lot of her building where she finds her electric car. Using her Smartphone, she unlocks the vehicle and starts the engine. The integrated navigation system guides her to the hospital for her medical checkup. During the journey, the car communicates with the network of Smart traffic lights to optimize her route.

At the hospital, Diana picks up her new insulin pen connected to an app on her Smartphone. This Smart device analyzes her blood composition in real time and
administers the appropriate dose of insulin automatically, providing better control of her blood sugar levels.

Next, Diana heads to her Smart factory workplace. She plugs her electric car into the factory parking, ensuring optimal charging. At work, she uses various Smart devices to monitor and control production processes. Connected sensors and cameras provide real-time information on the status of machines and production lines.

At the end of the afternoon, Diana reunites with her dog Bob, equipped with a Smart collar. They set off for a bike ride using her Smart bike. During the ride, Bob's collar transmits data on his physical activity and health status to Diana's app, ensuring the well-being of her loyal companion.

As the day draws to a close, Diana returns to her Smart apartment with fresh groceries. The Smart kitchen appliances recognize the new items and suggest recipes based on the available ingredients. Diana selects a recipe using her Smart kitchen assistant, and the appliances guide her through the cooking process, ensuring a delicious and healthy dinner.

After dinner, Diana engages in some leisure time. Her Smart TV recommends personalized shows based on her preferences, and she uses voice commands to control the entertainment system. Meanwhile, the Smart lighting system adjusts the ambiance to create a relaxing atmosphere.

Before bedtime is a special time for Diana, who, as an avid bird enthusiast, installed a connected bird feeder on her balcony. She enjoys scrolling through photos taken by the feeder's camera, which automatically uploads images of visiting birds to the Internet but also to a real-time urban bird-tracking association.

Before heading to bed, Diana checks her sleep tracker app, which is synced with her Smartwatch. The app provides insights into her sleep patterns and suggests adjustments for better sleep quality. The Smart thermostat ensures the bedroom temperature is optimal for a restful night.

As Diana settles into bed, she instructs her Smart home assistant to enter "night mode," which dims the lights, locks the doors, and adjusts other settings to enhance security and conserve energy overnight. Throughout the night, various Smart devices continue to monitor and optimize her home environment, contributing to a seamless and technology-enhanced daily routine.

This illustrative day shows how Diana interacts with a multitude of Smart devices, simplifying her daily life and enhancing her management of health, mobility, and domestic affairs.

### 1.4.1 Smart Artifact

A *Smart artifact* is defined as any autonomous and stand-alone artifact equipped with a data processor, connected or not, that obtains, stores, processes, and utilizes
data to initiate actions within its environment. Examples of Smart artifacts can be a
Smart watch, Smart window, Smart toy, Smart antenna, and so on.

Referring to our ongoing example, Diana is using a lot of Smart artifacts:

- A Smart insulin pen to regulate her blood sugar levels. This Smart device operates autonomously, without requiring any user input. Once the blood sugar level falls below the set limit, the pen delivers precisely what is needed for Diana.

- A Smart fridge. It features internal cameras for automated inventory management and temperature control settings that can be adjusted remotely. The refrigerator is intelligent enough to automatically place grocery orders when items are running low.

- A Smart TV. It is a television with integrated Internet connectivity and interactive features. It allows users to access online content, streaming services, apps, and other interactive features directly through the television, often eliminating the need for external devices like streaming boxes or game consoles.

1.4.2 Smart Application

A *Smart application* is defined as a network of interconnected Smart artifacts,
potentially including other Smart applications working together toward a shared
goal. For instance, a Smart city application may consist of individual components
such as a Smart parking application, a Smart energy application, and so on.
Similarly, a Smart home can be composed of a Smart TV, a Smart fridge, a Smart
assistant, and more. It's a requirement for a Smart application to encompass a
minimum of two Smart artifacts.

Diana uses also several Smart applications.

- Smart home. She is living in a Smart apartment. It refers to a residence equipped with various devices and systems that are connected to the Internet, allowing for remote monitoring and control. These interconnected devices enhance the automation, efficiency, and convenience of daily tasks. Examples of Smart devices within a Smart home include Smart fridges, Smart thermostats, Smart security systems, Smart home assistants, Smart TV, and so on. The goal of a Smart home is to enhance comfort, energy efficiency, and security while providing users with greater control over their living environment.

- Smart factory. She is working in a Smart factory. Within a Smart factory, various Smart devices and systems work together to optimize production processes. Examples of Smart applications within a Smart factory include predictive maintenance systems, Smart energy management systems, Smart parking, and so on. The aim of a Smart factory is to increase productivity, reduce costs, and improve flexibility in responding to market demands. By leveraging interconnected technologies, Smart factories transform traditional manufacturing processes into more agile, efficient, and data-driven operations.
<figure><img src="image_8.png" /><figcaption>**Fig. 1.3** Domains of Smart applications (Cliparts used in this figure were designed by Freepik)</figcaption></figure>

- *Smart hospital.* She regularly goes to a Smart hospital. It uses interconnected systems to enhance patient care. Within a Smart hospital, various Smart devices and systems work together to create a connected and intelligent healthcare environment. Examples of Smart things within a Smart hospital include Smart patient rooms, Smart medication management, Smart wearable health technologies, and so on. The goal of a Smart hospital is to improve patient outcomes, enhance the patient experience, and streamline healthcare operations through the integration of cutting-edge technologies.

**1.4.3 Smart Domain**

Smart applications are grouped into four domains (cf. Fig. 1.3), which are explained in detail in Chap. 2.

**Smart Applications for Persons** This domain unifies various applications that concern people's personal life, such as Smart home, Smart healthcare, Smart welfare, etc.

*Diana lives in a Smart home. In addition, she deals with several other applications: Diana uses a Smart home temperature management system (a Smart home application), a Smart drug delivery system (her insulin pen, from a Smart healthcare application), and a Smart training application for her dog Bob (Smart welfare of animals).*

**Smart Applications for Society** We put in this domain all the Smart applications that are related to the society itself, like Smart city, Smart education, Smart social media, etc.
<figure><img src="image_9.png" /><figcaption>**Fig. 1.4** Conceptual model of Smart Life with links between Smart concepts</figcaption></figure>

Diana uses a Smart parking application in the building where she lives to charge her electric car.

**Smart Applications for Environment** All applications dealing with the natural environment are grouped into this domain, like Smart natural resource management, Smart disaster management systems, Smart earth, etc.

Diana uses a Smart bird feeder to help track urban birds for an ornithological association.

**Smart Applications for Enterprises** This domain groups all Smart applications useful for companies, like Smart industry applications, Smart transportation, Smart agriculture, etc.

Diana works in a Smart factory (a Smart industry application) and uses a Smart driving tool in her car (a Smart transportation application).

### 1.4.4 Smart Life

Smart Life is defined as the overall domain comprising all Smart domains, Smart applications, and Smart artifacts (as shown in the Fig. 1.4), aimed at enhancing experiences from personal, societal, environmental, and enterprise perspectives (Kornyshova et al. 2022b). It can be viewed as the societal domain that consolidates all initiatives involving the application of Smart artifacts and applications in diverse settings. Additionally, we also propose to use the term Smart Life as a comprehensive scientific research domain that encompasses all research areas exploring Smart technology, artifacts, and applications. Under this umbrella term, representatives from academia, society, and industry can convene and plan activities to promote all forms of Smart concepts.

The conceptual model of Smart Life shows a set of links between the three main elements composing Smart life: Smart domain, Smart application, and Smart artifact. We use our running example to illustrate these links.

- A Smart domain groups Smart applications and Smart artifacts within the same category.
Diana uses several Smart applications for people domain: a Smart home application, a Smart welfare application (for her dog and its training system), etc.

- Smart applications are composed of Smart artifacts.

Diana's Smart home groups Smart artifacts, like her Smart refrigerator and her Smart assistant.

- Smart applications can be composed of other applications.

Diana's Smart home groups other Smart applications, for instance, a Smart temperature management system.

- Smart artifacts can be composed of Smart artifacts.

Diana's Smart bird feeder is a Smart artifact that is composed of several other artifacts: a weight sensor (to detect when a bird lands in front of the bird feeder), a camera (triggered by the weight sensor), etc.

### 1.4.5 Actors in Smart Life

We use the term actor for any kind of user, group, or organization that is involved in the usage and functioning of a Smart Life experience. We can observe that the actor is at the core of the Smart Life concept, itself linked to all the concepts described above, as shown in Fig. 1.5.

<figure><img src="image_10.png" /><figcaption>**Fig. 1.5** Central role of actors</figcaption></figure>
Actors can have various roles:

- **Consumer/user.** A consumer is a person who buys goods or services for their own use; in the same way, a user is someone who uses a product, machine, or service (Online Cambridge dictionary 2024b).

Diana uses her Smart assistant to trigger some answers from her Smart home (for instance, dimming the lights at bedtime).

- **Supplier.** A supplier is a company, or a person, that provides things that people want or need (Online Cambridge Dictionary 2024a).

All the artifacts used by Diana keep, manage, and interpret a lot of her data. To give some examples, her Smart refrigerator analyzes the presence of food to make orders to the grocery store; her driving assistant analyzes her GPS data to guide her in her way; her insulin pen manages her blood sugar level to trigger shoots of insulin; and so on. She can be considered as the supplier of all these data.

- **Designer.** A designer is a person who imagines how something could be made and draws plans for it (Online Cambridge dictionary 2024c).

Diana had the opportunity to choose between several designs for some of her Smart artifacts. For instance, she chose the color, size, and functionalities of her Smart fridge.

- **Deployment participant.** A deployment can be defined as the movement of equipment to a place where they can be used when they are needed (Online Cambridge dictionary 2024d). A deployment participant is then a person who participates to this movement by putting a specific equipment at a certain place where it can be used effectively.

Diana's bird feeder is deployed at her home, and the results are automatically sent to an ornithologist association for urban bird tracking.

- **Sponsor.** A sponsor can be defined as a person, or company, that supports a person, organization, or activity by giving money (Online Cambridge dictionary 2024e).

Diana is sponsored by an urban bird-tracking association to deploy her bird feeder at her home. She paid only half of the device's price, allowing her to directly benefit from all the bird pictures and observe the birds feeding on her balcony, while the association covered the remaining cost.

### 1.4.6 Smart Life Engineering

Smart Life Engineering involves the application of scientific principles on the design, construction, implementation, and evolution of Smart Life applications. This is a concept that needs for a lot of elaboration on it. We highlight the importance of the progress in this field. While Smart Life Engineering shares common ground with information systems engineering, it also establishes extensive connections with
research domains such as wireless network technology, artificial intelligence, urban
geography, logistics, medical technology, and more.

**1.5 Smart Life Governance**

In the context of Smart Life, the management of artifacts is done at the level of applications (Smart home, Smart city, etc.). Smart applications, especially in the domain of Smart applications for society, imply interactions between numerous actors. In this case, it is important to define how different actors (users, designers, sponsors, etc.) participate and contribute to the whole functioning.

To understand better the various roles and interactions, we consider Smart applications as ecosystems. In this section, we introduce an organizational model, function distribution within Smart application ecosystems, and a governance matrix.

**1.5.1 Organizational Model of Smart Application Ecosystem**

We define a high-level organizational model for a Smart application ecosystem, detailing the different functions and requirements. The organizational model of Smart application ecosystems consists of three main components: Smart application, Smart artifacts, and actors (see Fig. 1.6).

A *Smart application* can be viewed as an ecosystem; thus, it is a network of interconnected actors and eventually other Smart applications through Smart artifacts. A Smart application can be a system of other Smart applications. For instance, Smart home application can be composed of Smart lightning application and a Smart energy heating application.

<figure><img src="image_11.png" /><figcaption>**Fig. 1.6** Organization model of a Smart application ecosystem</figcaption></figure>
*Smart artifacts* can represent various tools, such as sensors, detectors, processors, measuring tools, Smartwatches, etc. Smart artifacts, with different functions, are used by the actors to achieve their purposes. They allow applications to interact with actors. They are connected to a main Smart application that defines the collaboration rules.

*Actors* can have different roles including designer, developer, sponsor, or user and may assume multiple roles also simultaneously, such as consumer and supplier. Actors have goals contributing to the overall Smart application objectives. Data generation and sharing by these actors are regulated in the basis of a set of rules defined by a given actor. These actors engage in both competition and cooperation within a shared, multi-layered space encompassing products and services, processes, data, and other resources as depicted in Fig. 1.6.

### 1.5.2 Function Distribution in Smart Application Ecosystems

In each Smart application ecosystem, functions could be viewed at three levels: for the actors, applications, and artifacts.

**Actor Functions** An actor can take on several roles, and these roles give it different functions depending on its degree of involvement in the ecosystem and the level of detailed data they intend to share. Each actor occupies a portion of the multi-layered space, according to their involvement within the Smart application ecosystem. We define the following functions for actors:

* Actors define the goals of Smart applications.

* Actors have goals and contribute to the goals achievement of Smart applications.

* Actors define and set rules within the Smart application.

* Actors follow the rules established within Smart applications.

* Actors perform and prioritize their activities depending on their goals.

* Actors manage, manipulate, and share their data.

* Actors participate in both producing and consuming products and services.

**Smart Application Functions** In order to function correctly, within the framework of established rules, Smart applications must enable the following functions:

* Smart applications align the goals of the corresponding ecosystem to the goals of the involved actors.

* Smart applications broadcast the defined rules throughout the network.

* Smart applications check whether the actors adhere to the established rules.

* Smart applications handle the synchronization and integration of processes.

* Smart applications handle the synchronization and integration of data.

* Smart applications ensure the availability and compliance of products and services for all actors.
**Smart Artifact Functions** Smart artifacts are mandatory present in any Smart application. Their main contribution is that they support the whole operational functioning of Smart applications. We will not talk here about technical aspects. With regard to the functions of Smart artifacts in Smart applications, we identified the following:

* Smart artifacts support the functioning of Smart applications for the goals of the actors and are designed to satisfy this criterion.

* Smart artifacts are designed to respect various rules (security and privacy related, and so on).

* Smart artifacts participate in processes by supporting actors' activities.

* Smart artifacts collect, store, process, and report data.

* Smart artifacts constitute products and support services of Smart applications.

### 1.5.3 Governance Matrix

To provide additional insights for characterizing the relationships between actors, Smart applications, and Smart artifacts, we grouped the identified functions in a governance matrix (illustrated in Fig. 1.7, cf. the comment below). We grouped

<table><caption>**Fig. 1.7** Governance matrix for Smart application ecosystems</caption><thead><tr><th></th><th>Actor</th><th>Smart Application</th><th>Smart Artefact</th></tr></thead><tbody><tr><th>Goals</th><td>- Actors define the goals of Smart applications.<br>- Actors have goals and contribute to the goals achievement of Smart applications.</td><td>Smart applications align the goals of the corresponding ecosystem to the goals of the involved actors.</td><td>Smart artefacts support the functioning of Smart applications for the goals of the actors and are designed to satisfy this criterion.</td></tr><tr><th>Rules</th><td>- Actors define and set rules within the Smart application.<br>- Actors follow the rules established within Smart applications.</td><td>- Smart applications broadcast the defined rules throughout the network.<br>- Smart applications check whether the actors adhere to the established rules.</td><td>Smart artefacts are designed to respect various rules (security-, privacy-related, and so on).</td></tr><tr><th>Processes</th><td>Actors perform and prioritize their activities depending on their goals.</td><td>Smart applications handle the synchronization and integration of processes.</td><td>Smart artefacts participate in processes by supporting actors' activities.</td></tr><tr><th>Data</th><td>Actors manage, manipulate, and share their data.</td><td>Smart applications handle the synchronization and integration of data.</td><td>Smart artefacts collect, store, process, and report data.</td></tr><tr><th>Product and Services</th><td>Actors participate in both producing and consuming products and services.</td><td>Smart applications ensure the availability and compliance of products and services for all actors.</td><td>Smart artefacts constitute products and support services of Smart applications.</td></tr></tbody></table>
the functions depending on the main elements related to the organizational model: goals, rules, processes, data, and products and services.

An actor can have multiple roles and be involved in different ways in a Smart application ecosystem. Since actors interact with each other, the interactions between customer and supplier within a Smart application are recursive, with each participant potentially assuming both roles according to use case. Actors create, share, and consume resources while adhering to established rules and striving to achieve the goals of Smart applications.

Smart application ecosystems represent an intricate topic that should be studied in more detail. We tried to give some preliminary ideas to develop how its governance should be done. From these functions, there are several important requirements that a Smart application ecosystem must meet:

* Goals should be analyzed at two levels, actors and Smart applications, and both should be aligned to each other.

* All relevant actors should communicate data management rules, including compliance with the Smart application ecosystem rules.

* Each actor should focus on his/her activities depending on goals.

* Smart application ecosystems should support the integration and synchronization of processes among actors.

* Data should be managed and manipulated by their respective owners.

* Smart application ecosystems should allow the integration and synchronization of data among actors.

* Smart applications should consider the new requirements of personas representing actors.

## 1.6 Conclusion

In this chapter, we presented a detailed vision of the concept “Smart” and its application to various domains existing in the current landscape of digital technologies. We improved the notion of Smart Life as an umbrella concept unifying the whole spectrum of initiatives of Smart artifacts and Smart applications. We state that such a vision is important to provide more sustainable, green, human-aware, and efficient development of digital technologies.

## References

* Cho, Y., Lee, S., Hong, J., Pak, S., Hou, B., Lee, Y.W., Jang, J.E., Im, H., Sohn, J.I., Cha, S., Kim, J.M.: Sustainable hybrid energy harvester based on air stable quantum dot solar cells and triboelectric nanogenerator. J. Mater. Chem. (2018)

* Chong, Z.J., Qin, B., Bandyopadhyay, T., Wongpiromsarn, T., Rebsamen, P., Ang Jr., M.H.: Autonomy for mobility on demand. Intell. Auton. Syst. 12, 671–682 (2013)
Faisal, A., Kamruzzaman, M., Yigitcanlar, T., Currie, G.: Understanding autonomous vehicles: a systematic literature review on capability, impact, planning and policy. J. Transp. Land Use. **12**(1), 45–72 (2019)

Fu, J.: Intelligent hardware somatosensory design. In: 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control, pp. 331–334. (2016)

González García, C., Meana Llorián, D., Pelayo García-Bustelo, B.C., Cueva Lovelle, J.V.: A review about Smart Objects, Sensors, and Actuators. Int. J. Interactive Multimedia Artif. Intell. **4**(3) (2017)

Ho, Y., Sato-Shimokawara, E., Wada, K., Yamaguchi, T., Tagawa, N.: Developing a life rhythm related human support system. In: IEEE International Symposium on Industrial Electronics, pp. 894–899. (2015)

Husain, A.: The sentient machine: the coming age of artificial intelligence. Scribner (2017)

Kornyshova, E., Gressier-Soudan, E.: Introducing sentient requirements for information systems and digital technologies. In: European, Mediterranean, and Middle Eastern Conference on Information Systems, Dubai, United Arab Emirates (2021)

Kornyshova, E., Deneckere, R., Gressier-Soudan, E., Murray, J., Brinkkemper, S.: From Smart Life to Smart Life engineering: a systematic mapping study and research agenda. In: 15th International Baltic Conference on Digital Business and Intelligent Systems. Springer, Riga, Latvia (2022a)

Kornyshova, E., Deneckere, R., Sadouki, K., Gressier-Soudan, E., Brinkkemper, S.: Smart Life: review of the contemporary smart applications. In: International Conference on Research Challenges in Information Science, pp. 302–318. (2022b)

Lee, S.H., Lee, J., Jung, J., Cho, A.R., Jeong, J.R., Dang Van, C., Nah, J., Lee, M.H.: Enhanced electrochemical performance of micro-supercapacitors via laser-scribed cobalt/reduced graphene oxide hybrids. ACS Appl. Mater. Interfaces. **13** (2021)

Liu, X., Baiocchi, O.: A comparison of the definitions for smart sensors, smart objects and Things in IoT. In: IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference, pp. 1–4. (2016)

Merriam-Webster. Intelligent. In: Merriam-Webster dictionary. https://www.merriam-webster.com/dictionary/intelligent (2024a)

Merriam-Webster. Autonomous. In: Merriam-Webster dictionary. https://www.merriam-webster.com/dictionary/autonomous (2024b)

Merriam-Webster. Stand-alone. In: Merriam-Webster dictionary. https://www.merriam-webster.com/dictionary/stand-alone (2024c)

Merriam-Webster. Sentient. In: Merriam-Webster dictionary. https://www.merriam-webster.com/dictionary/sentient (2024d)

Mizintseva, M.F.: Smart Technologies for Smart Life. “Smart Technologies” for Society, State and Economy. **155**, 653–664 (2021)

Morris, A.S., Langari, R.: Intelligent devices. Measurement Instrum., 289–314 (2016)

Nawandar, N.K., Cheggoju, N., Satpute, V.R.: Stand-alone device for IoT applications. arXiv preprint arXiv:2110.15405. (2021)

Nikolaev, M.Y., Fortin, C.: Systems thinking ontology of emergent properties for complex engineering systems. J. Phys. Conf. Ser. **1687**, 012005 (2020)

Olaverri-Monreal, C.: Autonomous vehicles and smart mobility related technologies. Infocommun. **J. 8**, 17–24 (2016)

Online Cambridge dictionary. Supplier. In: Online Cambridge dictionary. https://dictionary.cambridge.org/dictionary/english-french/supplier (2024a)

Online Cambridge dictionary. Supplier. In: Online Cambridge dictionary. https://dictionary.cambridge.org/dictionary/english-french/consumer (2024b)

Online Cambridge dictionary. Designer. In: Online Cambridge dictionary. https://dictionary.cambridge.org/dictionary/english-french/designer (2024c)

Online Cambridge dictionary. Deployment. In: Online Cambridge dictionary. https://dictionary.cambridge.org/dictionary/english/deployment (2024d)
Online Cambridge dictionary. Sponsor. In: Online Cambridge dictionary. https://dictionary.cambridge.org/dictionary/english/sponsor (2024e)

Online Etymology Dictionary. Smart. In: Online Etymology Dictionary. https://www.etymonline.com/search?q=smart (2024)

Oxford Dictionary. Smart. In: Online Oxford Dictionary. https://www.oxfordlearnersdictionaries.com/definition/english/smart_1?q=smart (2024)

Schuh, G., Zeller, V., Hicking, J., Bernardy, A.: Introducing a methodology for smartification of products in manufacturing industry. Procedia CIRP. **81**, 228–233 (2019)

Sharma, K., Tayal, S.: Indian smart city ranking model using taxicab distance-based approach. Energy Syst. (2019)

Spyropoulou, I., Penttinen, M., Karlaftis, M., Vaa, T., Golias, J.: ITS solutions and accident risks: prospective and limitations. Transp. Rev. **28**, 549–572 (2008)

Thagard, P.: Computational philosophy of science. MIT Press (1993)

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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<figure><img src="image_12.png" /></figure>

Chapter 2
# Taxonomy of the Scientific Domain of Smart Life

Rébecca Deneckère, Elena Kornyshova, Kaoutar Sadouki, and Sjaak Brinkkemper

**Abstract** The field of Smart Life represents a large spectrum of various application domains coming from very established ones as Smart energy and Smart home to very recent ones as Smart environment, Smart airport, or Smart Earth. We observed a substantial growth in the scientific literature with over 126,000 papers containing “Smart” in their titles in 2021. Despite this huge number, we did not identify a detailed classification of these fields in the existing literature. There is still a notable gap in the classification and systematization of these fields. To address this, we developed a generic taxonomy for Smart applications by conducting a systematic mapping study focused on state-of-the-art and research agenda-oriented papers (2341 scientific publications in total).

**Keywords** Smart Life · Smart domain · Taxonomy

## 2.1 Introduction

There is a huge number of scientific publications in the various Smart domains. In 2021, more than 126,000 papers containing the term “Smart” in their titles were extracted from the scientific databases. In front of this quantity of scientific works, we decided to reduce our scope to state-of-the-art- and research agenda-oriented

<footnote>R. Deneckère
Centre de Recherche en Informatique, Université Paris 1 Panthéon-Sorbonne, Paris, France
e-mail: rebecca.deneckere@univ-paris1.fr</footnote>

<footnote>E. Kornyshova (✉) · K. Sadouki
Centre d'Etudes et de Recherche en Informatique et Communications, Conservatoire National des Arts et Métiers, Paris, France
e-mail: elena.kornyshova@cnam.fr; kaoutar.sadouki@lecnam.net</footnote>

<footnote>S. Brinkkemper
Department of Information and Computing Sciences, Utrecht University, Utrecht, the Netherlands
e-mail: s.brinkkemper@uu.nl</footnote>
papers. The motivation behind was to target more established sub-fields of Smart Life as the presence of this kind of publication can testify.

We discovered that the number of scientific literature reviews in Smart Life-related topics is also important and growing continuously. For instance, for the period from 1986 to the beginning of 2022, we identified 891 literature reviews on Smart industries, 384 on Smart cities, 191 on Smart homes, and 190 on Smart healthcare, to cite the most widespread ones.

Most of these state-of-the-art works deal with a particular sub-topic in the Smart domain: IoT application for energy consumption (Wang et al. 2021), security in Internet of Things (IoT) (Harbi et al. 2019), IoT usage in Smart cities with a classification of Smart technologies (Tai-hoon et al. 2017), computer technologies from the viewpoint of artificial intelligence (Yamane 2017), machine-to-machine usage (Severi et al. 2014), and so on. Despite the explosion of literature reviews in Smart domains, we noted an absence of research dedicated to the classification, organization, and systematization of these fields.

We undertook a systematic mapping study (Petersen et al. 2008) to explore and structure the scientific domain of Smart Life. Our main goals were to elaborate a taxonomy of Smart Life applications, which does not exist yet in the current literature and to understand the evolution of these applications and related concepts.

To elaborate on these goals, we established two research questions:

* RQ 1: *How can Smart applications be classified?*

* RQ 2: *How have these applications evolved through time?*

The objective of this chapter is to identify the existing Smart applications and taxonomize the scientific domain of Smart Life. We elaborated a generic taxonomy of applications around Smart Life to answer the first question. The second one is detailed in Chap. 3 of this book. Our results were partially presented in Kornyshova et al. (2022a, b).

In this chapter, we explain our research method in Sect. 2.2. We highlight the obtained results in Sect. 2.3 and each of the Smart domains in Sects. 2.4–2.7. Clusters are explained in Sect. 2.8. We conclude in Sect. 2.9.

## 2.2 Research Methodology

We adopted a systematic mapping design (Petersen et al. 2008) to explore the Smart Life field of research. Systematic mapping studies (SMS) share similarities with other systematic reviews (like SLR—systematic literature review—synthesizing the existing research in established fields), except in their broader inclusion criteria to select a wider range of research papers. SMS are intended to map out topics within a field classification rather than synthesize study results. Our study is focused on consolidating existing work in the Smart Life domain, following the process outlined in Petersen et al. (2008), which includes five steps: definition of research
questions, finding papers, screening papers, classification scheme definition, and
data extraction.

**2.2.1 Step 1: Definition of the Research Questions**

The objective of this chapter is to identify existing Smart applications and taxono-
mize the scientific domain of Smart Life. Thus, we focus on the first question (the
second question is answered in Chap. 3):

* *RQ 1: How can Smart applications be classified?*

**2.2.2 Step 2: Finding Papers**

This step focuses on identifying a set of papers based on a relevant search string. We conducted our search and selected papers from the SCOPUS scientific database using the SCOPUS Search API in November 2021. (This database includes all “articles being published in virtually all scholarly journals of any significance in the world.”)

Initially, we conducted a broad search for papers containing the term “Smart”
solely in the title, yielding over 126,000 papers. Consequently, we limited our
search to secondary research papers such as state of the arts and research agendas.
This narrowed search resulted in obtaining 2410 sources with DOIs. The inclusion
criteria related to the search string are given in Table 2.1.

**2.2.3 Step 3: Screening Papers**

We analyzed the titles and, when necessary, the abstracts and content of papers.
We excluded 69 sources that did not represent research papers or were irrelevant
to Smart Life topics. We obtained 2341 papers.<fnref n="1" /> In Table 2.1, we summarize the
exclusion criteria employed to compile the final list of papers.

<footnote n="1">¹ The complete list of these papers is available at http://cri-dist.univ-paris1.fr/rcis22/RCIS2022_Appendix%20A%20-%20references%20list.pdf</footnote>
<table><caption>**Table 2.1** Inclusion/exclusion criteria for the study on Smart topics</caption><thead><tr><th>Selection criteria</th><th>Criteria description</th></tr></thead><tbody><tr><td>Inclusion criteria (2410 sources identified)</td><td>The title includes the term “smart” and at least one of the terms “research agenda,” “state of the art,” “review,” or “survey”: Search string: TITLE(smart) AND (TITLE(“research agenda”) OR TITLE(“State-of-the-art”) OR TITLE(review) OR TITLE(survey))</td></tr><tr><td rowspan="3">Exclusion criteria (2341 sources selected)</td><td>The source is not a research paper (erratum, retracted, etc.)</td></tr><tr><td>The source is related to an abbreviation SMART, for instance, SMART (stroke-like migraine attacks after radiation therapy) syndrome.</td></tr><tr><td>The source mentions the term “smart,” which is used in its ordinary sense, like “working smart and hard”</td></tr></tbody></table>

### 2.2.4 Step 4: Classification Scheme Definition

The goal of this step in the SMS is to determine the classification scheme to be applied to the acquired results. To answer the defined research questions, we grouped all relevant papers according to the Smart application category and the year of publication of the paper (the second criterion will be presented in detail in the next chapter).

### 2.2.5 Step 5: Data Extraction

For each paper, we identified the main topic based on the title and abstract (and content if required). Within the identified topics, we observed that they concerned either applications or artifacts. We focused only on applications. We grouped these applications in categories related to their main usage, like Smart industry, Smart healthcare, Smart agriculture, and so on. Finally, we grouped these categories into four Smart domains: persons, society, environment, and enterprises. Thus, we obtained a taxonomy composed of a hierarchy of Smart applications.

### 2.2.6 Validity Threats

Qualitative research relies on subjective, interpretive, and contextual data. Therefore, we analyzed the potential biases that could jeopardize the validity of our research. Thomson (2011) proposes five categories of validity. To mitigate the impact of validity threats on our study, we present them along with the corresponding mitigation actions as follows.
*Descriptive validity* (accuracy of the data). We standardized the concepts and criteria utilized in the study and structured the information to be collected through a data extraction form, ensuring a consistent recording of data.

*Theoretical validity* (ability to get the information that it is intended to capture). We used a search string and implemented it across a library encompassing the most popular digital repositories in computer sciences and software engineering. We established a set of inclusion and exclusion criteria. To mitigate the risk of overlooking available evidence, we employed two distinct search methods: an automatic search and a manual search (backward and forward snowballing). The decision to focus on English sources aimed to minimize the impact on disregarding other languages.

*Generalization validity* (ability to generalize the results). Our set of research questions is sufficiently broad to identify and classify the findings related to Smart applications.

*Evaluative validity* (conclusions reasonable given the data). Two researchers independently examined the papers, with an overlap in their studies to identify potential variations in analysis. Every conclusion was validated by at least two researchers.

*Transparency validity* (repeatability of the research protocol). The research process protocol is sufficiently detailed to ensure it can be thoroughly replicated.

## 2.3 Four Main Domains of the Smart Applications Taxonomy

Many applications have interconnections, yet a systematic structuring is necessary. To address this, we studied the subjects covered by the 2341 selected papers of the SMS(). Initially, we adopted a bottom-up approach by looking the titles of all papers. However, when titles proved insufficient in identifying the application field, we extended our examination to include abstracts. If further clarification was required, we looked into the content of the papers.

We assigned each paper a classification of 1–3 levels of applications (e.g., Smart energy → Smart grid → Smart grid communication). This classification was applied to all papers except those addressing Smart artifacts, technologies, or systems without a clear association with a concrete application field. An expert carried out the assignment of each paper to a Smart application. In cases where the alignment of a topic with a Smart domain was not straightforward, a more in-depth analysis involving two or more experts was conducted to reach a consensus.

After qualifying the papers, we structured them within a taxonomy using a top-down approach. This involved categorizing the major fields of Smart Life, such as Smart home, Smart city, Smart industry, Smart healthcare, etc. and organizing the identified application fields within these major fields. Considering the number of major application fields, we organized them into four Smart Life domains: Smart applications for persons, Smart applications for environment, Smart applications for society, and Smart applications for enterprises (Fig. 2.1).
<figure><img src="image_13.png" /><figcaption>Fig. 2.1 Smart Life domains</figcaption></figure>

* *Smart applications for Persons* contain applications focused on the personal Life of humans, like Smart healthcare, Smart home (Smart living), Smart welfare, etc. (see Sect. 2.4)

* *Smart applications for Environment* include various topics related to the study and preservation of the environment: Smart natural resource management, Smart earth, Smart disaster management, and Smart space exploration (see Sect. 2.5).

* *Smart applications for Society* deal with various not-for-profit aspects of human life in groups, including Smart city and Smart village. It also includes applications related to the management of people living in groups, such as Smart citizenship, Smart state, Smart policing, and so on (see Sect. 2.6).

* *Smart applications for Enterprises* concern applications in the for-profit sectors, covering three basics domains: Smart agriculture, Smart industry, and Smart service industry. This category also covers applications related to Smart transportation and Smart business management (see Sect. 2.7).

We detail the sub-categories in the following sections.

## 2.4 Smart Applications for Persons

In our study, we found three categories of Smart applications belonging to this domain: Smart healthcare, Smart home, and Smart welfare (Fig. 2.2).
<figure><img src="image_14.png" /><figcaption>**Fig. 2.3** Smart Life applications for Smart healthcare</figcaption></figure>

<figure><img src="image_15.png" /></figure>

## 2.4.1 Smart Healthcare

Smart healthcare does not have a unique definition. Yin et al. (2018) define Smart healthcare as that besides clinical usage; it utilizes implantable and wearable medical devices to gather, store, and process various types of physiological data during daily activities. However, our SMS allows us to identify six categories of Smart healthcare applications, as shown in Fig. 2.3.

**Smart Patient Healthcare** There are a lot of studies about technologies linked to the care of patients, especially for specific needs. For instance, Moraitou et al. (2017) look into the needs of vulnerable groups that need nursing for a long period of time and offer a review on the main technological, psychological, ethical, and economic challenges raised by the implementation of a Smart health caring home.

**Smart Elder Healthcare** Information technologies in geriatric healthcare are needed, due to rapidly ageing societies. Zhao et al. (2021) propose a review aiming to examine the extent, variety, and characteristics of existing Smart concepts and feasible healthcare technologies in nursing home settings. Demiris and Hensel (2008) look into Smart homes-needed features to consider older or disabled people.

**Smart Disabled People Healthcare** In the same way, there are works looking into specific technological needs for disabled people. For instance, Jamwal et al. (2020)
aim to explore the impact of Smart home and communication technology on the outcomes of people with disabilities and complex needs.

**Smart Healthcare Monitoring** Yusuf et al. (2020) define a health monitoring system as a system that allows patients to monitor the health-related problem to avoid further complications that could result in loss of life.

**Smart Vaccination** Andries et al. (2014) propose to use the current repertoire of devices used in RNA synthetic biology to propose programmable “Smart vaccines” that will revolutionize the field of RNA vaccination.

**Smart Medication** Medication can be identified and delivered with the help of Smart artifact. We found a subset of these Smart applications specifically addressing the delivery of drugs, essentially insulin for diabetic people.

*Smart drug delivery:* These artifacts are able to deliver drugs to people. For instance, Smart insulin pens, which automatically delivers insulin to people as a continuous subcutaneous infusion, were being used by 375,000 people with type 1 diabetes in 2008 (Zisser et al. 2008).

## 2.4.2 Smart Home (Smart Living)

The aim of the Smart home is to improve the quality of life for its residents through automating household tasks, such as energy management, security surveillance, and so on (Alaa et al. 2017). Smart home is defined in Lutolf (1992) as “the integration of different services within a home by using a common communication system. It assures an economic, secure, and comfortable operation of the home and includes a high degree of intelligent functionality and flexibility.” Smart home applications can be categorized into several other type of applications, as shown in Fig. 2.4.

### Smart Home Management

*Smart Home Water Management.* Water management is possible by real-time monitoring of water level and quality. Real-time water level monitoring can significantly reduce wastage of water due to overflow from tanks. The water management system can also help detect water leaks in a Smart home by analyzing water levels during different hours of the day (Singh and Suhaib 2020). Different Smart water frameworks, including definition and architecture, are proposed, as in (Li et al. 2020).

*Smart Home Temperature Management.* It groups all the applications that help regulate the temperature in a Smart home system. Malekpour et al. (2020) address these applications by studying Smart thermostats, a device which has the capability to remotely control the set points as well as to adjust set point schedules of the temperature system. In some cases, it can also monitor occupant behavior and learn from this behavior for improved operational efficiency and occupant comfort.
<figure><img src="image_16.png" /><figcaption>**Fig. 2.4** Smart Life applications for Smart home (Smart living)</figcaption></figure>

*Smart Home Air Management.* On the same way as water or temperature, air can be controlled by the use of sensors. Guyot et al. (2018) provide a literature review on Smart ventilation used in residential buildings, based on energy and indoor air-quality performance.

*Smart Home Energy management.* Various system components such as hardware elements, software algorithms, network connections, and sensors need to collaborate to offer diverse services in a Smart home environment. Energy management at the household level must consider reducing environmental impact while supporting human lifestyles (Komminos et al. 2014). We can find two different kinds of energy management specific to a Smart home system: *Smart home gas management* and *Smart home electricity management*.

**Smart Human Mobility in Home** Smart homes can integrate considerations for human mobility within the residence environment. For example, the use of Smart wheelchairs can lead to a seamless user experience and enable control over all household appliances (Leaman and La 2017).

**Smart Home Security** There is a range of security issues in a Smart home management (Lin and Bergmann 2016). Yan et al. (2021) focus on four aspects of Smart home security, Smart devices, cloud platforms, mobile applications, and communications, and summarize the attack and defense methods. Aljanah et al. (2021) specifically study the solutions offered about how to authenticate a large number of heterogeneous and possibly resource-constrained devices in a secure and efficient manner.

**Smart Home Building** In Smart home buildings, it is possible to manage household appliances such as lighting, air conditioning, fans, washing machines, TVs, refrigerators, security systems, and so on. Through the application of artificial
<figure><img src="image_17.png" /><figcaption>**Fig. 2.5** Smart Life applications for Smart welfare</figcaption></figure>

intelligence, these systems can also achieve self-control and monitoring capabilities (Shaker 2020).

**Smart Welfare** Smart welfare is about how Smart technologies can be applied to enhance the life quality of a person with the help of devices and sensors. Figure 2.5 shows the different types of Smart welfare applications.

**Smart Clothing** Smart clothes embed Smart wearables into garments. Fernandez-Carames et al. (2018) propose the paradigm of “Internet of Smart clothing,” which envisions a world where Smart garments communicate with each other, with the objects on their environment, and with remote servers on the Internet in order to provide advanced services.

**Smart Game Playing** Costa et al. (2018) present the application of proxemics in digital games to enhance game-mediated interactions within Smart ecosystems.

**Smart Animal Welfare** Animal welfare refers to an assessment of whether animals are healthy, free of pain and suffering, and positively stimulated in their environment. Smart technologies can also be used for animal welfare, as studied in (Jukan et al. 2017), for instance.

**Smart Sport** Smart sport refers to the integration of advanced technologies and data-driven solutions in the realm of sports to enhance performance, training methodologies, and overall athlete experience. This paradigm leverages innovations such as wearables, sensors, analytics, and connectivity to collect and analyze real-time data, providing valuable insights for athletes, coaches, and sports organizations.

*Smart Fitness*. Smart fitness is divided into three categories: fitness trackers (including wearable and non-wearable sensors), movement analysis, and fitness applications (Farrokhi et al. 2021). The aim is to enhance training performance in a fitness environment.

*Smart Cycling*. These applications offer interactions between traffic participants, to help communicate the cyclist's intentions to others or to enhance playfulness and social cohesion, on and beyond the cycle path. Smart cycling technologies offer
possibilities to be in touch with people who are not sharing the road with you, and a ride can be recorded, “re-lived,” and shared in improvised social networks (Nikolaeva et al. 2019).

*Smart Training*. With the increase of participation trends in mass sporting events, as well as the involvement of people in sporting activities, there is a need for systems/applications that can guide, help, and support people in enjoying their activities. Modern technology is revolutionizing the way athletes maximize their performance and compete on a higher level than ever before (Rajsp and Fister 2020).

## 2.5 Smart Applications for Environment

This domain encompasses three types of applications, those dedicated to natural resource management, those focused on Smart Earth solutions, and those geared towards space exploration, as shown in Fig. 2.6.

### 2.5.1 Smart Natural Resource Management

The management and preservation of natural resources are paramount in today's world, driven by the pressing need for sustainability and environmental stewardship. Within this context, Smart technologies are playing an increasingly crucial role in revolutionizing how we approach resource conservation and management. This section looks into several key Smart applications focused on natural resources, highlighting innovative solutions that harness technology to address critical challenges in water management, climate engineering, forestry, and air quality, as shown in Fig. 2.7.

**Smart Water Management** Efficient water management has emerged as a significant concern for numerous countries and water industries. Researchers are leveraging ICT to develop self-learning systems, which possess the capability to enhance water management efficiency (Gupta et al. 2020a). We can distinguish two

<figure><img src="image_18.png" /><figcaption>**Fig. 2.6** Smart Life applications for environment</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Fig. 2.7** Smart Life applications for Smart natural resource management</figcaption></figure>

types of Smart water management systems: Smart flood monitoring and Smart water grids.

*Smart Flood Monitoring.* Smart technology is used to determine the accurate level of flooding of land, which is ordinarily dry in nature, and the connected phones are able to give the forecasting information to the affected people to prevent the unnecessary loss caused by this natural hazard (Subashini et al. 2021).

*Smart Water Grid.* There is a need for water management, measures to control water scarcity and a way to conserve water (Behera and Pradhan 2019).

**Smart Climate Engineering** Global warming represents one of the most significant challenges humanity faces. To achieve climate goals, technologies such as CO₂ capture and storage and CO₂ capture and utilization are crucial for mitigating hard-to-avoid CO₂ emissions (Wang et al. 2020).

**Smart Forestry** It is based on digital forestry, using cloud computing, Internet of Things, mobile Internet, big data, and other new generation information technology. It can promote the coordinated development of forestry resource management and ecosystem construction (Zou et al. 2019).

**Smart Air Quality Management** Air quality is a huge and pressing global issue, leading to over 7 million annual deaths according to the World Health Organization. Access to air quality information allows individuals to make informed decisions, yet analyzing this data and implementing effective solutions remains challenging. Efficient methods for analyzing big data are crucial to reveal hidden insights and address the invisible impacts of air pollution effectively (Iskandaryan et al. 2020).
## 2.5.2 Smart Earth

Smart Earth is a set of environmental applications of the Internet of Things but articulated across a much wider range of ecosystems and land use types (Bakker and Ritts 2018). Smart Earth technologies use terrestrial, aquatic, and aerial sensors, satellites, and monitoring devices.

## 2.5.3 Smart Space Exploration

Over the last century, the progress of space exploration has imposed demanding requirements on payloads and increased the spacecraft complexity, with the result of a constant rise in their average launch mass, but space industries and agencies are currently more interested in very small satellites with launch masses below 10 kg (nanosatellites, picosatellites, femtosatellites). Niccolai et al. (2019) discuss about the result of this miniaturization process with the design of integrated miniaturized components into a common substrate of silicon wafer layers. It gives the possibility of creating a single component-satellite, capable of generating and distributing power, exchanging heat with the environment, communicating, and hosting a payload, with subsystems assembled in a 20 mm × 20 mm × 3 mm monolithic integrated circuit.

# 2.6 Smart Applications for Society

The integration of advanced technologies into urban environments has given rise to the concept of “Smart cities,” representing a significant subset of Smart applications for society. This section explores the multifaceted initiatives within the domain of Smart applications for society, as shown in Fig. 2.8.

This domain contains a wide set of Smart applications, some widely known, such as Smart city or Smart education, or a bit less studied, like Smart state or Smart citizenship.

### 2.6.1 Smart City

As defined in Washburn et al. (2009), the realization of a Smart city is about integrating software services and applications to improve regular city services and ease and facilitate the life of citizens. In Chamee (2020), Smart city is “a city that collects and utilizes data gathered from distributed sensors and video cameras that connect everything from trash bins to streetlights.”
<figure><img src="image_20.png" /><figcaption>**Fig. 2.9** Smart Life applications for Smart city</figcaption></figure>

<figure><img src="image_21.png" /></figure>

To provide more specific examples, the concept of Smart city is employed to address challenges in urban cities by providing a high-quality living environment for citizens (Al-Ani et al. 2019). Therefore, within the taxonomy, the Smart city application domain contains applications addressing issues as security, urban growth, lighting, parking, and more (see Fig. 2.9).

**Smart City Security** The dependence on ICT makes Smart cities prone to cyberattacks (Alibasic et al. 2017). We identify two main categories of Smart
applications relative to Smart city security: Smart city surveillance and Smart city identification.

**Smart City Surveillance.** Smart cities enhance residents' lifestyle by offering efficient infrastructure and improved security. Continuous surveillance, often a repetitive and monotonous task, can diminish the performance of human guards over extended periods, and the use of the huge amount of data can help increase this performance (Thakur et al. 2021).

**Smart City Identification.** An essential component of Smart cities is their surveillance infrastructure, which necessitates intelligent techniques for analyzing video footage from surveillance cameras (Behera et al. 2020).

**Smart Parking (The Parking Process)** Smart parking sensors and technologies assist drivers in finding vacant parking slots while they are on the way to their destination (Zulfiqar et al. 2021).

**Smart City Growth** Smart growth aims to enhance housing affordability and diversity. However, one of the goals of Smart growth, compact development, restricts land development and presents challenges for affordability (Addison et al. 2013). Smart growth-related planning practices, including growth management policies and urban design tools like transit-oriented development and urban infill, can influence housing markets significantly.

**Smart Urbanism.** The tightening integration of urban systems, coupled with increased coordination in urban domains, enables the provision of more holistic views and synoptic city intelligence to give a better understanding of urban life (Bibri 2019).

**Smart City Management** The concept of Smart cities involves comprehensive city organization and management through technology. It is built and sustained by integrating technologies like sensors, electronics, and networks. The primary motivation behind investigating Smart city services is to enhance city services for citizens and improve their overall quality of life (Al-Smadi et al. 2019).

**Smart City Lighting** Lighting systems play an important role in the evolution process of Smart cities, thanks to their ability to affect city life at night along with people's mood and behavior (Scorpio et al. 2020).

**Smart Street Light Management.** Smart street light management refers to a networked system of streetlights equipped with sensors, actuators, and communication infrastructures to illuminate roads, highways, parking areas, and public spaces efficiently. Addressing environmental concerns and aiming to reduce electricity costs, utilizing these applications with real-time monitoring and energy efficiency features benefits municipalities, utility companies, and city residents alike (Mahoor et al. 2020).

**Smart Parking Light Management.** With the rise in population, there is a corresponding increase in the number of vehicles on roads, necessitating the development of sustainable and efficient parking solutions. However, continuous lighting in these
<figure><img src="image_22.png" /><figcaption>**Fig. 2.10** Smart Life applications for Smart education</figcaption></figure>

areas is not resource-efficient. To address this, integration of automated lighting that activates only when a vehicle is detected, thus combining Smart parking with automated lighting for a practical, sustainable, and efficient solution (Shastry et al. 2020).

## 2.6.2 Smart Education

Conventional teaching methods, involving classroom instruction followed by independent student exercises, are being supplanted by modern learning approaches, including distance learning, mobile learning (m-learning), personalized learning, flipped and blended learning, social collaborative learning, and game-based learning, among others (Demir 2021). Figure 2.10 shows the sub-taxonomy for Smart education.

**Smart University** The concept of Smart university encompasses a thorough modernization of educational processes, leading to enhanced quality across various university activities such as education, research, and commercial endeavors. This transformation includes the adoption of technologies like Smart boards, screens, and ubiquitous wireless Internet access, redefining the educational landscape with advanced tools and connectivity (Tikhomirov et al. 2015).

*Smart Learning*. This modern education approach is using technology integration for enhanced learning outcomes by combining Smart devices and pedagogies, to prioritize personalized and adaptive learning experiences (Wong and Li 2021).

*Smart Classroom Management*. Installing Smart devices and emerging trending technologies help teachers focus on student's learning process in place of wasting time in taking attendance for a large group of students, transforming traditional classrooms into Smart classrooms (Gupta et al. 2020b).

**Smart Campus** Similar to the goal of a Smart city, which is to make the quality of life of its population increase, the purpose of a Smart campus is also about how the quality of life of university stakeholders can be improved (Imbar et al. 2020).
### 2.6.3 Smart Policing

Smart policing refers to the application of data-driven approaches by police authorities. Afzal et al. (2020) present a framework to show the connections between Smart use of data and police approaches and strategies to show how Smart policing can be an area of increased interest in digital government and public management research.

### 2.6.4 Smart Court

A Smart court is characterized by the automation and digitization of judicial procedures, forming part of a comprehensive set of reforms aimed at establishing a more precise and consistent judiciary. In Smart courts, certain tasks may be automated using programs, potentially incorporating learning algorithms but not necessarily all. The essence of Smart courts lies in the interaction between human judges and the technology that supports them in their work during this partially automated digital judicial process (Papagianneas 2021).

### 2.6.5 Smart Social Media

Smart social media refers to a set of practices and approaches used to optimize the use of social media in an intelligent and effective manner. This involves leveraging advanced technologies, algorithms, and analytical tools to enhance the management, engagement, and relevance of content on social media platforms. The goal is to achieve more meaningful results in terms of visibility, interaction, and conversion through a strategic and thoughtful approach to social media (Lia et al. 2021).

### 2.6.6 Smart Village

The notion of a Smart village involves leveraging technology to address intercon- nected challenges such as financial exclusion, poverty, hygiene, and education, presenting an empowering approach to uplift underserved communities through improved access to power, education, and entrepreneurial opportunities (Kaur and Parashar 2021).
## 2.6.7 Smart State

There are evident trends indicating increased inter-organizational collaboration, information sharing, and integration, paving the way for what could be termed as a Smart State. Gil-Garcia (2012) delves into the promises and challenges already identified in government information sharing and integration initiatives. The case study of the Brisbane city is studied in Hortz (2016); it evaluates the Smart State strategy's effectiveness, analyzes barriers to knowledge-based urban development, explores limitations in public policy development for urban planning, and presents lessons learned to enhance frameworks for intelligent urban planning in the context of global economic development.

## 2.6.8 Smart Island

The European Union has clearly outlined priorities for sustainable and low-carbon energy systems, highlighting the significant role of islands. These islands are seen as ideal locations to develop and test innovative strategies and solutions, which can then be applied to accelerate the transition to sustainable energy systems on the mainland (Groppi et al. 2021). Smart island applications are then good tests for techniques that deal with such solutions in the insular contexts.

## 2.6.9 Smart Citizenship

To effectively develop Smart cities, it is crucial to involve citizens as integral participants. They play key roles as users, decision-makers, consumers, and sources of valuable data and information (Tadili and Fasly 2019). Smart citizenship includes Smart voting (see Fig. 2.11).

*Smart Voting*. In democratic countries, the voting system plays a major role during elections. Electronic voting machines can be used, but they need more manpower, are time-consuming, and also less trustworthy. For avoiding misconceptions during elections, there are lot of advanced techniques that are being proposed using various methods (Deepika et al. 2017).

<figure><img src="image_23.png" /><figcaption>**Fig. 2.11** Smart Life applications for Smart citizenship</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>Fig. 2.12 Smart Life applications for enterprises</figcaption></figure>

## 2.7 Smart Applications for Enterprises

This domain contains applications about transportation, agriculture, service industry, business management, and industry, as shown in Fig. 2.12.

### 2.7.1 Smart Transportation

Smart transportation refers to the integration of advanced technologies, data analytics, and intelligent systems to enhance the efficiency, safety, and sustainability of transportation networks. It optimizes traffic flow, improves public transit systems, enhances vehicle safety, and reduces environmental impact. Smart transportation aims to create a seamless, interconnected, and data-driven mobility ecosystem for the benefit of both commuters and the overall transportation infrastructure. The Smart transportation field includes several topics illustrated in Fig. 2.13.

**Smart Driving** Smart driving refers to a conscientious and environmentally friendly approach adopted by drivers in response to the growing emphasis on climate change in social and political discussions (Young et al. 2011). While traditional ergonomics research has predominantly concentrated on enhancing vehicle safety and performance efficiency, the evolving landscape demands a shift toward the development of systems that actively promote eco-conscious driving behavior. Smart driving integrates technological solutions, behavioral insights, and ergonomic considerations to contribute to sustainable and environmentally responsible transportation practices.

**Smart Transportation Mobility** Smart mobility, a crucial element of Smart cities, has the potential to alleviate traffic congestion, reduce commute times, lower road accidents, and offer passengers the flexibility to personalize their journeys. Planning Smart mobility solutions ranks among the foremost challenges faced by major cities globally, necessitating deliberate actions supported by advanced technologies (Biyik et al. 2021).

**Smart Vehicle Relocation** Traditional car-sharing systems require users to pick up and return cars at designated stations, whereas free-floating systems establish a geo-
<figure><img src="image_25.png" /><figcaption>**Fig. 2.13** Smart Life applications for Smart transportation</figcaption></figure>

fence around the city center, allowing users to hire and drop cars directly at or near their desired locations without visiting a station (Hermann et al. 2014).

**Smart Traffic** The increasing number of vehicles on roads and limited capacities are making traffic networks increasingly complex and challenging, resulting in congestion and traffic jams that significantly impact the economy, environment, and human health. As a result, there is a strong motivation to develop new traffic management systems to address these issues effectively (Merrad et al. 2016).

**Smart Vehicle Sharing** Transport and urban planners are focused on creating policies that improve service access for transportation activities, ensuring convenient and affordable public transportation, integrating all modes effectively, ensuring safety, and developing transport infrastructure. The initial cost of fully autonomous vehicles may be prohibitive, leading to their introduction in the public market as shared-fleet services initially. On-demand mobility, incorporating both car-sharing and ride-sharing, along with automation, especially in integrated demonstrations like shared autonomous vehicles, are presented as sustainable solutions to urban mobility challenges (Golbabaei et al. 2021).

**Smart Road Network** The management and maintenance of road infrastructures require extensive data on their maintenance history and current condition. There is a growing need to improve the efficiency of managing and maintaining road infrastructures. In recent years, several promising technologies have emerged to address various identified challenges in this area (Carneiro et al. 2018).
**Smart Vehicle Charging** The transportation sector is currently grappling with challenges related to its reliance on oil-based energy models. Electric mobility represents a crucial step toward sustainability in contemporary society. The integration of plug-in electric vehicles into electrical distribution networks stands to benefit all stakeholders by enhancing system efficiency both technically and economically. Smart charging technology plays a pivotal role in achieving this ambitious objective (Garcia-Villalobos et al. 2014).

**Smart Airport** Smart airports enhance both passenger and airport experiences by employing technologies, sensors, and processors, to maintain an inclusive and continuous connection between passengers and various services, accessible anytime and anywhere within the airport ecosystem (Alansari et al. 2019). They establish a real-time communication framework that enables seamless responsiveness and analysis. Unlike traditional setups, passengers at these airports experience unified check-in processes, with all crucial information centralized.

## 2.7.2 Smart Agriculture

Smart agriculture refers to the use of advanced technologies, including the Internet of Things, sensors, data analytics, and automation, to optimize various aspects of farming processes. The goal is to enhance efficiency, productivity, and sustainability in agricultural practices by collecting and analyzing real-time data, enabling informed decision-making, and promoting resource optimization. Smart agriculture encompasses a range of applications, from precision planting and irrigation to livestock monitoring and crop management, ultimately contributing to more intelligent and resource-efficient farming practices. The sub-topics of Smart agriculture are shown in Fig. 2.14.

**Smart Farming** The increase of the global population and its subsequent surge in food demand pose challenges to agricultural production. Concurrently, decreasing rural workforce and rising production costs further compound the challenges faced by the agricultural sector. In response to these complexities, the concept of Smart farming, using the Internet of Things, emerges as a farm management solution. Smart farming employs IoT to address contemporary issues in food production, enhancing efficiency and sustainability (Navarro et al. 2020).

**Smart Fishery** Smart fishing spans various domains, as the assessment of the economic aspects of commercial fleets, electronic monitoring of catch and bycatch, identification and prediction of fishing grounds, and the simulation of fishing vessel behavior (Ebrahimi et al. 2021).

**Climate-Smart Agriculture** The literature contains numerous projections regarding the impact of climate change on agriculture, particularly on crop, livestock, and fishery production sectors. Understanding the scale of these impacts is crucial for
<figure><img src="image_26.png" /><figcaption>**Fig. 2.14** Smart Life applications for Smart agriculture</figcaption></figure>

informing adaptation strategies and developing policies that promote climate-Smart agriculture (Zougmoré et al. 2016).

**Smart Crop Management** In the context of Smart agriculture, contemporary farming increasingly relies on new technologies. IoT farmers can remotely monitor their crops and equipment using smartphones and computers (Mekala and Viswanathan 2017). Crop production in agriculture is influenced by numerous factors including climate, geography, biology, economics, history, politics, socio-economic conditions, and agro-ecological zoning. Intelligent agro-ecological zoning plays a crucial role in this context, aiming to accurately suggest and predict crops that optimize production yields (Chetan et al. 2021).

**Smart Livestock Farming** The future of livestock farming is marked by precision, sustainability, and intelligence. By harnessing modern information technology, precision livestock farming addresses traditional farming challenges, paving the way for modernization and stimulating sustainable growth within the livestock sector (Zhang et al. 2021).

**Smart Irrigation** In most parts of the world, the lack of efficient irrigation methods leads to wastage or excessive use of irrigation water. Installation of Smart water irrigation systems can prove useful for saving water, time, and cost (Issaka et al. 2018).

**Smart Pest Management** Insect pests pose a significant threat to global agricultural crop yield and quality. Efficient and accurate monitoring of insect pest populations is vital for prediction and control measures. The emergence of Smart pest monitoring, driven by advancements in artificial intelligence theories and technologies, aims to enhance the automatic and intelligent collection of crucial crop insect pest data (Li et al. 2021).
<figure><img src="image_27.png" /><figcaption>**Fig. 2.15** Smart Life applications for Smart service industry</figcaption></figure>

### 2.7.3 Smart Service Industry

The concept of a “Smart service industry” encapsulates a diverse array of applications that include cutting-edge technologies to enhance customer experiences, improve operational efficiency, and drive sustainable practices across various sectors, from data-driven insights in tourism to streamlined shopping experiences and optimized restaurant operations, as shown in Fig. 2.15.

**Smart Tourism** Smart tourism denotes the growing reliance of tourism stakeholders on emerging information and communication technologies for substantial data transformation, leading to enhanced tourist experiences, heightened destination competitiveness, and improved sustainability. The application of software and analytics to analyze and predict tourist behaviors represents a notable trend among practitioners, ultimately benefiting tourists, industry players, and destinations alike (Ye et al. 2020).

**Smart Shopping** Smart shopping includes consumers' efforts to minimize the expenditure of time, money, or energy while maximizing the hedonic and utilitarian value derived from the shopping experience (Green Atkins and Youn-Kyung 2012).

**Smart Restaurant** Accurate demand forecasting plays a pivotal role in the effectiveness of a restaurant's yield and revenue management system, a critical aspect for both independent restaurants and larger restaurant chains (Lasek et al. 2016).

### 2.7.4 Smart Business Management

To ensure the sustainability of their businesses, various companies, especially for small and medium-sized enterprises (SMEs), must embrace the Internet as a crucial element in designing new business model values, enhancing customer experiences, and bolstering internal capabilities that support key operations. Companies need to leverage information and communication technology in their business operations to remain competitive and meet evolving market demands (Rozmi et al. 2021).
<figure><img src="image_28.png" /><figcaption>**Fig. 2.16** Smart Life applications for Smart industry</figcaption></figure>

## 2.7.5 Smart Industry

A “Smart industry” application refers to an application to manufacturing and production that integrates IoT advanced technologies to optimize processes, improve efficiency, and enhance decision-making. We can find several kinds of applications related to Smart industry, especially Smart energy ones, as shown in Fig. 2.16.

**Smart Energy** Being the most widespread in the manufacturing sector, Smart energy applications have a significant impact on sustainability and the environment. These include renewable energies such as solar, wind, hydropower, and biomass,
as well as Smart grids, catalysis industry advancements, and innovations in power
storage and distribution (Rangel-Martinez et al. 2021).

*Smart Grid*. The term “Smart grid” typically refers to a technology class enabled by two-way communication techniques. These techniques facilitate real-time data transfer, essential for control systems to enhance efficiency, reliability, and flexibility (Abdelwahab et al. 2019). We can distinguish six different types of Smart grid applications: for side management, for communication, for distribution (micro-grid), for resilience, for optimization, and for natural gas.

*Smart Gas Management*. Smart gas management includes various applications related to the handling of gas resources. For example, Smart gas sensing integrates physical and chemical material sciences, electronic circuits, statistics, chemometrics, communication networks, and machine learning methods. The general process of Smart gas sensing technology includes sensing materials and sensor arrays, signal processing for drift compensation and feature extraction, and gas pattern recognition technology based on machine learning, as outlined by Feng et al. (2019).

*Smart Energy Delivery*. In energy-related sectors, direct interaction between utility providers and consumers from residential, industrial, and commercial sectors is crucial. The integration of digital computation and communication technologies ensures secure, efficient, and reliable electricity delivery and information exchange among power generators, utility companies, and electric power consumers (Reka and Dragicevic 2018).

*Smart Energy Storage*. Energy storage technologies are central in today's context, spanning from low-capacity mobile storage batteries to high-capacity batteries linked with intermittent renewable energy sources (Sufyan et al. 2019).

*Smart Energy Community*. Community energy storage represents a new generation of energy storage important to Smart grids. Its placement at the grid's edge and proximity to customers provide significant advantages over storage located at substations (Sardi and Mithulananthan 2014). We can identify two types of Smart energy community applications, those aiming to help municipality and those addressed to the neighborhood.

*Smart Wind Energy Management*. Given that renewable energy sources depend on weather conditions beyond human control, there is a growing need for new functionalities within electricity networks (Xing et al. 2013). The study and integration of wind power are crucial aspects of Smart wind energy management applications. Two different kinds of Smart applications related to Smart wind energy management can be identified: for energy metering and for energy consumption and price forecasting.

*Smart Energy Consumption*. There is a gradual increase in electricity demand, but establishing new power plants for electricity generation is challenging due to stringent pollution control policies and increased government awareness of environmental preservation. To address this challenge, sufficient profiling of electricity consumption can be used to explore alternative methods for managing
electricity loads in the future while utilizing existing generation capacity (Ahmad et al. 2018).

**Smart Manufacturing** In response to the growing demand for highly customized products and smaller lot sizes, companies must rapidly adapt to new market opportunities to thrive in a competitive environment, underscoring the importance for manufacturers to cultivate approaches fostering dynamism, flexibility, and reconfigurability at the factory level (Alemao et al. 2021).

**Smart Factory** Quite close to Smart manufacturing, Smart factories focus on the transformation of factories into smarter, more efficient, safer, and environmentally sustainable environments results from the amalgamation and integration of production technologies, devices, information and communication systems, and data in network infrastructures. Illustratively, in a Smart factory, the capabilities of flexibility and re-configurability in production, coupled with enhanced customer interaction for understanding their needs, enable the customization of goods and services with cost efficiency comparable to mass production (Strozzi et al. 2017).

**Smart Logistics** Smart logistics enables the intelligent implementation of an integrated logistics system through real-time processing and comprehensive analysis of logistical information. It enhances end-to-end visibility; optimizes logistics processes including transportation, warehousing, and distribution; and provides efficient information services. Smart logistics not only contributes to significant time and cost savings but also holds the potential to mitigate environmental pollution associated with logistical activities (Song et al. 2021).

**Smart Industry Building** Buildings are evolving toward interactive features that can dynamically adjust to users' needs and changing conditions, be it external factors like climate and grid prices or internal factors such as occupants' requirements. This transition reflects a shift from unresponsive buildings to highly efficient ones capable of energy consumption, production, storage, and distribution (Al Dakheel et al. 2020).

## 2.8 Clusters

In addition to this main taxonomy, we've discovered the existence of generic clusters that have relevance across multiple areas; thus, they are applied across various Smart applications. We can cite Smart energy and Smart transportation clusters.

Smart energy can be seen as an independent application domain within the Smart industry. However, its presence extends beyond this application, making appearances in other fields such as Smart city, Smart home, etc. In case of Smart cities, energy applications are vital, as energy management systems enhance energy economics, consumption efficiency, grid stability, and reliability. The residential sector significantly impacts global energy consumption and offers flexibility in adjusting consumption patterns, helping address major global challenges (Mahmood
et al. 2021). For Smart homes, energy applications allow to reduce environmental impact and supports lifestyles. They push residents to participate in the energy market and addresses technical aspects like peak shaving, load shifting, and strategic conservation while also having significant socioeconomic impacts (Amer et al. 2014).

Another identified cluster is Smart transportation, which designates applications specifically associated with the transportation and mobility field. For instance, Smart transportation applications in Smart cities include Smart electric vehicles. Smart vehicle charging could be applied in Smart cities to monitor the flow of electric vehicles. They involve a wide range of technologies from road and rail vehicles to surface and underwater vessels and even electric aircraft (Shuai et al. 2016). It allows to contribute to the overall efficiency and resilience of Smart cities.

Smart transportation also contributes to the energy distribution in the grid, for instance, through the charging of electric vehicles. To ensure the sustainability, reliability, and efficiency of the electric-power distribution grid, it is essential to develop and implement Smart charging techniques. By integrating Smart transportation with Smart energy solutions, it is possible to promote sustainable mobility while optimizing energy distribution and usage (Igbinovia et al. 2016).

## 2.9 Conclusion

In this chapter, we presented a classification of Smart Life applications by means of a unique taxonomy. We conducted a systematic mapping study based on 2341 publications to define this Smart application taxonomy.

We observed the introduction of some more special concepts in the literature, even if they are not fully implemented yet.

* *Smart Earth* is defined as an innovative approach to environmental monitoring in (Bakker and Ritts 2018).

* *Smart island* aims to classify current Smart-related methodologies in the urban context and assess their potential applicability to islands (Desogus et al. 2019).

* The concept of *Smart State* (Gil-Garcia 2012) aligns with the notion of “knowledge-based urban development.”

* Kaur and Parashar (2021) provide a literature review on the use of blockchain technology in *Smart villages*.

Finally, the term Smart world appeared in the literature (Kumari et al. 2020) (Liu et al. 2019) as a unifying term encompassing various Smart applications. For instance, Liu et al. (2019) define a Smart world as “an attractive prospect with comprehensive development of ubiquitous computing involving penetrative intelligence into ubiquitous things.” In our perspective, this term corresponds with our vision of Smart Life.

In developing the taxonomy for Smart Life applications, we deliberately excluded 328 sources only focused on Smart artifacts, Smart technologies, and
Smart systems without clear associations with specific application domains. Additionally, the taxonomy omits Smart infrastructure-related applications as we consider them as transversal issues (infrastructure can be found in almost all major Smart applications fields).

This taxonomy will not be a static structure; it will evolve just like taxonomies in other domains. We plan to develop our taxonomy by extracting a classification of the Smart artifacts and link them to the Smart applications and Smart domains identified in this taxonomy. We also plan to carry out a bibliometric study to explore the relationships between the Smart applications. Finally, we also intend to pursue investigations into the variety research challenges in the field of Smart Life and Smart Life Engineering. Our main goal is to discover whether a common method- ological basis could be identified in the field of Smart Life and its engineering approaches.

## References

* Abdelwahab, R.H., El-Habrouk, M., Abdelhamid, T.H.: Survey of communication techniques in smart grids. In: 21st International Middle East Power Systems Conference. (2019)

* Addison, C., Zhang, S., Coomes, B.: Smart growth and housing affordability: a review of regulatory mechanisms and planning practices. J. Plan. Lit. (2013)

* Afzal, M., Panagiotopoulos, P.: Smart policing: a critical review of the literature. Int. Conf. Electron. 12219, 59–70 (2020)

* Ahmad, T., Chen, H., Wang, J., Guo, Y.: Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renew. Sust. Energ. Rev. (2018)

* Alaa, M., Zaidan, A.A., Zaidan, B.B., Talal, M., Kiah, M.L.M.: A review of smart home applications based on Internet of Things. J. Netw. Comput. Appl. 97, 48–65 (2017)

* Al-Ani, K.W., Abdalkafor, A.S., Nassar, A.M.: Smart city applications: a survey. In: 9th Interna- tional Conference on Information Systems and Technologies. (2019)

* Alansari, Z., Soomro, S., Belgaum, M.R.: Smart airports: review and open research issues. In: Emerging Technologies in Computing, pp. 136–148. (2019)

* Al Dakheel, J., Del Pero, C., Aste, N., Leonforte, F.: Smart buildings features and key performance indicators: a review. In: Sustainable Cities and Society. (2020)

* Alemão, D., Rocha, A.D., Barata, J.: Smart manufacturing scheduling approaches—systematic review and future directions. Appl. Sci. (Switzerland) (2021)

* Alibasic, A., Al Junaibi, R., Aung, Z., Woon, W.L., Omar, M.A.: Cybersecurity for smart cities: a brief review. In: International Workshop on Data Analytics for Renewable Energy Integration. (2017)

* Aljanah, S., Zhang, N., Tay, S.W.: A survey on smart home authentication: toward secure, multi- level and interaction-based identification. IEEE Access. (2021)

* Al-Smadi, A.M., Alsmadi, M.K., Baareh, A.K., Almarashdeh, I., Abouelmagd, H., Ahmed, O.S.S.: Emergent situations for smart cities: a survey. Int. J. Electric. Comput. Eng. (2019)

* Amer, M., Naaman, A., M'Sirdi, N.K., El-Zonkoly, A.M.: Smart home energy management systems survey. In: International Conference on Renewable Energies for Developing Countries (2014)

* Andries, O., Kitada, T., Bodner, K., Sanders, N.N., Weiss, R.: Synthetic biology devices and circuits for RNA-based ‘smart vaccines’: a propositional review. In: Expert Review of Vaccines. (2014)
Bakker, K., Ritts, M.: Smart Earth: a meta-review and implications for environmental governance. In: Global Environmental Change (2018)

Behera, S.K., Pradhan, B.B.: Review on implementing smart water grid for smart cities in India. Int. J. Psychosoc. Rehab. (2019)

Behera, N.K.S., Sa, P.K., Bakshi, S.: Person re-identification for smart cities: state-of-the-art and the path ahead. Pattern Recogn. Lett. (2020)

Bibri, S.E.: The leading smart sustainable paradigm of urbanism and big data computing: a topical literature review. Adv. Sci. Technol. Innov. (2019)

Bıyık, C., Abareshi, A., Paz, A., Ruiz, R.A., Battarra, R., Rogers, C.D.F., Lizarraga, C.: Smart mobility adoption: a review of the literature. J. Open Innov. Technol. Mark. Complex. (2021)

Carneiro, J., Rossetti, R.J.F., Silva, D.C., Oliveira, E.C.: BIM, GIS, IoT, and AR/VR integration for smart maintenance and management of road networks: a review. In: IEEE International Smart Cities Conference. (2018)

Chamee, Y.: Historicizing the smart cities: genealogy as a method of critique for smart urbanism. Telematics Inform. 55 (2020)

Chetan, R., Ashoka, D.V., Ajay Prakash, B.V.: Smart agro-ecological zoning for crop suggestion and prediction using machine learning: an comprehensive review. In: Advances in Intelligent Systems and Computing (2021)

Costa, L.V., Veloso, A.I., Mealha, Ó.: A review of proxemics in ‘smart game-playing’. In: Smart Innovation, Systems and Technologies (2018)

Deepika, J., Kalaiselvi, S., Mahalakshmi, S., Agnes Shifani, S.: Smart electronic voting system based on biometrie identification-survey. In: 3rd IEEE International Conference on Science Technology, Engineering and Management (2017)

Demir, K.A.: Smart education framework. Smart Learn. Environ. 8, 29 (2021)

Demiris, G., Hensel, B.K.: Technologies for an aging society: a systematic review of “smart home” applications. In: Yearbook of Medical Informatics (2008)

Desogus, G., Mistretta, P., Garau, C.: Smart islands: a systematic review on urban policies and smart governance. In: International Conference on Computational Science and Its Applications (2019)

Ebrahimi, S.H., Ossewaarde, M., Need, A.: Smart fishery: a systematic review and research agenda for sustainable fisheries in the age of AI. Sustainability (Switzerland). (2021)

Farrokhi, A., Farahbakhsh, R., Rezazadeh, J., Minerva, R.: Application of Internet of Things and artificial intelligence for smart fitness: a survey. Computer Networks (2021)

Feng, S., Farha, F., Li, Q., Wan, Y., Xu, Y., Zhang, T., Ning, H.: Review on smart gas sensing technology. Sensors (Switzerland). (2019)

Fernández-Caramés, T.M., Fraga-Lamas, P.: Towards the internet-of-smart-clothing: a review on IoT wearables and garments for creating intelligent connected E-textiles. Electronics (Switzerland). (2018)

García-Villalobos, J., Zamora, I., San Martín, J.I., Asensio, F.J., Aperribay, V.: Plug-in electric vehicles in electric distribution networks: a review of smart charging approaches. Renew. Sustain. Energy Rev. (2014)

Gil-Garcia, J.R.: Towards a smart State? Inter-agency collaboration, information integration, and beyond. Inf. Polity. 17 (3–4), 269–280 (2012)

Golbabaei, F., Yigitcanlar, T., Bunker, J.: The role of shared autonomous vehicle systems in delivering smart urban mobility: a systematic review of the literature. Int. J. Sustain. Transp. (2021)

Green Atkins, K., Youn-Kyung, K.: Smart shopping: conceptualization and measurement. Int. J. Retail Distrib. Manag. 40 (5), 360–375 (2012)

Groppi, D., Pfeifer, A., Garcia, D.A., Krajačić, G., Duić, N.: A review on energy storage and demand side management solutions in smart energy islands. Renew. Sust. Energ. Rev. (2021)

Gupta, A.D., Pandey, P., Feijóo, A., Yaseen, Z.M., Bokde, N.D.: Smart water technology for efficient water resource management: a review. Energies. (2020a)

Gupta, D., Nayak, P.K., Parashar, Y., Goel, V., Nath, V.: Internet of things for smart class rooms: a review. In: Lecture Notes in Electrical Engineering (2020b)
Guyot, G., Sherman, M.H., Walker, I.S.: Smart ventilation energy and indoor air quality performance in residential buildings: a review. Energy Build. (2018)

Harbi, Y., Aliouat, Z., Harous, S., Bentaleb, A., Refoufi, A.: A review of security in internet of things. Wirel. Pers. Commun. **108**, 325–344 (2019)

Herrmann, S., Schulte, F., Voß, S.: Increasing acceptance of free-floating car sharing systems using smart relocation strategies: a survey based study of car2go Hamburg. In: International Conference on Computational Logistics (2014)

Hortz, T.: The Smart State test: a critical review of the Smart State Strategy 2005-2015's Knowledge-Based Urban Development. Int. J. Knowl. Based Dev. (2016)

Igbinovia, F.O., Fandi, G., Mahmoud, R., Tlustý, J.: A review of electric vehicles emissions and its smart charging techniques influence on power distribution grid. J. Eng. Sci. Technol. Rev. (2016)

Imbar, R.V., Supangkat, S.H., Langi, A.Z.R.: Smart campus model: a literature review. In: 7th International Conference on ICT for Smart Society (2020)

Iskandaryan, D., Ramos, F., Trilles, S.: Air quality prediction in smart cities using machine learning technologies based on sensor data: a review. Appl. Sci. (Switzerland). (2020)

Issaka, Z., Li, H., Yue, J., Tang, P., Darko, R.O.: Water-smart sprinkler irrigation, prerequisite to climate change adaptation: a review. J. Water Clim. Change. (2018)

Jamwal, R., Jarman, H.K., Roseingrave, E., Douglas, J., Winkler, D.: Smart home and communication technology for people with disability: a scoping review. In: Disability and Rehabilitation: Assistive Technology (2020)

Jukan, A., Masip-Bruin, X., Amla, N.: Smart computing and sensing technologies for animal welfare: a systematic review. ACM Comput. Surv. (2017)

Kaur, P., Parashar, A.: A systematic literature review of blockchain technology for smart villages. In: Archives of Computational Methods in Engineering (2021)

Kominos, N., Philippou, E., Pitsillides, A.: Survey in smart grid and smart home security: issues, challenges and countermeasures. IEEE Commun. Surv. Tutor. (2014)

Kornyshova, E., Deneckere, R., Gressier-Soudan, E., Murray, J., Brinkkemper, S.: From smart life to smart life engineering: a systematic mapping study and research agenda. In: 15th International Baltic Conference on Digital Business and Intelligent Systems. Springer, Riga, Latvia (2022a)

Kornyshova, E., Deneckere, R., Sadouki, K., Gressier-Soudan, E., Brinkkemper, S.: Smart Life: review of the contemporary smart applications. In: International Conference on Research Challenges in Information Science, pp. 302–318 (2022b)

Kumari, M., Sharma, R., Sheetal, A.: A review on hybrid WSN-NGPON2 network for smart world. In: Advances in Intelligent Systems and Computing (2020)

Leaman, J., La, H.M.: A comprehensive review of smart wheelchairs: past, present, and future. In: IEEE Transactions on Human-Machine Systems (2017)

Lasek, A., Cercone, N., Saunders, J.: Smart restaurants: survey on customer demand and sales forecasting. In: Smart Cities and Homes, Key Enabling Technologies (2016)

Li, J., Yang, X., Sitzenfrei, R.: Rethinking the framework of smart water system: a review. Water (Switzerland). (2020)

Li, W., Zheng, T., Yang, Z., Li, M., Sun, C., Yang, X.: Classification and detection of insects from field images using deep learning for smart pest management: systematic review. Ecol. Inf. (2021)

Liao, H.T., Zhou, Z., Zhou, Y.: A systematic review of social media for intelligent human-computer interaction research: why smart social media is not enough. In: International Conference on Intelligent Human Computer Interaction (2021)

Lin, H., Bergmann, N.W.: IoT privacy and security challenges for smart home environments. Information. **7**(3), 44 (2016)

Liu, H., Ning, H., Mu, Q., Zheng, Y., Zeng, J., Yang, L.T., Huang, R., Ma, J.: A review of the smart world. In: Future Generation Computer Systems (2019)
Lutolf, R.: Smart home concept and the integration of energy meters into a home based system. In: 7th international conference metering apparatus and tariffs for electricity supply, pp. 277–278. (1992)

Mahmood, D., Latif, S., Anwar, A., Hussain, S.J., Jhanjhi, N.Z., Sama, N.U., Humayun, M.: Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: a review. Int. J. Adv. Appl. Sci. (2021)

Mahoor, M., Hosseini, Z.S., Khodaei, A., Paaso, A., Kushner, D.: State-of-the-art in smart streetlight systems: a review. In: IET Smart Cities. (2020)

Malekpour Koupaei, D., Song, T., Cetin, K.S., Im, J.: An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews. Build. Environ. (2020)

Mekala, M.S., Viswanathan, P.: A survey: smart agriculture IoT with cloud computing. In: International Conference on Microelectronic Devices, Circuits and Systems (2017)

Merrad, W., Rachedi, A., Busawon, K., Binns, R.: A survey on smart traffic network control and optimization. In: 1st International Conference on Multidisciplinary Engineering Design Optimization. (2016)

Moraitou, M., Pateli, A., Fotiou, S.: Smart health caring home: a systematic review of smart home care for elders and chronic disease patients. Adv. Exp. Med. Biol. (2017)

Navarro, E., Costa, N., Pereira, A.: A systematic review of IoT solutions for smart farming. Sensors (Switzerland). (2020)

Niccolai, L., Bassetto, M., Quarta, A.A., Mengali, G.: A review of Smart Dust architecture, dynamics, and mission applications. In: Progress in Aerospace Sciences (2019)

Nikolaeva, A., te Brömmelstroet, M., Raven, R., Ranson, J.: Smart cycling futures: charting a new terrain and moving towards a research agenda. J. Transp. Geogr. (2019)

Papagianneas, S.: Towards smarter and fairer justice? A review of the Chinese scholarship on building smart courts and automating justice. J. Curr. Chin. Aff. (2021)

Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering, vol. 17. (2008)

Rajšp, A., Fister, I.: A systematic literature review of intelligent data analysis methods for smart sport training. Appl. Sci. (Switzerland). (2020)

Rangel-Martinez, D., Nigam, K.D.P., Ricardez-Sandoval, L.A.: Machine learning on sustainable energy: a review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chem. Eng. Res. Des. (2021)

Reka, S.S., Dragicevic, T.: Future effectual role of energy delivery: a comprehensive review of Internet of Things and smart grid. Renew. Sust. Energ. Rev. (2018)

Rozmi, A.N.A., Nohuddin, P.P.N.E., Hadi, A.R.A., Bakar, M.I.A.: Identifying Small and medium enterprise smart entrepreneurship training framework components using thematic analysis and expert review. Int. J. Adv. Comput. Sci. Appl. (2021)

Sardi, J., Mithulananthan, N.: Community energy storage, a critical element in smart grid: a review of technology, prospect, challenges and opportunity. In: 4th International Conference on Engineering Technology and Technopreneuship. (2014)

Scorpio, M., Laffi, R., Masullo, M., Ciampi, G., Rosato, A., Maffei, L., Sibilio, S.: Virtual reality for smart urban lighting design: Review, applications and opportunities. Energies. (2020)

Severi, S., Sottile, F., Abreu, G., Pastrone, C., Spirito, M., Berens, F.: M2M technologies: enablers for a pervasive internet of things. In: European Conference on Networks and Communications. (2014).

Shaker, A.S.: A survey of smart buildings and homes using low-power wide-area network (LoRa WAN). In: 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. (2020)

Shastry, R., Ramana Murthy, B.V., Kishor Kumar Reddy, C., Anisha, P.R.: Automated lighting smart parking using internet of things. In: Intelligent Computing, Information and Control Systems, vol. 1039. Springer, Cham (2020)
Shuai, W., Maille, P., Pelov, A.: Charging electric vehicles in the smart city: a survey of economy-driven approaches. In: IEEE Transactions on Intelligent Transportation Systems (2016)

Singh, M., Ahmed, S.: IoT based smart water management systems: a systematic review. In: Materials Today: Proceedings. (2020)

Song, Y., Yu, F.R., Zhou, L., Yang, X., He, Z.: Applications of the internet of things (IoT) in smart logistics: a comprehensive survey. IEEE Internet Things J. (2021)

Strozzi, F., Colicchia, C., Creazza, A., Noè, C.: Literature review on the ‘smart factory’ concept using bibliometric tools. Int. J. Prod. Res. (2017)

Subashini, M.J., Sudarmani, R., Gobika, S., Varshini, R.: Development of smart flood monitoring and early warning system using weather forecasting data and wireless sensor networks – a review. In: 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (2021)

Sufyan, M., Rahim, N.A., Aman, M.M., Tan, C.K., Raihan, S.R.S.: Sizing and applications of battery energy storage technologies in smart grid system: a review. J. Renew. Sustain. Energy. (2019)

Tadili, J., Fasly, H.: Citizen participation in smart cities: a survey, pervasive health. In: Pervasive Computing Technologies for Healthcare (2019)

Tai-hoon, Kim, T., Ramos, C., Mohammed, S.: Smart city and IoT. Futur. Gener. Comput. Syst. 76, 159–162 (2017)

Thakur, N., Nagrath, P., Jain, R., Saini, D., Sharma, N., Hemanth, D.J.: Artificial intelligence techniques in smart cities surveillance using UAVs: a survey. In: Studies in Computational Intelligence (2021)

Thomson, S.B.: Qualitative research: validity. J. Adm. Gov. 6(1) (2011)

Tikhomirov, V., Dneprovskaya, N.: Development of strategy for smart University. In: Open Education Global International Conference, Banff, Canada, pp. 22–24 (2015)

Wang, D., Zhong, D., Souri, A.: Energy management solutions in the Internet of Things applications: technical analysis and new research directions. Cogn. Syst. Res. 67, 33–49 (2021)

Wang, H., Liu, Y., Laaksonen, A., Krook-Riekkola, A., Yang, Z., Lu, X., Ji, X.: Carbon recycling – An immense resource and key to a smart climate engineering: a survey of technologies, cost and impurity impact. Renew. Sust. Energ. Rev. (2020)

Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N.M., Nelson, L.E.: Helping CIOs understand smart city initiatives. Forres Res Growth. 17 (2009)

Wong, B.T.M., Li, K.C.: The benefits and challenges of smart learning: a literature review. In: International Symposium on Educational Technology (2021)

Xing, T.W., Hong, S., Jian, D., Lei, D., Han, J.H., Song, Y.T.: Survey of wind power and integration smart grid technologies. Adv. Mater. Res. (2013)

Yamane, S.: Deductively verifying embedded software in the era of artificial intelligence = machine learning + software science. In: 6th IEEE Global Conference on Consumer Electronics (2017)

Yan, H., Peng, G., Luo, Y., Liu, S.: Survey on smart home attack and defense methods. J. Cyber Secur. (2021)

Ye, B.H., Ye, H., Law, R.: Systematic review of smart tourism research. Sustainability (Switzerland). (2020)

Yin, H., Akmandor, A.O., Mosenia, A., Jha, N.K.: Smart healthcare. Foundations and Trends®. Electron. Des. Autom. 12(4), 401–466 (2018)

Young, M.S., Birrell, S.A., Stanton, N.A.: Safe driving in a green world: a review of driver performance benchmarks and technologies to support ‘smart’ driving. Appl. Ergon. (2011)

Yusuf, N., Hamza, A., Muhammad, R.S., Suleiman, M.A., Abubakar, Z.A.: Smart health internet of thing for continuous glucose monitoring: a survey. Int. J. Integr. Eng. (2020)

Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., Zhang, X.: Wearable Internet of Things enabled precision livestock farming in smart farms: a review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. J. Clean. Prod. (2021)
Zhao, Y., Rokhani, F.Z., Shariff Ghazali, S., Chew, B.H.: Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review protocol. *BMJ Open*. (2021)

Zisser, H., Robinson, L., Bevier, W., Dassau, E., Ellingsen, C., Doyle, F.J., Jovanovic, L.: Bolus calculator: a review of four “smart” insulin pumps. In: Diabetes Technology and Therapeutics (2008)

Zou, W., Jing, W., Chen, G., Lu, Y., Song, H.: A survey of big data analytics for smart forestry. In: *IEEE Access* (2019)

Zougmoré, R., Partey, S., Ouédraogo, M., Omitoyin, B., Thomas, T., Ayantunde, A., Ericksen, P., Said, M., Jalloh, A.: Toward climate-smart agriculture in West Africa: a review of climate change impacts, adaptation strategies and policy developments for the livestock, fishery and crop production sectors. In: Agriculture and Food Security (2016)

Zulfiqar, H., Haque, U., Mahfooz, H., Tariq, F., Khan, R.M.: A survey on smart parking systems in urban cities. In: Concurrency and Computation: Practice and Experience (2021)

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<figure><img src="image_29.png" /></figure>

**Chapter 3**
# Evolution of the Scientific Domain of Smart Life

Rébecca Deneckère, Elena Kornyshova, and Sjaak Brinkkemper

**Abstract** Research in “Smart” domains has proliferated over recent decades, branching into various subfields including Smart energy, Smart cities, and Smart homes and, more recently, Smart farming, Smart universities, and Smart tourism. Building on a bibliographic study of 2341 literature reviews and state-of-the-art papers, this chapter addresses the research question: *How have Smart applications evolved through time?* We show the growth of the Smart Life scientific domain reflecting a rising interest among researchers and society and provide an analysis of the increasing diversity of the research concepts in Smart Life. Finally, we present the evolution of four key Smart domains: Smart applications for enterprises, persons, environment, and society. Therefore, this chapter illustrates the surge in publications over the past decade underscoring the expanding relevance and impact of Smart Life in contemporary technological and societal contexts.

**Keywords** Smart Life · Evolution · Bibliographic study

**3.1 Introduction**

Research in the “Smart” domains expands continuously, with various subfields emerging over the past decades. We developed in the previous chapter the taxonomy of various Smart Life applications. We studied 2341 state-of-the-art and research

<footnote>R. Deneckère
Centre de Recherche en Informatique, Université Paris 1 Panthéon-Sorbonne, Paris, France
e-mail: rebecca.deneckere@univ-paris1.fr</footnote>

<footnote>E. Kornyshova (✉)
Centre d'Etudes et de Recherche en Informatique et Communications, Conservatoire National des
Arts et Métiers, Paris, France
e-mail: elena.kornyshova@cnam.fr</footnote>

<footnote>S. Brinkkemper
Department of Information and Computing Sciences, Utrecht University, Utrecht, the Netherlands
e-mail: s.brinkkemper@uu.nl</footnote>
agenda papers on Smart Life.<fnref n="1" /> We detailed different Smart applications, such as Smart energy (Loganathan et al. 2018), Smart cities (Chamee 2020), Smart homes (Lutolf 1992), Smart farming or Smart agriculture (Hidayat et al. 2020), Smart university (Al-Shoqran and Shorman 2021), Smart tourism (Mehraliyev et al. 2020), and so on.

This work led to the specification of a taxonomy on Smart applications. In this chapter, we investigate their evolution and distribution over time. Thus, we aim at answering the second research question elaborated in the research methodology section of the previous chapter *RQ2: How have Smart applications evolved through time?*

The growth of the scientific domain of Smart Life with a detailed analysis of the related research concepts is studied in Sect. 3.2. Section 3.3 proposes a deeper look on the evolution of the four domains of Smart Life: Smart applications for enterprises, for persons, for environment, and for society. Section 3.4 explains the segmentation of the Smart applications type into each of these four domains. We conclude in Sect. 3.5.

## 3.2 Growth of the Scientific Domain of Smart Life

During a preliminary examination of research papers containing the strict term of “Smart Life,” we observed an increasing number of research articles interested on this domain (Kornyshova et al. 2022a). As shown in Fig. 3.1, the number of papers on Smart Life just has increased in the last decade. A nearly consistent rise has been detected since 2011, with a stable progression in the number of papers on Smart Life, particularly since 2016.

However, the number of papers on Smart Life was restricted; thus, we undertook a larger study to better understand the evolution of this field (the study of 2431 research articles). In the following, we present the evolution of Smart Life-related concepts since the appearance of the first states of the art in 1986 issue from this study. We start by the concepts for the whole period, and then we present them by sub-periods.

**Main concepts used in Smart Life** The main concepts discussed in the Smart Life paper corpus during the whole period are shown in Fig. 3.2. To construct and visualize the network, we used the VOSviewer tool (https://www.vosviewer.com/) with all the meta-data of the 2341 papers, ranging from 1986 to 2022. The obtained network highlights several important clusters, like *technology-oriented* (Internet of things, big data, sensors, cloud computing, and blockchain), *goal-oriented*

<footnote n="1">¹ The complete list of these papers is available at http://cri-dist.univ-paris1.fr/rcis22/RCIS2022_Appendix%20A%20-%20references%20list.pdf</footnote>
<figure><img src="image_30.png" /><figcaption>**Fig. 3.1** Growth of the number of papers on Smart Life from 2011 to 2020</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>**Fig. 3.2** Concepts used in the meta-data of the studied papers for the entire period</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>**Fig. 3.3** Concepts in the meta-data of the studied papers from 1986 to 2005</figcaption></figure>

(security, energy-efficiency, privacy, and sustainability), and *application-oriented* (Smart grids/meter, Smart home, and Smart city).

To understand better the evolution of Smart Life concepts, we decomposed the paper corpus into three time periods: 1986–2005 (20 years), 2006–2014 (9 years), and 2015–2022 (8 years).

**The Main Concepts Used in Smart Life from 1986 to 2005 (34 Papers)**

Smart Life was at its very beginning in those days and applied in some domains, which will become more and more important later on (Fig. 3.3). For instance, “energy consumption” problematics were at the root of the appearance of Smart energy applications. Similarly, Smart healthcare applications began with “aging” people care. The concepts of “data security,” “information security,” “protection,” and “cryptography” were already the core of the area related to data collected by the sensors and the complexity to manage them correctly and confidentially. Technology-oriented concepts are also present, with “microscopy,” “Smart cards,” and “read on memory.”
<figure><img src="image_33.png" /><figcaption>**Fig. 3.4** Concepts in the meta-data of the studied papers from 2006 to 2014</figcaption></figure>

## The Main Concepts Used in Smart Life from 2006 to 2014 (310 Papers)

As shown in Fig. 3.4, this period of time highlights the explosion of the Smart energy applications, essentially dedicated to the “Smart grid” ones but also to other kind of applications, like “energy management,” “energy efficiency,” and so on. “Sensors” or “Smart materials” are also representing a good set of concepts in the figure. “Artificial intelligence” makes its appearance. “Smart home” emerges in this period. “Security,” “privacy,” and “optimization” are representing all the goal-oriented concepts. It is remarkable that some concepts about sustainability appear, like “Smart growth.” However, this is not related to the other Smart Life concepts yet.

## The Main Concepts Used in Smart Life from 2015 to 2022 (1997 Papers)

Figure 3.5 illustrates the network of papers for this period. Eighty-five percent of our paper corpus were published in this last period of time, which shows the incredible expansion of the Smart Life domain on the last decade. A lot of new Smart applications appear, like “Smart agriculture,” “Smart governance,” and “Smart building.” However, even if we find a lot of these new concepts, the main ones appearing in this network are “Smart grid” (same as in the preceding period) and “Internet of Things,” which shows the importance of technology in this area. Related to IoT, we find all the “Smart devices” concepts, like “actuator,” “Arduino,”
<figure><img src="image_34.png" /><figcaption>Fig. 3.5 Concepts in the meta-data of the studied papers from 2015 to 2022</figcaption></figure>

or “zigbee.” We also find other concepts like “Industry 4.0,” “machine learning,” and “Internet,” which contribute to the number of technology-oriented concepts. Next to that, novel considerations about sustainability appear, like “sustainable city” and “climate change,” which are now completely related to the other concepts of the Smart Life domain.

## 3.3 Evolution of Smart Domains

The evolution of four Smart domains is illustrated in Table 3.1 covering the period from 1986 to 2022 (we should mention that the year 2022 is not complete as the study was done during 2022). The initial 21 years are aggregated into a single
<table><caption>**Table 3.1** Smart domains evolution (from Kornyshova et al. 2022b)</caption><thead><tr><th>Smart Life domains</th><th>1986-2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th><th>Total</th></tr></thead><tbody><tr><td>Persons</td><td>3</td><td>4</td><td>6</td><td>9</td><td>3</td><td>6</td><td>9</td><td>13</td><td>9</td><td>10</td><td>25</td><td>21</td><td>45</td><td>64</td><td>93</td><td>84</td><td>4</td><td>408</td></tr><tr><td>Smart healthcare</td><td>3</td><td>3</td><td>5</td><td>4</td><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>5</td><td>7</td><td>11</td><td>13</td><td>31</td><td>39</td><td>47</td><td>2</td><td>190</td></tr><tr><td>Smart home (Smart living)</td><td></td><td>1</td><td>1</td><td>4</td><td>1</td><td>2</td><td>5</td><td>8</td><td>3</td><td>4</td><td>18</td><td>9</td><td>27</td><td>29</td><td>45</td><td>33</td><td>1</td><td>191</td></tr><tr><td>Smart welfare</td><td></td><td></td><td>1</td><td></td><td></td><td>1</td><td></td><td></td><td></td><td>1</td><td></td><td>1</td><td>5</td><td>4</td><td>9</td><td>4</td><td>1</td><td>27</td></tr><tr><td>Society</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>4</td><td>6</td><td>13</td><td>23</td><td>37</td><td>50</td><td>107</td><td>89</td><td>100</td><td>5</td><td>440</td><td></td><td></td></tr><tr><td>Smart city</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>3</td><td>6</td><td>12</td><td>16</td><td>32</td><td>42</td><td>96</td><td>80</td><td>87</td><td>5</td><td>384</td><td></td><td></td></tr><tr><td>Smart</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>1</td><td></td><td>1</td><td></td><td></td><td></td><td></td><td>3</td></tr><tr><td>Smart court</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>Smart education</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>5</td><td>4</td><td>6</td><td>9</td><td>7</td><td>11</td><td></td><td>43</td></tr><tr><td>Smart island</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td>1</td></tr><tr><td>Smart policing</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td>1</td></tr><tr><td>Smart social media</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>1</td><td>3</td></tr><tr><td>Smart state</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>Smart village</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>2</td><td></td><td></td><td>1</td><td></td><td>3</td></tr><tr><td>Environment</td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>2</td><td>2</td><td>6</td><td></td><td>16</td></tr><tr><td>Smart disaster management</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>3</td><td></td><td></td><td>5</td></tr><tr><td>Smart earth</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>Smart natural resource management</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3</td><td></td><td></td><td></td><td></td><td>1</td><td>3</td><td></td><td>9</td></tr><tr><td>Smart space exploration</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td>1</td></tr><tr><td>Enterprises</td><td>1</td><td></td><td>2</td><td>11</td><td>15</td><td>40</td><td>39</td><td>43</td><td>56</td><td>98</td><td>89</td><td>130</td><td>179</td><td>199</td><td>231</td><td>9</td><td>1142</td></tr><tr><td>Smart agriculture</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>2</td><td>1</td><td>2</td><td>9</td><td>8</td><td>12</td><td>26</td><td>41</td><td>2</td><td>104</td></tr><tr><td>Smart business management</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td>2</td><td>5</td><td>10</td><td>15</td><td>24</td><td>3</td><td>60</td></tr><tr><td>Smart industry</td><td>7</td><td>1</td><td>2</td><td>11</td><td>14</td><td>38</td><td>37</td><td>37</td><td>37</td><td>50</td><td>91</td><td>72</td><td>108</td><td>137</td><td>137</td><td>145</td><td>4</td><td>891</td></tr><tr><td>Smart service industry</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td><td>1</td><td></td><td>5</td><td>5</td><td>3</td><td></td><td></td><td>16</td></tr><tr><td>Smart transportation</td><td></td><td></td><td></td><td></td><td>1</td><td>2</td><td></td><td></td><td>3</td><td>5</td><td>4</td><td>5</td><td>9</td><td>15</td><td>16</td><td>18</td><td></td><td>78</td></tr><tr><td>Total by year</td><td>1</td><td>6</td><td>6</td><td>13</td><td>14</td><td>22</td><td>51</td><td>56</td><td>59</td><td>82</td><td>146</td><td>147</td><td>226</td><td>352</td><td>383</td><td>421</td><td>18</td><td>2013</td></tr></tbody></table>
"Big Five"

<figure><img src="image_35.png" /><figcaption>**Fig. 3.6 Evolution over time of the five largest Smart applications**</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>**Fig. 3.7 Smart domains distribution over time**</figcaption></figure>

column due to the considerably low number of publications during this period. We complete this chronological perspective with two graphical representations, the first one highlighting the evolution of the five largest Smart applications (see Fig. 3.6) and the second one illustrating the distribution of Smart domains (see Fig. 3.7).

We can note that the earliest reviews in the literature focus on Smart applications for enterprises dating back before 2006. Subsequently, the literature shows an increasing interest in two other domains, Smart applications for persons and for society, particularly during the 2000s. More recently, Smart applications for environment came out more prominently.
We can identify the growth of the five most studied Smart applications as shown in Fig. 3.6. For instance, Smart industry and Smart city are the most popular and represented in 891 and 384 state-of-the-art or research agenda papers, respectively, in this 36-year period of time.

The evolution of the four Smart domains is shown in Fig. 3.7 (excluding papers of 2022 as the year was not complete for the study). Within the domain of Smart applications for enterprises (1142 papers), Smart industry dominates (891 papers), with a substantial focus on Smart energy-related topics, followed by Smart agriculture (104 papers), Smart transportation (78 papers), and Smart business management (60 papers).

Smart home and Smart healthcare are equally represented in Smart applications for persons (191 and 190 papers, respectively, over 408), with a smaller portion dedicated to Smart welfare.

Smart applications for environment are relatively limited, with only 16 papers; half of them focused on Smart natural resource management (nine papers).

Smart city dominates the Smart applications for society (384 papers out of 440), followed by Smart education (43 papers).

On the whole, the first works to appear on Smart applications were of the enterprises domain and still correspond to more than half the works published in the Smart Life field. Then we find the Smart applications for persons and society. The last domain, Smart applications for environment, began to appear in 2014.

## 3.4 Segmentation of the Smart Domains

Besides the evolution of the four domains over time, it is interesting to know the segmentation of the papers in each of the domains. Figure 3.8 shows this segmentation for each domain.

*Smart applications for Enterprises* are divided into five main types, as shown in Fig. 3.8a. The most dominant one is Smart industry (78% of the corpus), one of the Big Five (See Fig. 3.6) that comprises the Smart energy and Smart grid fields, which we already identified as really common Smart applications. We find then Smart agriculture, Smart transportation, and Smart business management. Finally, Smart service industry appear with only 1% of the corpus papers.

*Smart applications for Persons* are separated into three main types (Fig. 3.8b). Smart home and Smart healthcare applications take the best part of the share with 47% and 46% each. Smart welfare applications area come as a challenger in this domain with new Smart applications like Smart clothing or Smart sports.

*Smart applications for Environment* can be considered as belonging to four main types, as shown in Fig. 3.8c. Smart natural resource management applications are represented in more than half of the paper corpus (56%), with the increase of the environment problematics awareness. Smart disaster management applications share (32%) also shows a growing interest with increasingly frequent environmental disasters, such as repeated floods or fires.
<figure><img src="image_37.png" /><figcaption>**Fig. 3.8** Distribution of Smart applications (a) for enterprises, (b) for persons, (c) for environment, and (d) for society</figcaption></figure>

*Smart applications for Society* (see Fig. 3.8d) is mainly concerned with Smart home applications, as it was one of the first Smart application to appear in the domain (87.27%). 9.77% of our paper corpus represent Smart healthcare applications. There are also a set of seven other Smart application types which appear on a marginal way (compared to these two first types).

**3.5 Conclusion**

In this chapter, we have looked at the evolution of the Smart Life area over time, both by closely examining the terms and concepts used in the corpus articles and through the lens of the four domains identified in the previous chapter: Smart applications for enterprises, for persons, for environment, and for society. This bibliographic study demonstrates that the research domain concerning Smart Life involves an increasingly diverse yet strongly interconnected range of fields. The huge expansion in the number of publications in the last decade indicates a growing interest among researchers in these issues, which also reflects a growing interest of our society in Smart Life.
## References

Al-Shoqran, M., Shorman, S.: A review on smart universities and artificial intelligence. In: Studies in Computational Intelligence. (2021)

Chamee, Y.: Historicizing the smart cities: genealogy as a method of critique for smart urbanism. In: Telematics and Informatics, vol. 55. (2020)

Hidayat, T., Mahardiko, R., Franky, S., Tigor, D.: Method of systematic literature review for internet of things in ZigBee smart agriculture. In: 8th International Conference on Information and Communication Technology. (2020)

Kornyshova, E., Deneckere, R., Gressier-Soudan, E., Murray, J., Brinkkemper, S.: From smart life to smart life engineering: a systematic mapping study and research agenda. In: In: 15th International Baltic Conference on Digital Business and Intelligent Systems. Springer, Riga, Latvia (2022a)

Kornyshova, E., Deneckere, R., Sadouki, K., Gressier-Soudan, E., Brinkkemper, S.: Smart life: review of the contemporary smart applications. In: International Conference on Research Challenges in Information Science, pp. 302–318. (2022b)

Loganathan, N., Mayurappriyan, P.S., Lakshmi, K.: Smart energy management systems: a literature review. In: MATEC Web of Conferences. (2018)

Lutolf, R.: Smart home concept and the integration of energy meters into a home based system. In: 7th International Conference Metering Apparatus and Tariffs for Electricity Supply, pp. 277–278. (1992)

Mehraliyev, F., Chan, I., Cheng, C., Choi, Y., Koseoglu, M.A., Law, R.: A state-of-the-art review of smart tourism research. J. Travel Tour. Mark. (2020)

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
<figure><img src="image_38.png" /></figure>

# Chapter 4
## A Review of Social, Behavioral, and Ethical Considerations for Smart Life Engineering

**John Murray**

**Abstract** Smart Life (SL) systems do not exist in a technological vacuum; they are embedded in a social ecosystem and have rich and complex interactions with their surroundings. It is important to recognize that their design and implementation will have an impact on the individuals and communities around them. This chapter examines the social, behavioral, and ethical issues to be considered in a holistic systems approach to SL engineering, to help practitioners and stakeholders to design the strategies needed for successful SL deployments. It addresses these topics from several disciplinary viewpoints and provides a comprehensive set of references for those requiring further detailed information.

**Keywords** Socio-technical systems · Engineering ethics · Human-centric design

### 4.1 Introduction

In the process of examining the key tenets of Smart Life (SL) engineering—or indeed any comprehensive technological infrastructure—it is important to recognize that these technologies do not exist in a vacuum but are embedded in the host environment or social ecosystem. They influence, and are influenced by, contextual settings that involve autonomous elements and independent actors whose behaviors and interactions with the infrastructure are often unpredictable and difficult to model. At the center of these so-called externalities are of course humans, both as individuals and as groups or communities.

The purpose of this chapter is to explore the various forms of human and societal interactions within and among the major elements of this ecosystem, to identify some of the core requirements for designing and deploying SL systems in this context, and to help structure the plans and policies for administering and guiding them successfully.

<footnote>J. Murray (✉)
San José State University, California, USA
e-mail: jxm@acm.org</footnote>
The next section describes a *system-level approach to SL Engineering*, which emerges from a heritage that includes cyber-physical systems, ubiquitous computing, and socio-technical systems, among others. That is followed by a discussion of the *ethical design requirements* and the techniques and principles that have been successfully implemented by various practitioners.

The succeeding section examines several key issues related to *system control*, *safety*, and *security* of SL systems. These concerns include the vulnerability of systems to cyberattacks and tampering, the importance of strong leadership at the policy-making level, as well as the typically inadequate sensitivity to data quality and provenance.

The next area to consider is the topic of *accessibility to Smart Life applications*. While civil rights policies and legislation are in place to mitigate discrimination against those with disabilities in the physical environment, there is still considerable work to be done to provide equivalent accessibility in the digital domain.

This is followed by a discussion of the *ethical concerns with Smart military systems*, which includes some notes on the relevant parallels and technical applicability of particular features to the safety and security of SL systems in a civilian environment.

The topic of *engineering education and ethics awareness* is then addressed, with some suggestions and proposals for pedagogical strategy, curriculum content, and professional certifications.

The final area to be covered is a brief summary of the regulatory landscape for *oversight of SL human research ethics*. This section outlines the context and challenges for ethics oversight committees when social studies research is part of their purview.

The chapter is rounded out with some overall conclusions, followed by author acknowledgments and references.

## 4.2 System-Level Approach to Smart Life Engineering

An important consideration in the exploration of Smart Life engineering is the development of a comprehensive systems approach to technologies being deployed. Such technologies do not exist in a vacuum but are embedded in the host environment or social ecosystem. They influence, and are influenced by, a contextual setting that may be more or less controlled, more or less chaotic, and more or less hostile or benign.

In this respect, SL engineering draws upon an extensive heritage of several long-standing fields of engineering research and practice, as well as human behavioral studies. These include early work on cybernetics and society (Wiener 1954), human augmentation (Engelbart 1962), and socio-technical systems (Emery & Trist 1960), together with ubiquitous computing or ubicomp (Weiser 1993), cyber-physical systems (Talcott 2008), and cyber-physical-social systems (Reine et al. 2021). Not
to mention the many recent deployments of AI-based technologies across numerous sectors, especially those involving extensive edge computing capabilities.

In order to characterize the domain of SL engineering, we can use the extensive work in these disciplines to inspire the strategies needed to interpret and understand these systems. But what are the core differences between these various heritage fields and the smart life of today? In pursuit of these answers, we can identify several key connected features of interest.

*Digital vs Physical* One important element relates to the direct physical manifestations of “smartness.” For example, the development of tangible computing enables hybrid interfaces to be designed, which take advantage of the fact that digital and physical resources have different and complementary properties (Fraser et al. 2018).

Ubicomp, often referred to as pervasive computing (Satyanarayanan 2001), traditionally centered upon the conceptual separation of digital and physical materials, but innovative manufacturing techniques have enabled the deployment of information processing capabilities in new embodiments.

*Form vs. Function* As new types of functional materials gain prominence, the differences in the *form* of a product, which is traditionally physical, and its *function*, which is traditionally digital, become increasingly difficult to separate across the physical-digital divide. This change has important effects on users’ interactions with such technologies, since the system outputs to users, and its sensing of their inputs, are predominantly in the physical domain.

As an example, the production of nano-fabrics using stimuli-responsive polymers enabled the development of smart clothing and sensing systems, which has implications for entirely new design principles that emphasize combining physical manifestations of form and function (Dang and Zhao 2021, Haque 2019).

This work derives from an extensive research background in the use of electroactive polymers for human performance augmentation, in particular in support of prosthetic devices such as artificial muscles (Chiba et al. 2006). As with 3-D printing, the traditional consumer product design themes of economy, agency, creativity, and sustainability become significantly disrupted by innovative fabrication techniques.

*Participatory Design* For a long time, numerous public programs involving socio-technical systems have emphasized the need for stakeholder participation from the early stages of the proposed system design (Schuler & Namioka 1993). This approach has its origins in 1970s Scandinavia, where user involvement and cooperative design became particularly prevalent. This was especially the case in situations where automation and computer technologies were being introduced in many workplaces (Ehn & Kyng 1987).

The expansion of this type of collaborative design to broader communities offers some useful insights for SL engineering. For example, the Array of Things (AoT) project in Chicago, Illinois, incorporates several key features concerning community involvement (Catlett et al. 2020). At the core of the AoT project was the
deployment of an urban-scale intelligent measurement system, which centered upon
the application of software-defined sensors to understand urban activity patterns and
support social and behavioral science investigations. The advantage of software-
defined sensors is that it's not necessary to define all possible measurements prior
to building and installing devices.

*Environmental Observations* While common sensor networks are relatively straightforward to build and scale, closely involving the community draws attention to the need for both traditional sensor data acquisition and new types of measurements—*observations*—that require extensive edge computing capabilities. Processing images within the devices enhances the opportunities to maintain stricter privacy norms, since data can be interpreted and acted upon without the need to rely on telemetry streaming to central servers for remote analysis. As was the case in with the AoT project, much attention needs to be paid to privacy and ethics considerations.

In essence, these types of projects can deploy general-purpose sensing platforms that may be tuned to the needs of individual communities. This provided considerable flexibility to the project organizers, enabling them to dynamically address and accommodate the particular issues and interests of specific districts. For example, residents of some AoT neighborhoods expressed concerns about air quality and/or noise, whereas other communities were in need of better data about traffic-related activities.

The Urban Wellbeing Project is another innovative use of sensing network data to gain insights into the relationships between environmental conditions and human health (Johnson & Kanjo 2023). In this case, a smartphone app accessed local air quality and noise sensor data in Nottingham, UK, and combined it with dynamic surveys of the user's momentary mental well-being. The research demonstrated the community health effects of busy, polluted, and green spaces, as well as impacts of poor air quality and loud ambient noise on perceptions of negative well-being.

## 4.3 Ethical Design Requirements

The proliferation of open-source software and auto-generated programming has introduced broad opportunities for non-proprietary, non-classically engineered software development. The result is a critical need for research to support rigorous assessment of such software to minimize vulnerabilities and protect against malicious alteration upon its deployment. As the ACM technical policy advisors have noted:

> Innovations in the tools and techniques for rigorously defining design requirements for software, hardware, and data – and for assuring that those requirements are correctly implemented – have significant potential for enhancing the security and trustworthiness of critical components of the digital ecosystem. (ACM 2023)
This assertion raises the question of whether we’re really talking just about
engineering anymore. The salient answer is no, it’s more about design. Clearly,
among the design requirements is the need to develop more awareness of ethical
implications during the design phase of SL systems, rather than trying to incorporate
those concerns later as an afterthought.

In the context of SL environments, we are concerned with dynamic networks
of technology agents, some of which may have some form of autonomy, possibly
because there are active software socio-cognitive agents purposely participating
within the SL system. Alternatively, it is possible that the underlying infrastructure
incorporates some type of agency, perhaps by actively producing unexpected user
outcomes, or because third parties may deliberately or unintentionally interact with
the system.

It is therefore desirable to find a design strategy that can be used to attend to such ethical issues when designing these types of environments. A potential candidate is value-sensitive design (VSD) (Friedman et al. 2017), which provides a theoretically grounded approach to the design of technology that accounts for human values in a principled and systematic manner throughout the process. VSD incorporates a suite of design methods focused upon identifying particular values in technology, which serve such purposes as stakeholder identification and legitimation, value representation and elicitation, and values analysis.

However, a salient shortcoming of VSD is that unintended principles can often be
immersed, typically unconsciously, in technological artifacts during a system design
and development phases. The process lacks a specific critical reflection activity to
help expose such unconscious inscription of values. To overcome this limitation,
principles of *Conscientious Design* (CD) (Noriega et al. 2021) have been proposed;
they are underpinned by ethical and social values and extend the approach used by
the VSD research framework.

In particular, CD responds to three key properties of conscientiousness; thor-
oughness, mindfulness, and responsibility. These are summarized in Table 4.1.

CD also draws inspiration from other well-established human-centric design
practices, including Alexander's "habitable spaces" in physical architecture
(Alexander 1979) and Deming's evolutionary approach to total quality management
(TQM), with the goal of achieving product excellence over time (Deming 1982).

In support of this integration of physical and social elements, CD describes
IT-enabled systems that (1) afford collective activities involving individuals, both
human or artificial, (2) reason about the ethical aspects of social interaction, and
(3) can act within a stable shared social space. This is essentially equivalent to
socio-technical systems and is also an appropriate characterization of Smart Life
systems. Conscientious design provides a useful means for mapping ethical design
principles for building trustworthy autonomous and intelligent systems that conform
to broader international initiatives for ethical alignment, such as those from the
European Union and the IEEE (EU HLEG 2019, IEEE 2019).
<table><caption>**Table 4.1** The key principles of conscientious design</caption><thead><tr><td>Property</td><td>Definition</td><td>Techniques entailed</td></tr></thead><tbody><tr><td>Thoroughness</td><td>The system is provably technically correct; all the requirements have been properly identified and faithfully implemented</td><td>Appropriate formal methods, tools, accurate system modelling, simulations, digital twins, and proper use of verification tools</td></tr><tr><td>Mindfulness</td><td>Supra-functional features provide users with awareness of the characteristics of the system and the possibility of selecting a satisfactory tailoring to individual needs or preferences</td><td>Incorporated techniques should include ergonomics, governance, coherence of purpose and means, identification of side effects, no hidden agency, and the avoidance of unnecessary affordances</td></tr><tr><td>Responsibility</td><td>Leadership is empowered to honor commitments, and responsiveness to stake-holders' legitimate interests is required. This responsibility applies both to individual users and participants, as well as the obligations involving society in general</td><td>Required features include scrutiny, transparency, and accountability, alongside a proper support of privacy, a "right to forget"; proper handling of identity and ownership, liabilities, and proper risk allocation; and support of values like justice, fairness, and trustworthiness</td></tr></tbody></table>

## 4.4 System Control, Safety, and Cybersecurity

All Smart Life systems interact in some manner with the world and its human occupants, and any autonomous SL system would need to be able to reason using some sort of underlying world model, in order to make coherent and sensible decisions. Undertaking this type of analysis involves sophisticated logical processes, such as causality and counterfactuals, which form the groundwork for ethical reasoning and explainability (Pearl & Mackenzie 2018). Such capabilities would typically be extensions beyond what is needed to support the primary functions of the SL system.

For example, a fully autonomous road vehicle needs coherent decision-making guidelines to arbitrate among conflicting collision warnings. The uncoordinated approach to regulating autonomous vehicles, along with the legal wrangling after injury accidents, demonstrates the significant shortcomings in this area (Thadani 2023). The process for “optimizing” the outcomes of an impending serious accident may involve moral and ethical dilemmas that SL systems are ill-equipped to handle (Kulicki et al. 2018).

Here are some key safety and risk issues of concern for Smart Life systems:

*Ad Hoc Loosely Coupled Networks:* The ecosystem of most SL systems spans multiple organizations and diverse stakeholder entities and groups. As a result, a common outcome of SL technical integration projects is a predominantly ad hoc, loosely coupled network of many autonomous subsystems of varying quality and provenance. In this context, little attention is often paid to issues of system-wide maintenance, reliability, and safety.
For a long time, *failure modes and effects analysis* (FMEA) strategies provide approaches to addressing these issues (Stamatis 2019, Paté-Cornell, 2002). FMEA is a common method of interpreting the behavior of a complex, centrally managed system and often places particular emphasis on stakeholder consultation and impacts. However, it is less used with highly distributed, locally managed federated systems, like those envisioned in Smart Life engineering.

*Humans-in-the-Loop:* Consumer electronics product developers largely ignore the human users and operators in the loop, when framing safety and cybersecurity solutions in real-world situations. For example, Kukkala et al. (2022) provide a comprehensive technical roadmap for strengthening cybersecurity in autonomous transportation systems, yet the work makes no meaningful reference to “users” or “humans” at all.

This type of omission motivates the need for a human-centric approach to cybersecurity, in particular by shifting focus from “humans as a problem” to “humans as a solution.” An example of such a strategy is the *security and privacy preserving* model, as proposed by Rohan et al. (2022), which illustrates how a human-centric approach is initiated, explains its important components, and demonstrates how security and privacy can be preserved with a human focus. Essentially, user interactions with IoT systems are examined using a four-layer framework that addresses discovery, inference, choice, and communication. See Table 4.2.

*Cyberattack Portals:* The potential for Internet of Things (IoT) devices and other SL network elements being used as portals for cyberattacks is significant. There is a clear need for better resilience under adversarial conditions, yet the topic often receives little or no attention in the IoT engineering community. Given the extent to which modern civil and military infrastructures and economies rely on digital technologies and embedded systems, it is likely that any future great-power war will probably start with a comprehensive and coordinated set of cyberstrikes. The pervasive use of generic, ill-secured IoT devices in many cyber-physical systems

<table><caption>**Table 4.2** Security and privacy preserving framework, structuring the relationship that humans have with IoT devices and services</caption><thead><tr><td>Layer</td><td>Factors of concern</td></tr></thead><tbody><tr><td>Discovery</td><td>How does a person discover the presence of IoT devices and services in their surroundings? What data is being collected about them, and what privacy properties are applicable in the vicinity?</td></tr><tr><td>Inference</td><td>What inferences can a person make from the information provided in the discovery layer? How do they interpret the effects of this data collection on their personal security and privacy?</td></tr><tr><td>Choice</td><td>How can the person be helped with regard to making decisions about their preferences? What is the process for expressing these preferences?</td></tr><tr><td>Communication</td><td>What is the nature of the communication between the IoT device or service? What notifications or alerts are appropriate, and how are they expressed?</td></tr></tbody></table>
render them vulnerable to attacks and has led to numerous calls for mandating better resilience and improved security measures.

*Data Provenance:* Cybersecurity professionals have pointed out that most regulatory strategy to date, in the domain of embedded systems, has focused on the safety and security of critical physical infrastructure, with little or no attention paid to the reliability and provenance of the incoming data being used to support that those systems (ACM 2023).

For example, conventional digital signatures are not routinely used for data authentication or provenance. It is important to ensure that decision support systems are sensitive to the characteristics of the data they process, for example, to check whether input data was compromised or of low quality. In addition, for most systems, it's not clear how to provide resiliency when a data source has been found to be compromised.

*Cultural and Legal Issues:* There are extensive cultural and legal issues related to the control and access to SL data products and related surveillance information (Zuboff 2019). It is clear that perceptions of personal data ownership and rights differ substantially between the United States, the European Union, and the People's Republic of China. On the other hand, SL systems can empower communities that are underserved or ignored by governing authorities, enabling them to take grassroots action to improve their living conditions. For example, the Ushahidi (2023) open-source platform provides crowdsourcing and mapping resources to communities seeking gather and report real-time information about safety problems and hazards in their neighborhoods.

*Chief Information Security Officer (CISO)*: The leadership role of the CSIO or equivalent is pivotal in overseeing cybersecurity policies across an entire municipal administration or network of agencies. The task of coordinating such strategies must be managed at the highest echelons of the organization, yet the CISO typically lacks the authority and resources to mandate cybersecurity requirements within and among the various departments and agencies (SIW 2023). Even in commercial businesses where the ultimate responsibility lies with the corporate board of directors, the CISO's ability to influence decisions is usually quite modest, in comparison to the clout of the chief financial officer or the vice presidents of the individual operational units. The development and maintenance of loosely coupled networks of departmental champions across the organization is often the best approach to addressing this challenge (Metcalf et al. 2019).

In summary, the key role of ethical sensitivity in treating the human element when defining and managing safety and security for SL engineering cannot be overstated.
## 4.5 Accessibility of Smart Life Applications

In the area of accessibility, it is important to ensure that Smart Life design and engineering professionals are provided with adequate resources to implement accessible systems. The domain offers particularly valuable opportunities for innovative accessibility design, because of its close integration with the built environment.

There are already social and legislative responsibilities in place for preventing discrimination and exclusion in the physical world on the basis of impairment or disability. For example, discrimination occurs when a person with limited mobility cannot access transportation systems, government offices, or business premises.

Numerous regulations are already in place to mitigate the “socially created disadvantage and marginalization” experienced when someone with accessibility challenges is treated differently and less favorably due to their impairment (Lawson & Beckett 2021). Civil rights and equality laws and policies, including the US Americans with Disabilities Act, prohibit discrimination on the basis of disability (ADA 2010).

Taking this situation in the built environment as a starting point, it is important to examine the gaps in designing the digital world that lead to inaccessible technology and disability discrimination (Horton 2022).

The four key capability areas for attention are the visual, hearing, cognitive, and motor domains. Although there are still no formal regulatory frameworks in place yet, a well-established suite of solid building blocks is already available to support accessibility professionalism across all four of these areas in the digital environment.

For example, in 2012, the International Organization for Standardization published ISO/IEC 40500 that identifies the specifications and requirements for supporting accessibility in digital resources. This standard, which is based upon Web Content Accessibility Guidelines (Kirkpatrick et al. 2018), provides the measures used to assess compliance with nondiscrimination laws and policies. Unfortunately, these standards are not yet widely recognized among technology professionals.

In the healthcare arena, a useful taxonomy and architecture for accessibility to IoT devices has been developed (Lopes 2020). The architecture encompasses the perspectives of healthcare stakeholders and other generic requirements. There is good scope for extending these types of resources for applicability to broader areas of SL engineering.

There are a number of outreach initiatives focused on advancing accessibility awareness in education and professional development. These include curriculum development, mentoring, and advocacy, to address industry demands for accessibility skills. For example, TeachAccess has developed a strategic plan that addresses the digital accessibility skills gap (TeachAccess 2021). Similarly, Teaching Accessibility in the Digital Skillset (TeachingAccessibility 2022) is undertaking research on developing effective accessibility teaching pedagogy and related curriculum resources that will be applicable across multiple technology disciplines.
In addition, several influential organizations provide professional certification programs on accessibility topics in digital and built environments. These include the International Association of Accessibility Professionals (2023) and the US Department of Homeland Security (2023). However, these types of specialist certification do not address the need for core competency in accessibility across technology design and engineering professions.

## 4.6 Ethical Concerns with Smart Military Systems

Descriptions of technical equipment and systems that are designed for law enforcement and military applications introduce additional nuances of the term *smart*. In some cases, the term is used primarily for marketing and promotional purposes, rather than any inherent sophisticated quality of smartness. For example, a *Smart bullet* is essentially a miniaturized version of a precision-guided missile that relies on a laser or other directing system to identify its target (Barrett 1997). Similarly, the term *Smart gun* may be used to describe a weapon equipped with user sensing technology, such as fingerprint recognition or RFID tagging, to ensure that only an authorized individual can activate it.

On the other hand, equipment intended for the projection of disabling or lethal force may also be described as smart, for example, when they include functionality to reduce or eliminate accidental fratricide or “friendly fire.” Similarly, remotely guided weapon systems or robotic drones often incorporate capabilities, such as target recognition features, so that they can operate autonomously in particular combat situations.

Smart military assets typically operate as networked clusters or linked devices that communicate their status and coordinate their actions. For example, the ability of smart surveillance systems to disambiguate multiple sightings of the same enemy resource helps reduce battlefield confusion, and the use of sensor fusion improves the accuracy of identifying intended targets. In this regard, the concept of “smartness” may therefore be thought of as existing across the network, as in other cyber-physical systems, rather than residing in individual self-managing units (Zhang 2014).

In the case of fully autonomous weapon systems (AWS), some key ethical considerations center upon aspirations to apply situation awareness judgement principles in the absence of timely human-in-the-loop capabilities. There has been widespread discussion concerning the extent to which AI-based systems could ever reach a level of sophistication that made them better than humans at making moral judgments (Purves et al. 2015, Lorca Albareda 2024). On the other hand, it is possible that an AWS could be trained to avoid engaging putative noncombatants, only to use proportional, less-than-lethal force, and never to attack retreating or defenseless combatants. From an ethical standpoint, the core question is what data sources would be used to train such systems and, more to the point, who gets to decide which datasets would be used (Cogley 2017). The same ethical reasoning
applies equally to non-military SL systems, where autonomous decision-making
may impact the well-being of any stakeholders and bystanders alike.

The potential for cyberstrikes on military systems is as significant, or perhaps
greater, than other civilian SL systems. But beyond these issues, there are also
particular concerns about ensuring that smart military equipment can be neutralized
or disabled if it is in danger of falling into enemy hands. A further refinement of
this requirement is the ability to reconfigure autonomous networked equipment
and modify functionality to reflect changing political priorities and accommodate
adjusting alliances among partner forces.

On a broader front, there is also a requirement to reconfigure or repurpose tech-
nical systems when military command structures need to be rearranged. Dynamic
changes to the *unit task organization* become necessary when individual elements or
subgroups become incapacitated or have to be reassigned or merged for operational
reasons (Donnelly 2011). The ability to easily modify the relationships among semi-
autonomous devices, and to adjust corresponding roles and responsibilities among
them, has clear advantages for managing many Smart Life systems, well beyond the
strictly military command and control networks.

Finally, there are significant impediments to introduce innovative new technolo-
gies into military systems, because of the burdensome and bureaucratic procurement
processes involved (Clifton & Copeland 2008). This has led to calls for adopting a
broader, stakeholder-centric *design studio* approach to seek out novel concepts and
to develop and simulate smart systems using processes that are ten times faster than
the current acquisition procedures (Schmidt 2023).

In conclusion, although some unique requirements are associated with designing
and deploying smart military systems, there are many operational features and
challenges that have parallels in civilian SL systems. We would do well to
understand the techniques and solutions used, to learn from them, and to improve
the safety and security of all smart systems.

## 4.7 Engineering Education and Ethics Awareness

Topics in professional ethics have long been an educational staple in fields involving
proficiency certification or licensing, including medicine, law, and business. In addi-
tion, as noted earlier, there are research ethics awareness and oversight requirements
for many studies involving human subjects, especially those relating to the approval
of medical devices or clinical trials.

In the broader technology domain, it is almost 70 years since Jacques Ellul (1954)
presented a penetrating analysis of how *la technique* transitions from a support
system for humankind into an ongoing deployment of dominating systems that are
solely intended to meet their own ends. Likewise, the technology ethics community
has been actively promoting the moral aspects of automation for quite some time
(Sullins 2011).
Yet until recently, the general topic of professional ethics was marginalized or ignored completely in most standard engineering and computer science curriculums. Although model syllabuses and quality course materials are widely available (De Witte 2022), they have rarely been mandated as part of the core requirements for completion of degree programs in engineering and computer science.

In discussing the lecture series *Ethics for Nerds* at Saarland University, Baum and Stern (2022) observe that students of computer science and related subjects should receive at least a basic ethical education to be able to do justice to their ever-growing responsibilities and duties. They describe experiences with and best practices of their teaching approach and offer advice on how to design a successful ethics course as part of a computer science study program.

There is a need for increased attention by product developers of the human context in complex systems. A key design strategy is to emphasize *human-machine teaming* rather than basic user interaction modeling (Ozkaya 2020). Using effective teaming models implies having a shared awareness of the task, team, and operational situation, as well as a common interpretation of the intended end goals to be achieved.

It is important to recognize the behavioral science of software engineering from a professional education point of view. An important feature of this strategy is to ensure that expectations of trust, ethics, and privacy should be prioritized from the start of any system design process. This should not be difficult since once the human context is introduced into the curriculum, topics like ethics and trust almost “slip in” through the backdoor, as it were.

A useful ethically oriented technique for characterizing the level of community involvement in technology projects is provided by a research team from INRIA in France (Anderson and Fort, 2022). Specifically, the approach focuses upon three dimensions of human participation in such projects—inclusion, guidance, and persistence (IGP). This strategy seeks to replace the much-overused term *human-in-the-loop*, originally coined more than 65 years ago (Bennett 1957), which has been continuously redefined to suit whatever fashionable command-and-control decision-making policies and regulations need to be promoted.

The INRIA authors note that there is a logical contradiction inherent in the original intent behind the terminology that hinders an appropriate ethics-centric framework. Inspired by the work of Bruno Latour (2008), they observe that just as the provenance of the technological object can be forgotten, the incorporation of a human as a simple component of the loop also trivializes the role that the individual plays within the community that is creating and operating the technology.

The IGP approach seeks to move away from an attitude that assume human oversight from *outside* of the technology toward direct participation *in* the creation of the technology. It lays the foundation for a more meaningful ethical engagement in the project, where community members are dynamically engaged in the technology producing process. Mere oversight by humans is thus transformed into active *guidance* as an involved community, which transforms the negative perspective of seeking to remove humans when possible.
<table><caption>**Table 4.3** IGP template for accessing levels of community involvement in technology projects</caption><thead><tr><th>IGP term</th><th>Level</th><th>Definition</th></tr></thead><tbody><tr><td rowspan="3">Inclusion</td><td>Replaceable</td><td>Technology community views a human as replaceable by any other human</td></tr><tr><td>Experienced</td><td>Technology community views human as experienced in multiple activities</td></tr><tr><td>Unique</td><td>Technology community develops technology around a human's unique skills and life context</td></tr><tr><td rowspan="3">Guidance</td><td>Tester</td><td>Human's use of system is merely "registered" (passive)</td></tr><tr><td>Trainer</td><td>Human's suggestions regarding system are implemented</td></tr><tr><td>Designer</td><td>Human designs complex parts of the system with others</td></tr><tr><td rowspan="3">Persistence</td><td>Brief</td><td>Human initiates or contributes to an abstracted phase of a technology community</td></tr><tr><td>Sustained</td><td>Human contributes to the actions of a technology community up to the completion of the technology community's goals</td></tr><tr><td>Evolving</td><td>Human contributes to the process of a technology community as it overlaps and weaves into different technology communities beyond itself</td></tr></tbody></table>

Table 4.3 summarizes a candidate set of IGP ranges that could be used for this purpose.

Steen (2023) provides a useful model curriculum for participatory and iterative reflection, inquiry, and deliberation on ethical issues in product development, both to better understand the problem and to envision and create solutions. The model promotes design process elements drawn from responsible innovation (Stilgoe et al. 2013) that involves people with diverse backgrounds and types of expertise, as well as individuals representing groups of citizens or societal organizations.

Another practical framework for discussing ethical concerns with smart systems is provided by Singh et al. (2023). Specifically, they represent smart cities as sociotechnical systems and model stakeholder values along multiple ethical dimensions using moral foundations theory (Graham et al. 2013).

Finally, in terms of resources for raising general public awareness of the ethical considerations for Smart Life systems, the European Union has published a set of educator guidelines on the use of AI and data in teaching (European Commission 2022). These include issues related to human agency, fairness, human dignity, and justified choice.

## 4.8 Oversight of Smart Life Research Ethics

The importance of ethical oversight and reviews is well understood in many areas of human participant research, particularly in fields like pharmaceutical studies or medical device design. In 1979, following a series of high-profile research studies in the United States that involved unethical treatment of individual participants, the
Belmont Report established the ethical principles and guidelines for human subject research (US DEHEW 1979). That became the foundation for a comprehensive set of guidelines and standards, which include special provisions to protect vulnerable populations like institutionalized individuals, children, prisoners, etc. Corresponding regulations have been implemented internationally and enable life sciences research to be undertaken ethically and efficiently across multiple jurisdictions. As an example, the process for undertaking international clinical trials for products like vaccines are well-known and widely understood.

However, in social science research, processes for ethical oversight and guidance are much less well coordinated. In the United States, institutional review board (IRB) approval is needed for human research supported by governments or private foundations. Nevertheless, when it comes to collaboration between academic research institutions and for-profit corporations, IRBs may simply step away from their oversight responsibilities for individual sensitivity and vigilance regarding personal privacy issues.

For example, in 2012, the Cornell University IRB considered Facebook's Emotional Contagion Study to be beyond its purview, although the feelings of over 600,000 users were deliberately manipulated without their consent by the social network company (Kramer et al. 2014).

Furthermore, the extent to which ethics oversight committees in non-US jurisdictions consider social studies research as part of their purview is very inconsistent (Murray et al. 2012, Murray & Fairfield 2014), and in any case, there is a persisting need for ethics committees to have better understanding of the technical risks, especially when it comes to managing data privacy and security (Huh-Yoo et al. 2021). The Menlo Report, which was published in 2012, was intended to address these shortcomings, but progress on implementation has been slow (Bailey et al. 2012). More recently, the Ada Lovelace Institute has published materials for promoting technical expertise and support for research ethics committees (Strait 2023).

There have also been ongoing concerns in the computer science community about who should be responsible for ensuring proper ethical oversight of human-related research. Conference program committees have frequently mandated that any ethics issues are properly documented and addressed as an acceptance condition for presentation and publication of papers (Allman 2008). However, by the time the study is complete and the findings are ready for publication, it is typically much too late to address any individual or group harms that were caused.

Numerous professional associations and consulting organizations in the technology field have voluntary codes of ethical conduct that apply to their members, but they frequently lack adequate powers of for meaningful enforcement. Likewise, when it comes to social studies research undertaken by commercial enterprises and private corporations, there are no regulatory requirements that correspond to those for IRBs. In the case of technology product-related research undertaken by business ventures, some firms have voluntarily implemented ethics oversight procedures (see, e.g., Jackman & Kanerva 2016). The underlying problem for the private sector is that such research ethics regulations and guidelines are not well structured to