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# Towards (An Aggregated)
Territorial Digital Twin

## From Smart-village to Smart-territory
via the Territorial System of
Digital Twin

Cédrick Béler,<fnref n="1" /> Gregory Zacharewicz,<fnref n="2" />,\*

Paul-Antoine Bisgambiglia,<fnref n="3" /> Bastien Poggi,<fnref n="4" />

Florent Poux<fnref n="5" /> and Antoine-Santoni Thierry⁶

## 1. Introduction

The concept of a Smart City has gained significant attention in recent years as urban populations continue to grow and the need for efficient and sustainable urban management becomes increasingly pressing. A Smart City is defined as a city that utilizes technology to improve the quality of life for its citizens, enhance sustainability, and optimize the use of resources (Boubekri, 2017). An intelligent city or smart city, therefore, not

<footnote n="1">¹ <cedrick.beler@enit.fr>; https://orcid.org/0000-0002-0439-724X</footnote>

<footnote n="2">² https://orcid.org/0000-0001-7726-1725</footnote>

<footnote n="3">³ <bisgambiglia pa@univ-corse.fr>; https://orcid.org/0000-0003-3026-2258</footnote>

<footnote n="4">⁴ <poggi_b@univ-corse.fr>; https://orcid.org/0000-0002-3253-6868
<florent@learngeodata.eu>; https://orcid.org/0000-0001-6368-4399</footnote>

<footnote n="5">⁵ <antoine-santoni_t@univ-corse.fr>; https://orcid.org/0000-0002-6645-9311.</footnote>

<footnote>\* Corresponding author: Gregory.Zacharewicz@mines-ales.fr</footnote>
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only places the citizen at the center of its development strategy, thanks to its information technology (IT) infrastructure for data-driven management but also for the implementation of digital services, often interactive or participative (information systems, communications, storage, security, responsiveness, sensor networks, etc.). Its constant objective would be supervising its natural, energy, human, and economic resources. This new approach has been tested for several years by major European cities such as Amsterdam, London, Stockholm, Barcelona, Paris, Manchester, Padua, and Santander (Zubizarreta et al., 2015). However, the heterogeneity of trajectories, political visions, and situations raises questions about the implementation of the smart city. Is it reality, marketing, or utopia? At a time of unprecedented climatic upheaval on a human scale and in a digital divide between urban and rural areas, is it not essential to ask the question of the territories on the outskirts of large cities and rural areas?

The 'smartification' of cities can create a digital divide between geographical areas, especially rural ones. In recent years, research on smart cities has attracted the attention of academics and practitioners. Smart, in this case, refers to initiatives by government officials, citizens, and other stakeholders, primarily using technology, to make cities more livable and places more 'intelligent', which refers to the continuum within the local government (Gil-Garcia et al., 2015). One key aspect of a Smart City is the creation and use of a digital twin.

On the industrial domain, emerged the concept of Digital Twin which is a virtual representation of a physical asset or system, such as a building, infrastructure, or city (Gao, 2018). It is created by collecting and analyzing data from various sources, such as sensors and internet of things (IoT) devices, and using this data to create a detailed, 3D models of the asset or system. The Digital Twin can then be used for a variety of purposes, such as simulation, prediction, and optimization (Jiang et al., 2019).

Coupling those two concepts, a digital twin for a smart city is a virtual replica of a city that can be used to simulate and analyze the city's physical and functional characteristics. It can be used for a wide range of purposes, such as urban planning, resource management, and emergency response. The digital twin is created by collecting and integrating data from a variety of sources, such as sensor networks, aerial imagery, and demographic data. The data is then used to create a detailed model of the city, which can be used to simulate different scenarios and analyze the impact of different decisions.

The objective of this chapter is to focus on Smart villages and smart territories (Komorowski and Stanny, 2020). The contribution presents the current development in relation to smart villages to provide direction to get a larger scale territory that goes beyond the frontier of a given city of village. The first part is dealing with recalls on experiences and feedback from the proof of concept in Cozzano. Then territorial services in large rural
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areas that consider medical and air quality cases are presented. Thereafter,
the major required concepts of a territorial System of digital twins including
human, system of systems, and 3D environments are dealt with. Finally,
there are some conclusions.

## 2. Experience Feedback

### 2.1 Feedback from the proof of concept in Cozzano (Smart Paesi – Corsica)

*The program Smart Village–Smart Paesi*

The program Smart Village–Smart Paesi: Emergence of Smart Territories is a scientific action of the Università di Corsica Pasquale Paoli/CNRS, launched 2017 in partnership with the IT company SITEC and EDF island energy systems within a small rural village of Cozzano (300 inhabitants) in central Corsica. Until July 2021, financially supported by the European Fund FEDER and the Collectivité de Corse, the project was to propose a proof of concept of a Smart Village at the scale of a commune by relying on its assets and its strategy.

The village of Cozzano is in the center of Corsica, surrounded by mountains at an altitude of 650 meters and forests of pine, chestnut, and oak. The community has worked for many years to maintain services and activities but has also embarked on a renewable energy strategy. The village of Cozzano aims to become a positive energy zone that produces more energy than it consumes through biomass boilers, river hydroelectric plants, and solar panels (with a self-consumption system) on municipal buildings.

During the construction of the project in 2015, without a validated definition of the smart village concept, four basic elements were proposed to design it: sustainable development (in all its components), IT/digital infrastructure, education/citizenship, and agricultural activities. The smart village system is based on an information system with five actions: collect, store, analyze, predict, and visualize to ensure data-driven management.

The Smart Paesi program builds a mindset for information systems (IS) data management along four axes:

1. Environmental and infrastructure monitoring via wireless sensor networks;

2. Information system integrating real-time monitoring and prediction tools for decision support;

3. System optimization (energy, water, public buildings, lighting, services) and connectivity/data visualization;

4. Development of services and places for actors/experimenters (farms, livestock, fire brigades, etc., follow-up) and public awareness.
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The Smart Paese is based on the collection of data on the environment of a village thanks to a network of sensors or connected objects using LoRA/LoRaWAN technology: weather, air, water and soil quality, energy consumption, and geolocation. LoRa technology was chosen for the telemetry activity because of its range of wavelengths allowing communications over several kilometers, its ability to overcome obstacles, and the low energy consumption of the connected objects using this technology. All this information is stored in a flexible and scalable database for analysis and prediction. Indeed, the data is used by a prediction-optimization algorithm called Smart Entity (Antoine-Santoni et al., 2019). This algorithmic structure must predict the evolution of the systems observed in the village and propose different levels of restitution by visualization (open data, dashboard for the municipality, information for the population) to optimize the sustainable development strategy.

Among these initiatives, a saffron grower's organic farm has been connected to a network of LoRaWAN wireless sensors to collect various data (soil quality, irrigation, weather). A field visualization dashboard allows him to monitor and manage his crop and identify the optimal environmental conditions for the operation. Similarly, collaborative work has been initiated between the saffron farmer and researchers to determine the saffron's flowering time, using the Smart Entity prediction tool. Pig and goat farmers use geolocation systems, integrating their farms into the Smart Village process (Antoine-Santoni and Poggi, 2022).

### 2.1.1 Information system deployed and related digital services

The Smart Paesi program builds its thinking around data-based management in an information system (IS) along 4 main lines:

* An infrastructure for monitoring the environment and the buildings of the municipality through a network of wireless sensors; the long-range communication technology LoRa/LoRaWAN has been chosen;

* An IS integrating real-time monitoring with predictive tools for decision support within the framework of ambient intelligence;

* System optimization (energy, water, public buildings, lighting, and services) and data interfacing/visualization;

* Development of services for experimental actors (monitoring of farms, livestock, firemen, etc.) and for public awareness.

Implementing these primary objectives made it possible to validate a proof of concept (POC) within a Living Lab integrating an effective and efficient IS. In line with the philosophy of the Smart Village definition, many parallel projects have been carried out in the municipality in a logic of third places, awareness-raising actions, and integration of citizens in the issue of sustainable development and reduction of the digital bill.
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The Centre d'Immersion et d'Innovation Numérique par la Technologie Ubiquitaire (CIINTU) is a third-party space that was designed in parallel with the implementation of the Smart Village. The need to create a place to store equipment and accommodate researchers appeared early in the scientific program. In this context, a building was renovated, and a new place was made available to researchers and inhabitants. This building has become the third place materializing the Smart Village but also a co-working space for people passing through the village who do not have an internet connection. It is equipped with a broadband connection, workspace, printer, various computer equipment and a small FabLab (3D printer, digital embroidery machine, robots, etc.). It currently hosts many activities: training sessions for various organizations, computer training for the population, workshops with the school, co-working, and researchers from the University of Corsica.

The Town Hall has a comprehensive website with a wealth of regularly updated information; it has a permanent communication system for social networks (Facebook, Instagram). It has a notice board on the front of the town hall providing information to residents and visitors: practical information, promotional films, events, and data on the Smart Village. The digital advisor, recruited in 2021 following a national strategy to deploy mediation agents in the most isolated areas, is located at the CIINTU. He assists the population of Haut-Taravo who may be suffering from illiteracy, whatever their profile, in getting to grips with digital tools and understanding their uses. To do this, it organizes group workshops as well as individualized support in various areas: teaching users to use the various computer equipment, making them independent in surfing the Internet, training them in writing and sending emails, word processing, and managing digital content (creation, storage, sharing), enabling them to fully master the use of a smartphone, in particular, to install and use applications, and giving them the keys to understanding digital vocabulary. In addition, it also has a role to play in facilitating specific uses such as social networks and digital communication tools, searching for a job on the Internet and applying online, carrying out administrative procedures online, knowing how to make purchases from the Internet, and understanding how digital school tools work, for users who must follow their children's schooling.

His role in digital mediation is central in the municipality. Moreover, the person recruited is a former member of the Smart Paesi program, which makes it much easier to explain the program.

The school is very much part of the Smart village philosophy. The two classrooms in the commune are well equipped with digital equipment, including multi-touch boards, digital tablets, and headsets, which allow many activities to be carried out with the children. This has been achieved by responding to calls for innovative projects from the CARDIE unit. The CARDIE unit, in collaboration with the INSPE of Corsica, proposes
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to accompany the schools and establishments of our academy in a
development approach based on innovation, supported by teachers and
teacher-researchers of the Università di Corsica.

In Cozzano, the school created a school newspaper. The children worked as little journalists for a year, making it possible to create three newspapers in the village, broadcasting scientific information on sustainable development (e.g., sorting or water management). The following year, the newspaper was transformed into a web radio. This year, robotics workshops are being offered to help children discover algorithms and programming.

Citizens are actors in the feedback process, thanks to the activity of
'mediators'. Indeed, students from the University of Corsica, holders
of a master's degree in engineering, come regularly for several weeks of
immersion to exchange with the population and collect the population's
feelings to imagine the evolution of the Smart Paesi. Indeed, many subjects
are shared with the population around the scientific project and digital,
health, and energy savings.

2.1.2 Conclusions and learnings on the smart-Paese project

The development of smart cities must be community-driven, even before
investments in technological solutions are made. Citizens are an invaluable
source of information for understanding problems and needs regarding the
quality and adequacy of urban services provided (Antoine-Santoni et al.,
2022). The success of a smart city is determined mainly by the degree of
involvement of citizens in the creation of the smart city itself. Therefore,
dialogues between citizens and policymakers, urban planners, and
managers are fundamental for planning and action strategies, especially
for collecting feedback to adjust and improve current and future policies.
Urban services refer to services to/on the territory and governance services.

Today, local authorities focus on designing, implementing, and
managing solutions for cities characterized by shared information and a
networked approach. However, it is necessary to question these models
of intelligent territories in the face of climate disruption and more local
crises, such as the digital or health divide. To enable this dialogue between
users and decision-makers for efficient decision-making, IT and digital
technology represent a significant opportunity.

3. Territorial Services in Large Rural Areas: Medical and Air Quality Cases

Whether city or village is concerned, there are boundaries to the developed
smart-systems that do not easily allow sharing information (or assets)
between these systems. In this section, two research works are presented to
demonstrate the need to overcome the frontier of a commune. The first one
• [HAL SHS](https://www.halshs.fr/)

• [HAL Thèses](https://www.halshs.fr/)

• [MédiHAL](https://www.medihal.fr/)

CCSD

• [CCSD](https://www.ccsd.fr/)

• [Episciences](https://www.episciences.org/)

• [Sciencesconf](https://www.sciencesconf.fr/)

CCSD
is about the necessary coordination to improve the medical desert situation while the second research is about collecting data from sensors on a large area so that municipalities can take decision regarding air quality.

## 3.1 Improving rural medical desert through ICT by better healthcare pathways

### 3.1.1 Medical desert statement

Rural areas are often more sparsely populated than urban areas, and are generally characterized by a lower population density, a smaller number of services including healthcare, and a more agricultural or natural resource-based economy. Some rural areas are relatively isolated and may be difficult to access, while others are more connected to urban centers and may have more developed infrastructure. In consequence, a rural area is a geographic region that is not located in or near a city or suburb frequently experiences healthcare resources shortage. So, as part of smart village features, making access to healthcare resources easier thanks to digital support is an important objective.

In more detail, in rural areas, it exists medical desert that is an area that lacks access to healthcare services. This can occur for a variety of reasons, such as a shortage of healthcare providers or geographical isolation. Medical deserts can have serious consequences for the people who live in them, as they may not have access to necessary medical care or preventive services. In some cases, medical deserts can lead to higher rates of illness and mortality, as well as increased healthcare costs due to the need for more expensive forms of care (such as emergency department visits or hospitalizations) for conditions that could have been treated more efficiently and effectively in a primary care setting.

### 3.1.2 Solutions

To address medical deserts and increase access to healthcare services in underserved areas, there are several solutions. Some of these strategies include:

1. Increasing the number of healthcare providers in the area: This can be done by developing more attractivity for recruiting and retaining more doctors, nurses, and other healthcare professionals to work in the area, or by using telemedicine and other technology to connect patients with healthcare providers remotely.

2. Improving transportation: Many people living in medical deserts may have difficulty getting to healthcare providers due to a lack of transportation options. Providing transportation assistance (such as
buses or vans) or improving public transportation can help to make it easier for people to access healthcare services.

3. Building or renovating healthcare facilities: Building or renovating healthcare facilities in underserved areas can help to improve access to care. Creating telemedicine spots can facilitate access to care as well. This can be especially important in rural areas, where the nearest healthcare facility may be a significant distance away.

4. Increasing community outreach and education: Providing education and outreach to the community can help to raise awareness about the importance of preventive care and encourage people to seek medical treatment when needed.

To deal with points 1, 2, and 3, it can be required to evaluate the costs and benefits for the population. Modeling and simulation can help with that. Authors in (Starkiene, 2013) described the maldistribution of human resources for health as a worldwide phenomenon that may appear in different dimensions. Shortages and imbalances in the distribution of the health workforce are social and political problems that, along with the socio-economic inequality and reduce the access of the population to the health services (Oliveira et al., 2017). This issue is more significant in areas with higher proportions of low-income and minority residents, such as rural areas, tend to suffer most from lower physician supply. The study (Sbayou et al., 2019) has chosen regions where there is a physicians' shortage and then they exposed and approach that combines Business Process Modeling and Notation (BPMN) with DEVS (Developers) for managing healthcare environment in French regions that suffer from physicians' imbalances issue. It is conceivable that smart villages can provide data to build such models and simulations.

Based on the data collected on the ground through the smart village digital twin, a model of the territory with services and population needs can be set. Then some healthcare processes or pathways can be described informally by text description or by semi-formal languages (Eshuis et al., 2010) according to the needs of the patients. A patient pathway process is generally defined as a sequence of events that uses patient status as inputs to produce outputs. Besides, a business process is an activity or set of activities that will accomplish a specific goal. It is also considered as a sequence of performed steps that drive information to produce and/or provides services. The business process needs to be managed and controlled to drive the calls to healthcare resources if available; it attests the need to use a business process management (BPM) methodology.

In this context, the use of BPM in healthcare sector can be a solution to anticipate population needs by simulation and then ensure coordination at run time between healthcare stakeholders. Among different languages to model BPM processes, one of the most useful one is BPMN. However,
one issue that can be faced by healthcare organizations while modeling with BPMN is that BPMN gives a static representation of the system, and as healthcare environment is becoming more dynamic and volatile, and follows more complex processes, there is a need to combine BPMN with an agent based model in terms of ensuring resource allocation, in the other hand, managing the availability of resources in healthcare sector is a very challenging problem with very little research attention.

### 3.1.3 Dordogne case study

In the works of (Sbayou et al., 2019), the Dordogne County (fr. Department) located in southwest of France was chosen and considered as a representative case of “medical desert” zone among the counties in Nouvelle Aquitaine region where the low population, as with most difficulties in accessing to health services. Figure 16.1 presents the map of this rural department. This map has been generated with QGIS thanks to INSEE French demographical and healthcare resources data from the yellow pages. It was coupled with a simulation platform to instantiate simulation agents (Zeigler, 2019). It can be observed in Fig. 16.1 that a patient located in the far north of the department may have access to a medical doctor (Fig. 16.1, arrow 1) in its neighborhood. Nevertheless, in the frame of a medical pathway that includes appointments with specialists' doctors (Fig. 16.1, arrow 3) and radio (Fig. 16.1, arrow 2) the distance is more important, and the waiting time can be impacted due to the limited number of resources. When a great part of this territory has a low level of population and almost no specialist healthcare resources, it leads to patient pathways that are not performant.

<figure><img src="image_1.png" /><figcaption>Fig. 16.1 Patient Pathway steps in Dordogne Case study.</figcaption></figure>
The study of (Sbayou et al., 2019) has reported a modeling and simulation approach that can be applied in the healthcare sector of population needs in a rural region, it can help decision support of decision-makers to install new healthcare services on one side and citizens on the other side as self-decision-maker on the building of their medical pathways. The idea presented in the study was to demonstrate the interest of coupling a healthcare patient pathway workflow modelled with BPMN with different healthcare resources organized in social networks described with DEVS based models as described in Fig. 16.2. Such an approach can be proposed to study a territory in terms of sufficient or insufficient resources/services available in a specific area.

Figure 16.2 depicts a modeling and simulation architecture and the social networks that can be used to get the information about the healthcare service required. The easiness/difficulty can depend on the physical location of the patient (first dimension), on the patient connection to healthcare services through the general practitioner (second dimension), and on the personal connection to people around the patient including family and friends (third dimension), both can provide support in searching for and reaching healthcare services. Then the architecture sets the patient in the different dimensions (layers) to attempt to identify and reach the appropriate resource. At the end, after having identified the resources, a BPMN process is used to follow the steps of the required medical pathway.

<figure><img src="image_2.png" /><figcaption>Fig. 16.2 Modeling and Simulation Platform.</figcaption></figure>
The idea in this study of the Dordogne territory was to face the problems regarding barriers to healthcare access and to anticipate solutions thanks to simulation. Results were interesting but mostly generated from estimated data. It can be assumed that thanks to the data provided directly from population inputs and updates, the identification of a problem and the anticipation of new healthcare solutions by providing new resources can be improved. Nevertheless, to reach the goals some barriers will have to be overcome. Real and fresh data, confidential data management, public services description, and integration in the database.

Finally, it can be integrated as one feature of a future Smart village digital twin. It can be interesting as well to apply this architecture in other sectors (transportation, education networks, employment...) to make it functional for any service-oriented needs.

### 3..1.4 Foresight conclusions and learnings

As lessons learned, there is a clear need to have a global and privacy respectful architecture to collect data and orchestrate processes for medical services. There is initiative in the domain including the French National Health Data System (SNDS) that represents a considerable advance in analyzing and improving the health of the population and provide database for analyses. Nevertheless, this database is more dedicated for a posteriori analysis rather than real-time decision support.

In addition, even if data are securely anonymized and managed at the local level, the information cannot stay only inside one city, it must go beyond the frontier of a commune, especially in rural areas to gather information and propose better support to build a patient pathway optimized according to the profile and location of the patient.

It leads at the end to the need of an Interterritorial continuum where the local data will have to interoperate with other data from other digital twins in the neighborhood.

### 3.1.5 Air quality monitoring in the Pyrenees

Air pollution is a major societal issue with significant health, social, environmental, and economic consequences, causing millions of deaths per year and loss of healthy life years. Numerous studies have been conducted and today regulations have determined exposure thresholds for different air pollutants (PM, VOCs, NOx, etc.) (World Health Organization, 2021). However, it is still quite difficult to know the exact composition of the atmosphere, especially due to lack of measuring points, particularly in rural areas. In France, ATMO, the association in charge of monitoring air quality, has only 720 measurement stations (fixed and mobile) to cover more than 550,000 square kilometers. The fixed stations mainly cover large cities, which are more prone to air quality issues. For example, in the Hautes-Pyrénées (4,464 km2), there are only two permanent ATMO stations located in Tarbes
and Lourdes, but no mountain area has an official measuring point. The principle of air quality prediction, a bit like weather forecast, relies on the injection of the measure of these stations into predictive models, but it is difficult for them to be precise in singular topologies (mountainous area in particular). In addition, these models are not sophisticated enough to detect or find the origin of local pollution phenomena (e.g., controlled burning done at more than 50 km of the stations). At best, some induced effects can be perceived. To address this problem of insufficient measurement resolution, there are public and inexpensive capture solutions. It is even possible to build low-cost stations using microsensors (Poupry et al., 2022).

### 3.1.6 BOLDAIR project

The BOLDAIR project started in 2019 and was financed by the French region Occitanie and the CCPVG inter-municipality (Communauté de Communes Pyrénées Vallées des Gaves). CCPVG is a medium to high mountain area, very touristy, including several ski resorts and a large part of which falls within the protected area of the Pyrenees National Park. Argelès-Gazost, its administrative seat, is also an important transit point since it is at the crossroads of three valleys. The CCPVG, aware of the importance of good air quality, has voluntarily committed to a Territorial Climate-Air-Energy Plan (PCAET) to give the inhabitants the opportunity to reflect on the future of their territory and to question the sustainability of well-being in the valleys. Several challenges have been established and the place of air quality has been defined as a priority. A second challenge identified in the context of this PCAET concerns governance, including the issue of monitoring and observation of pollutant emissions. In this rural context, the main emissions considered are those from the agricultural sector, the transport sector (especially during periods of high tourist affluence), biomass combustion, distinguishing outdoor air quality (controlled burning, methanization, etc.) and indoor air quality (inefficient wood heating equipment).

The goal of the BOLDAIR project is to enable municipalities to gain a better understanding of the air quality in their areas, which are often not well-instrumented, and to communicate this information to the public. Additionally, it provides decision-makers with tools to implement and evaluate measures to improve air quality. These tools will enable us to detect episodes of pollution, identify their causes, and potentially find and store solutions based on the given context. The Laboratoire Génie de Production (LGP) of Tarbes was responsible for the development of the BOLDAIR project. The objective was to create a decision-making system that would allow elected officials and citizens to monitor the levels of different pollutants and take appropriate action. The resulting architecture, illustrated in Fig. 16.3, consists primarily of an observation and decision-making system.
<figure><img src="image_3.png" /><figcaption>Fig. 16.3 General architecture of the BOLDAIR project.</figcaption></figure>

The observation system involves the monitoring of various physical pollutants (PM2.5, PM10, etc.) using a network of Low Cost Measurement Stations (LCMS) (Poupry et al., 2022). To achieve multiple measuring points at a reasonable cost, the solution is based on low-cost sensors (LCS). To address the limitations of LCS (precision, reliability, etc.), the architecture employs triple redundancy. The measurement station aggregates measurements from three sub-stations called Smart-Sensors (SmS that are based on a microcontroller with WiFi connection. These three SmS are connected to a micro-server based on a Raspberry Pi Zero (node A on Fig. 16.4), forming an autonomous measuring station that can communicate with the decisional system. The station also has local memory to store measuring points and perform some data processing. One of these processing capabilities is to provide a reliable measure from the three

<figure><img src="image_4.png" /><figcaption>Fig. 16.4 Low-Cost Measurement Stations (LCMS) and Smart-Sensors (SmS).</figcaption></figure>
captures. The station is also capable of self-analysis to detect any internal component failures.

The decision system provides a web-based tool to elected officials, giving them access to measurements from the LCMS, alerts if thresholds are exceeded, and instructions to follow in case of detected pollution episodes. The web application integrates open-source cartography capabilities (Open-Street-Map and IGN). The underlying architecture that aggregates spatio-temporal information from the LCMS is a network of nodes with a recursive aggregation strategy, enabling spatial scaling on a territory. Each LCMS corresponds to a specific place (latitude, longitude) from a municipality. An inter-communality may have interest in the data from several villages, connecting to the aggregating nodes of each of these villages. Recursively, a region or country may want to process the information from that village. The proposed architecture does not force a bottom-up inclusion, and every intermediary node can share its data to any other nodes if desired.

This aggregation or ascendant characteristics of the proposed architecture is synthetized in the following picture. So 'Observation' and 'Decision' are materialized with three types of nodes (Fig. 16.5). LCMS nodes are shown in gray. Green nodes enable recursive aggregation, receiving and sending spatio-temporal measurements to and from other nodes, and storing the information. Blue nodes, or HMI (Human Machine Interface) nodes are like green nodes but include user applications and interfaces. These nodes provide the necessary applications, algorithms, and processing capabilities for users, particularly for decision-makers (e.g., elected officials responsible for monitoring air quality).

This logic of aggregation has been the foundation of the concept of scaling spatially to cover large areas. If the data stream is properly managed and controlled (by respective owners in the physical world of the digital counterpart), it is believed that this type of architecture could be highly resilient, particularly if the network connection is possible without the Internet via direct connection between nearby locations. This is not an alternative to the current Internet, but rather a complementary approach. In

<figure><img src="image_5.png" /><figcaption>Fig. 16.5 'Observation' and 'Decision' information nodes.</figcaption></figure>
the same way that people tend to prefer short food circuits, it is particularly relevant to consider short data circuits. In other words, data should be stored close to where it is produced, and if a resilient smart-territory exists in the future, it is believed that it will be based on a distributed cloud/ edge architecture distributed in each home, village, town, city... For now, the architecture uses the Internet to reach other villages, but ideally each village should be physically connected with a direct connection (Village-to-Village) to avoid using the global network when not necessary. Existing peer-to-peer and meshed networks are very interesting alternatives from the perspective of resilience and trust.

### 3.1.7. *Perspectives of the BOLDAIR project*

Most of the project involved designing, constructing, and deploying an LCMS as an efficient and scalable data sensor architecture. However, it is not enough to simply provide information; it is essential to involve the population to create greater awareness, effect behavior change, and alter practices. This project seeks to develop a decision system for local authorities that integrates technological data from sensors, human reports, and expert analyses into collective decision-making and action tools. This system will take the form of a digital twin, which is “a dynamic representation of a physical system using data, models and interconnected processes that provides access to knowledge from past, present and future states for system management” (Camara et al., 2021). This digital twin will support the incremental development of public and private knowledge bases to help decision-makers and citizens understand the state of the territory and make informed decisions in real time to improve air quality (Endsley et al., 2003).

## 3.2 *Synthesis*

Experiments conducted in villages and larger territories have shown that developing a smart territory is more than just collecting and gathering information about the area. For the tool to be accepted and utilized to its full potential, it is essential to involve human participation and inclusion in the process (Boy, 2020). Inhabitants, elected officials, and developers should be brought together to discuss and propose the most useful services. Moreover, user-friendly interfaces between humans and machines must be developed. Maps and cartography have long been a universal representation that everyone knows how to use. With the advancement of ICT, 3D representation, and 3D virtual worlds are even more capable of accurately reproducing reality. It is important to use these to provide engaging interfaces that will encourage participation, and enhance ergonomics and usability. 3D worlds promise to be an excellent interface that could be easier and more natural to use than classic computer interfaces. Integrating all
these information systems, through a distributed architecture that is private
by design and resilient, into a 3D virtual world interface, could be seen as a
genuine digital twin of the territory, which could be the backbone of smart
villages, cities, and territories.

One important aspect of a smart territory is about being able to model
the current situation but also to be able to foresight possible future to
anticipate and take advised decisions. These aspects are fundamental in the
definition of a digital twin and therefore simulation capabilities must be
added to the whole proposition. Indeed, the notion of digital twins offers an
exciting opportunity to test new scenarios and the resilience of a territory.
Based on multi-scale modeling, this digitalized approach to the territory
makes it possible to imagine different scenarios for anticipating, resolving
crises or even to optimize the territorial and societal operations.

4. Towards a Territorial System of Digital Twins

A Digital Twin is essentially composed of 5 components as defined by and extended by (Xia et al., 2022). A Physical Part (the Scene), A Virtual Model, a Connection, which is the gathering device used for obtaining data for integrating virtual and physical spaces (Deng et al., 2021), Data from the physical part, and a Service (target application, e.g., smart cities). The use of a Digital Twin in a Smart City allows for improved decision-making and resource management. By having a detailed representation of the city and its systems, city officials and managers can analyze data and simulate potential scenarios to determine the most efficient and sustainable course of action (Jiang et al., 2019). Furthermore, the Digital Twin can be used to monitor the performance of the city and its systems, allowing for early detection of issues and proactive maintenance (Gao, 2018).

While the concept of a Smart City and a Smart Village share similarities,
there are distinct differences in the application of Digital Twins in these
contexts. A Smart City typically has a larger population, more advanced
infrastructure, and more resources at its disposal compared to a Smart
Village. As such, the implementation and use of a Digital Twin in a Smart
City may be more complex and require more advanced technology.
However, the principles of a Digital Twin can still be applied to a Smart
Village. The process of creating a Digital Twin for a Smart Village would
involve collecting and analyzing data from various sources, such as
sensors and IoT devices, to create detailed, 3D models of the village and its
systems. This Digital Twin can then be used for simulation, prediction, and
optimization of the village's resources and infrastructure.

One key difference between a Smart City and a Smart Village in terms of
Digital Twins is the scale and complexity of the systems being modeled. A
Smart City has a larger and more diverse set of systems and infrastructure,
such as transportation networks and public utilities, whereas a Smart Village typically has a more limited and simpler set of systems. As a result, the Digital Twin of a Smart Village may be less complex than that of a Smart City, and mostly focuses on trying to get an efficient virtual model. This part needs a direct gathering device, where recent work has shown a high level of accuracy in capturing data of the physical part in point clouds formats.

In summary, while the principles of a Digital Twin can be applied to both Smart Cities and Smart Villages, the implementation and use of a Digital Twin in a Smart Village may be less complex and require less advanced technology due to the smaller scale and simpler set of systems present in a village. On another consideration this digital twin will exist only by and humans. It will be made as a system of systems, based on an ecosystem, distributed, at the edge. Section II showed that to achieve the smartification of the territory, one must consider not only cities and villages but the whole territory. We detail in the following the major aspects to be implemented in the digital twin of villages.

## 4.1 Human centered and participatory dimensions of the digital twin

To effectively implement a digital twin in rural areas, it is necessary to consider the human centered and participatory dimensions of the project. Unlike the digital twins of smart cities ("cyborg city") where the human is considered as an object and not a subject (Cathelat B. et al., 2019), it seems preferable to develop a 'community' approach, placing the human at the center, involving every user from the beginning as soon as the design starts. Uses and practices of ICT should result in the definition of services co-constructed with the population and supported by the smart-village and its digital twins. Then it will be able to improve the social links between inhabitants but also e-governance with officials. To conduct such a project of smart society in rural areas, several dimensions need to be developed. Implementation of digital twins must be integrated with a service-oriented architecture that will support local services (professionals, neighbors, individuals...) via a local exchange system mechanism, as well as institutional services (e-governance, e-administration, security, and crisis management).

The dynamic modeling of territories must be carried out collectively to limit the cost but also to involve users (crowdsourcing). The development of (distributed) functional internal engines is a challenge, especially to ensure resilience and ensure respect of the privacy of actors (3D engine and service orchestrator). To include everyone, universal interfaces with the architecture must be developed, whether in the form of applications on
computers, mobile or tablet devices, or via immersive approaches enabled
by the 3D dimension of the project provided by the digital twin.

A major lock here, beyond capture, is proposed to be updated by
limiting the number of sensors and citizen participation. The acceptability
and appropriation of digital technologies is an important aspect to be
considered in the digital twin, and this will be addressed through a strong
hypothesis which is since 3D technologies bring a more appropriable
interface, being closer to reality, and thus reducing the digital divide (the
acceptability of immersive interfaces is also to be evaluated). Delegation
functionalities should also be included to address the fact that some people
may not be able to use the system (such as those in strong illectronisme
situations). Training aspects must also be considered, regarding the use of
the architecture through the interfaces and aspects related to cyber security,
so that bringing digital technology to those who need it does not open the
door to maliciousness.

## 4.2 Territorial digital twins, a system of digital twins

A system of systems (SoS) is a collection of multiple, independent systems that work together to achieve a common goal or set of goals (Boardman, 2006). These systems may be from different organizations or different domains and may have different levels of complexity and functionality. The SoS is characterized by its ability to integrate the capabilities of its constituent systems in a way that enables new or enhanced capabilities that are not possible with the individual systems alone. Examples of SoS include transportation networks, power grids, and military systems. The smart village is clearly made of sub systems. An SoS differs from a single monolithic system in that it consists of multiple autonomous systems linked by interfaces and protocols. Each system within the SoS is responsible for its own internal behavior and operations but can also interact and share information with other systems within the SoS to achieve common goals. This makes SoS more adaptable and resilient, but it can also make it more complex and harder to manage.

A system of ecosystems is a collection of multiple, interrelated
ecosystems that work together to maintain a balance within an environment
or region. Ecosystems are defined as a community of living and non-living
things and their interactions with each other and the environment. In the
context of smart villages and territories, a system of ecosystems refers to
the interconnections and interdependencies among different ecosystems
including human, physical and digital systems within a region. It is the
source of data for the smart village and or territory. The territorial digital
twin is a system of digital twins that will possess the following features.
### 4.2.1 Autonomous and composable

It is believed that a smart territory, which is a system of systems and ecosystems, must have a highly distributed global architecture that is federated around singular entities, each of which potentially has a digital twin and is interconnected when necessary. This architecture is more of an edge computing architecture than a cloud architecture, and data should be owned and managed as close as possible to its source. To achieve this, it is suggested that villages should have their own servers, and potentially each home could have instances of them too. The idea is to have a private space and several shared spaces for each entity eligible for a digital twin, and all interconnections and information exchanges between entities are to be controlled and available in these controlled shared information spaces. Thus, the major challenge is to design the architecture's engines to work in this highly distributed context of nodes, from individuals to the different layers of organization. It appears that an organic approach is necessary to be able to construct a smart territory that can scale.

### 4.2.2 Orchestration of distributed services

It is essential that the digital twin surpasses the boundaries of a single community, particularly in rural areas. The orchestration of distributed services will be provided by the digital twin workflows that can anticipate from user needs the building of the appropriate service to be proposed. The service can be based on internal service and external services. One major lock here is to provide generic service engines that operate within the systems of interdependent and composable digital twins. What is envisioned are internal service engines that belong to singular digital twins and external ones that a digital twin can subscribe to. Note that these external engines are owned and made available by other DT.

### 4.2.3 Twins and proxies

This research paper aims to explore the potential of creating a participatory Digital Twin of the territory, aggregating individual, and collective information views. The focus is on the holistic view brought by the interconnection of autonomous information spaces that compose the territory, rather than designing full-fledged digital twins of the components. It is proposed that a digital twin has in its system proxies of other entities with which it is in interaction, and these proxies may not always be connected to ensure resilience and scalability. To illustrate this approach, a village may have its own digital twins and proxies on inhabitants, which could be linked with the genuine digital twins of each person who has created their own. Each entity develops its own independent world, where some nodes may exchange information in a controlled way from the ground up. The future digital twins will be made of aggregated data from different sources
in a loose coupling manner, so that it can be federated for specific goals and
then unleashed after the building and delivery of the service through the
digital twin and smart village.

4.3 3D representation for the territorial digital twin

Support for smart-cities and smart-villages to reach their full potential is
enabled using interfaces that facilitate interaction. These interfaces are often
Geographic Information Systems (GIS) that use maps and cartography to
represent the territory, with additional information of interest typically
presented in two-dimensional form. However, the advancement of ICT has
made it possible to integrate three dimensions into these representations,
providing a more natural virtual representation that is closer to the reality
perceived by users. It is hypothesized that providing 3D representations
of cities, villages, and territories will help to increase acceptance and
usability, as it more closely resembles the real thing. Furthermore, progress
in virtual, augmented, and extended reality offer promising directions for
interfacing the real world with the virtual world, and to achieve this level of
immersion, 3D models of the environment must be considered. To explore
the feasibility of this in a rural context, low-cost and participatory reality
capture is examined.

4.3.1 *The Connection* (3D *gathering device*)

3D Reality Capture Methods, such as LiDAR, Photogrammetry, and Structure from Motion (SfM), are widely used to generate point clouds for Smart Cities (Nys et al., 2020). These methods involve collecting data from various sources, such as sensors, cameras, and drones, and using this data to create a detailed 3D model of the city and its infrastructure (Poux et al., 2017) (Fig. 16.6).

LiDAR (Light Detection and Ranging) technology uses laser beams to measure the distance between the sensor and the target, providing high accuracy and precision in the data collected. LiDAR has been widely used in Smart City applications, such as mapping, 3D modeling, and change detection (Gao, 2018). Photogrammetry involves using photographs to

<figure><img src="image_6.png" /><figcaption>**Fig. 16.6** Images Capture Methodology.</figcaption></figure>
measure the location and properties of physical objects. It is also used in Smart City applications such as building façade and cultural heritage mapping (Besl, et al., 1992). SfM (Structure from Motion) is a technique that uses a sequence of images to reconstruct a 3D model of a scene. It is widely used in creating 3D models of cities, towns, and villages. (Poux et al., 2020). These methods have proven to be effective in generating highly accurate and detailed point clouds for Smart Cities. However, they may not be as applicable to Smart Villages due to the smaller scale and simpler set of systems present in a village. Furthermore, the cost of acquiring and maintaining the necessary equipment and technology for these methods are prohibitive for smaller, rural communities (VanDerHorn et al., 2021). Indeed, acquiring datasets for entire cities often relies on dynamic systems such as Mobile Mapping or Aerial Configurations. Mobile Mapping Systems (MMS) and Aerial LiDAR are widely used for creating digital twins of smart cities. MMS involves mounting LiDAR sensors and cameras on a vehicle and driving around the city to collect data. Aerial LiDAR, on the other hand, involves using a laser scanner mounted on an aircraft to collect data from above. Both methods provide highly accurate and detailed point clouds of the city and its infrastructure. However, there are several limitations to the use of MMS and Aerial LiDAR for smart cities. One major limitation is the high cost of these systems. The cost of acquiring and maintaining the necessary equipment and technology for MMS and Aerial LiDAR can be prohibitive. Additionally, the scalability of these methods is limited in tight environments such as narrow alleys, urban canyons, and crowded city centers where the vehicle or aircraft may not have enough space to maneuver or a poor global navigation satellite system (GNSS) signal. Furthermore, MMS and Aerial LiDAR are not well-suited for capturing data of buildings and other structures with complex geometries, due to the limitations of the sensors used (Zhou, et al., 2020). Also, MMS and Aerial LiDAR may not be able to capture data of buildings and other structures that are obscured by trees, buildings, or other obstacles (Zhao, et al., 2021). Therefore, MMS and aerial LiDAR are widely used for creating digital twins of smart cities, but they have their limitations. These systems are high-cost and have limited scalability in tight environments, also they have difficulty capturing data of buildings and other structures with complex geometries and obscured buildings. On top, these methods may not be as applicable to Smart Villages due to the smaller scale, simpler set of systems and the cost of acquiring and maintaining the necessary equipment and technology for these methods.

### 4.3.2 Toward sustainable 3D Data Capture

#### 4.3.2.1 Low-cost Reality Capture

Low-cost terrestrial photogrammetry is a method of generating accurate and up-to-date point clouds of small villages that has several strengths.
This method involves using a consumer-grade camera, such as a DSLR or a smartphone camera, mounted on a tripod or handheld, to capture a series of overlapping images of the village. These images are then processed using photogrammetry principles to obtain such exhaustive 3D spatial information (Zhang et al., 2017) primarily as a point cloud. It is a {X, Y, Z} (+ attributes) spatial ensemble which digitally represents the recorded environment w.r.t the sensor strengths and limitations. The landscape of these instruments and acquisition methodologies is mature enough to allow digital replicas of the real world ranging from the object scale to the country scale (Narayanan et al., 2011). One of the main strengths of low-cost terrestrial photogrammetry is its affordability. Compared to other 3D Reality Capture Methods, such as Aerial LiDAR or MMS, this method requires minimal investment in equipment and technology. This makes it accessible to small villages that may not have the resources to invest in more expensive equipment. Another strength of this method is its ability to capture high-resolution and up-to-date images of the village. The use of consumer-grade cameras allows for capturing high-resolution images, which can be used to create detailed point clouds of the village. Additionally, the method can be easily repeated to capture updated images of the village, providing an up-to-date point cloud. Furthermore, low-cost terrestrial photogrammetry does not require special permissions or clearance, which makes it more accessible to small villages with limited resources. Therefore, the gain in affordability, high-resolution and up-to-date image capture that provides photogrammetry can be a valuable tool for small communities that wish to create a digital twin of their village.

### 4.3.2.2 Crowdsourced photogrammetry

Low-cost and crowdsourced photogrammetry have emerged as valuable techniques for creating up-to-date digital twins for Smart Villages. The use of low-cost equipment and crowdsourcing techniques, where the data is collected by members of the community, allows for the creation of digital twins that are both cost-effective and reflective of the local community's needs and perspectives. One of the key advantages of low-cost and crowdsourced photogrammetry is its ability to provide up-to-date digital twins of the village. The use of consumer-grade cameras and smartphones allows for the frequent capture of images of the village, which can be used to create updated point clouds. Additionally, the participation of members of the community in the data collection process ensures that the digital twin reflects the current state of the village and the community's needs and perspectives. Especially, it can be useful for the analysis of public opinion and sentiment regarding the village and its development (Zhou, et al., 2020).

Crowdsourced photogrammetry also has the potential to engage and empower members of the community, particularly youth and women, in the data collection process and decision-making related to the village's
development. It can provide a platform for community participation and co-creation in the smart village development. This is particularly important in rural areas where community participation and empowerment are key to sustainable development (Hossain, et al., 2020). Furthermore, low-cost, and crowdsourced photogrammetry can be integrated with other data sources such as satellite images, citizen-generated data, and open data to provide a more comprehensive picture of the village and its systems. For example, integrating data from social media platforms, such as Twitter and Facebook.

In conclusion, low-cost and crowdsourced photogrammetry bring important perspectives and valuable tools for creating up-to-date digital twins for Smart villages. These methods allow for the creation of cost-effective digital twins that are reflective of the local community's needs and perspectives. Additionally, crowdsourced photogrammetry has the potential to engage and empower members of the community in data collection. Therefore, this method is very promising to establish an efficient Digital Twin for monitoring, planning and decision-making related to the village's development and resource management (Khan et al., 2020).

<figure><img src="image_7.png" /><figcaption>Fig. 16.7 Example of 3D reconstruction from crowd-sourced photogrammetry.</figcaption></figure>

## 4.4 Simulation for large complex system of systems

In the previous sections, it has been demonstrated that simulation can be a powerful tool for validating the behavior of a system and can be used in the context of a digital twin to analyze different scenarios and anticipate the future. However, the works presented thus far have been largely limited
to specific sectors, failing to take a holistic, global view. Therefore, there is a need for a global architecture that can gather, aggregate, and process data and information from sensors, considering uncertainty, spatial, and temporal dimensions, and enable communication between officials and citizens. The feedback from previous projects suggests that modeling and simulation play an important role in digital twins for smart cities. However, the models are often heterogeneous and not always formal and unambiguous. Building a model is a specific process that involves selecting a theoretical framework, such as a formalism, to accurately describe the source system or object of study, while also ensuring that the whole process is suitable for the problem at hand. As Robinson (2015) notes, "conceptual modeling is more of an art than a science".

In the case of large and complex systems digital twins, such as a village, city, or region, it is necessary to use multi-modeling approaches to select the most appropriate methods for modeling the various phenomena, such as water networks, circulation, electricity production/consumption, consumers, and inhabitants.

Multi-modeling, as defined by Vangheluwe et al. (2002), is the method of combining multiple perspectives of a study system into a coherent representation. This involves managing phenomena at different levels (micro and macro), at different scales (temporal and spatial), and from different perspectives, by using multiple formalisms or modeling paradigms for the same problem. Other approaches, such as co-simulation (Gomes et al., 2018; Camus et al., 2018), can also be used in a concurrent or complementary manner.

According to Gomes et al. (2018), "co-simulation is a theory and set of techniques for simulating a coupled system, by combining individual simulators. Each simulator is generally considered as a black box that can represent behavior, consume inputs, and produce outputs."

These approaches focus on model composition and aim at selecting and connecting the most appropriate modeling techniques. However, they can be limited when the spatial or geographical representation dimension also needs to be considered in the model. This is particularly the case when it is necessary to stack layers of models representing phenomena at different scales, such as wastewater flow, car traffic, weather, etc. (Broutin et al., 2008; Broutin et al., 2010). Spatial representation is an essential element for any territory-scale modeling (Roche, 2014), and as stated by Williamson et al. (2010), a smart city must be equipped with spatial capabilities. Indeed, a "spatially active society is an evolving concept in which location, place, and other spatial information are available to governments, citizens, and businesses as a means to organize their activities and information".

Multilevel modeling can be used to solve several modeling problems (Morvan, 2013) that are related to the concerns of multi-paradigm modeling: (1) modeling the interaction between levels, (2) coupling heterogeneous
models, and (3), dynamically adapting the abstraction level (Franceschini et al., 2019). There are several approaches to model this spatial dimension, some of which even allow modeling and simulation:

Geographic information systems (GIS) (Fischer et al., 1992) (Bernhardsen T., 2002) can display spatially indexed data from various sources, modifying these data, storing them, retrieving them, and manipulating them for analysis. Their great strength lies in their ability to manage large, heterogeneous, multi-layered databases and to interactively query the existence, location, and properties of a wide range of spatial objects. However, the lack of analysis, modeling and simulation capabilities is widely recognized as a major shortcoming.

Nested graph structured representations (NGSR) (Macedo et al., 1998) are described by the nodes of the graph and a set of links between them, represented by the edges, with the particularity that each of the nodes of the graph can integrate another graph, and so on recursively.

Multilayered cellular automata (Bandini S., 1999) is based on the definition of a nested graph, it allows to reproduce multiscale processes with a fine spatial/temporal resolution, and especially to model and simulate transition rules between the states of the different layers.

Multi-Level Agent-based Modeling Modeling (ML-ABM) (Morvan, 2013) is characterized by the following definitions: (1) A level is a point of view on a system, embedded in a model as a specific abstraction. This name refers to levels of the organization, observation, analysis, etc. (2) An agent-based multilevel model integrates heterogeneous (agent-based) models, representing complementary views, called levels, of the same system. (3) Integration means that the ABMs in an ML-ABM can interact and share entities such as environments and agents. (4) Heterogeneity means that the ABMs integrated into an ML-ABM can be based on different modelling paradigms (differential equations, cellular automata, etc.), use different temporal representations (discrete events, stepwise, continuous system), and represent processes at different spatiotemporal scales. (5) The points of view are complementary for a given problem because they cannot be taken in isolation to treat it.

Tools such as Gama (Drogoul et al., 2013; Drogoul et al., 2013) and netlogo (Hjorth et al., 2020) incorporate these considerations. There is also formal work based on the DEVS formalism (Zeigler et al., 2018) that addresses these considerations (Broutin et al., 2008; Foures et al., 2018).

Capturing the globality of the points of view on the studied system is an essential prerequisite for its good modeling (Stanton et al., 2006). In this context, the vision advocated in some works on digital twins (augmented model) seems very relevant at the scale of a territory, and to set up processes to help decision-making based on data and models (Ketzler et al., 2020; Xia et al., 2022).
## 5. Conclusion

This chapter demonstrated that the digital world is one lever to facilitate life of population in the small cities and more rural territories. It focused on the contribution of digital to Smart villages and smart territories. The current developments in relation to smart villages provided interesting results as well as direction to get a larger scale territory that goes beyond the frontier of a given city or village. The first part that was dealing with recalls on experiences and feedback from the proof of concept in Cozzano showed that digitalized approach to the territory can make it possible to imagine different scenarios to resolve crises in the countryside. Then territorial services in large rural areas including medical and air quality cases presented the interest of modeling simulation to provide decision support to citizen, patient, and professionals. Finally, this study revealed that major required concepts of a territorial System of digital twins must include humans, system of systems and 3D environments. In the future Smart Village, the implementation of Digital Twin may be less complex and will require less advanced technology due to the smaller scale and simpler set of systems present in a village. The Digital Twin of a Smart Village may focus on creating an efficient virtual model with data collected from various sources, such as sensors and IoT devices with the use of simulation. However, the digital twin will exist only by and with humans. It will be made as a system of systems, based on an ecosystem, distributed, at the edge. One must consider not only cities and villages but the territory to achieve smartification of the territory.

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