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**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.

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**Chapter 11**
# Wireless Crowd Detection for Smart Overtourism Mitigation

Tomás Mestre dos Santos ID, Rui Neto Marinheiro ID, and Fernando Brito e Abreu ID

**Abstract** Overtourism occurs when the number of tourists exceeds the carrying capacity of a destination, leading to negative impacts on the environment, culture, and quality of life for residents. By monitoring overtourism, destination managers can identify areas of concern and implement measures to mitigate the negative impacts of tourism while promoting smarter tourism practices. This can help ensure that tourism benefits both visitors and residents while preserving the natural and cultural resources that make these destinations so appealing.

This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity. A flexible architecture was designed for a smart tourism toolkit to be used by small and medium-sized enterprises (SMEs) in crowding management solutions, to build better tourism services, improve efficiency and sustainability, and reduce the overwhelming feeling of pressure in critical hotspots.

The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies, mitigating the effect of MAC address randomization. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database, without infringing privacy rights. After that edge computing, sensors communicate the crowding information to a cloud server, by using a variety of uplink techniques to mitigate local connectivity limitations, something that has been often disregarded in alternative approaches.

Field validation of sensors has been performed on Iscte's campus. Preliminary results show that these sensors can be deployed in multiple scenarios and provide a diversity of spatiotemporal crowding data that can scaffold tourism overcrowding management strategies.

**Keywords** Overtourism · Smart tourism toolkit · Crowding sensor · Edge computing · Wi-Fi detection · Fingerprinting · MAC address randomization

<footnote>T. Mestre dos Santos (✉) · R. Neto Marinheiro · F. Brito e Abreu
Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal
e-mail: tmmss1@iscte-iul.pt; rui.marinheiro@iscte-iul.pt; fba@iscte-iul.pt</footnote>
# 11.1 Introduction

The tourism sector has been growing steadily. If the pre-pandemic trend is achieved from 2023 onward, it will reach 3 billion arrivals by 2027, based on the World Bank development indicators (see Fig. 11.1).

As a consequence, the impact of tourist activities in popular destinations has risen significantly over the years, often fostered by the proliferation of cheaper local accommodation (Guttentag 2015). That increase led to exceed of carrying capacity in those destinations, a phenomenon called *tourism overcrowding*, or simply *overtourism*. The latter degrades visitors' quality of experience, reducing their feeling of safety, making it difficult to move around, enjoy the attractions, and use basic services, such as transportation and restoration, due to long wait times, while reducing the authenticity from the perspective of tourists (Tokarchuk et al. 2022). Overtourism also deteriorates the lives of local residents, due to an increase in urban noise, less effective urban cleaning, higher prices for basic goods and services (as businesses seek to capitalize on the increased demand), displacement caused by local accommodation, and cultural clashes when visitors fail to respect local customs, traditions, and privacy, sometimes leading to the former expressing negatively against the latter (Biendicho et al. 2022). Last, but not least, the environmental sustainability, structures, and cultural heritage of overcrowded destinations are also jeopardized, leading to a loss of authenticity (Seraphin et al. 2018). Mitigating overtourism benefits all stakeholders:

* Local residents reduce their stress from over-occupation of personal space and privacy and improve their attitude toward tourists and tourism professionals.

* Tourism operators speed up service delivery and quality of service.

<figure><img src="image_94.png" /><figcaption>Fig. 11.1 Worldwide evolution of tourism arrivals, based on the World Bank's data</figcaption></figure>
* Tourists increase their visit satisfaction, with fewer delays, increased safety, and cleanliness.

* Local authorities improve services by making just-in-time decisions and planning more effectively urban cleaning and public safety routines, as well as reducing operating costs.

* Heritage managers can prevent heritage degradation more effectively, thus retaining the authenticity of destinations.

* Local businesses increase their share of tourism income.

Overtourism mitigation actions, such as promoting the visitation to less occupied but equally attractive areas, can be applied in recreational, cultural, or religious spots, both in indoor scenarios like palaces, museums, monasteries, or cathedrals and in outdoor ones such as public parks, camping parks, concerts, fireworks, or video mapping shows.

Besides assuring a better visiting experience, those actions are also necessary for security reasons (e.g., to prevent works exhibited in a museum from deteriorating or even being vandalized by exceeding room capacity), health reasons (e.g., preventing infection in pandemic scenarios by not exceeding the maximum people density specified by health authorities), or even for resource management (e.g., to reduce the intervention of security and cleaning teams).

To implement overtourism mitigation actions, crowding information should be made available. Several approaches can be used for crowd detection, such as image capturing, sound capturing, social networks, mobile operator's data, and wireless spectrum analysis (Dias da Silva et al. 2019). The latter can be performed using passive or active sniffing methods, characterized by exploring protocol characteristics and small information breaches, such as on Wi-Fi or Bluetooth protocols, extensively used in mobile devices. Figure 11.2 provides a comparison of those approaches for crowd counting in terms of range, precision, time delay of analysis, and implementation costs.

The best option regarding cost, precision, and the near-real-time availability of data required for managing tourism crowding effectively, while complying with privacy rights, is the one based on sensing wireless communication traces, since the vast majority of tourists carry a mobile phone (Dias da Silva et al. 2019; Singh et al. 2020). Earlier approaches relied on counting the number of unique MAC (Media Access Control) addresses in messages emitted by mobile devices. However, due to user privacy concerns, most mobile devices nowadays use MAC address randomization, i.e., the same device exposes different MAC addresses over time, making it more challenging to accurately count the number of devices, thus leading to inaccurate crowd counting.

This chapter describes a low-cost approach to monitoring overtourism. It consists of a crowding sensor that performs real-time detection of trace elements generated by mobile devices from different wireless technologies, namely, Wi-Fi and Bluetooth, while addressing the MAC address randomization issue when determining the number of mobile devices in the sensors' vicinity, as an improvement over our previous work Dias da Silva et al. (2019). Another improvement refers to
<figure><img src="image_95.png" /><figcaption>**Fig. 11.2** Different approaches for crowd counting in terms of range, precision, time delay of analysis, and implementation costs (Adapted from Dias da Silva et al. 2019)</figcaption></figure>

the provision of multiple communication methods for uploading the crowding information to a cloud server, by using either Wi-Fi or LoRaWAN protocols, thus mitigating network limitations on the installation location, something that has been disregarded on other approaches.

Our sensor is the basis of a Smart Tourism Toolkit (STToolkit) being built in the scope of the RESETTING<fnref n="1" /> project, funded by the European COSME Programme., to facilitate the transition toward a more sustainable operation of tourism SMEs and improved quality of the tourism experience. The STToolkit will guide how to build and set up our sensors, either in indoor or outdoor appliances, by including support materials such as an installation manual, video tutorials, setup images, and cost calculators.

Furthermore, this research considers a correlation between the number of mobile devices and the real number of people present in an area. This assumption is especially relevant in touristic scenarios, where our sensors are aimed to be deployed since tourists usually carry their mobile phones to take pictures and record videos during their visits. Therefore, it is assumed that the number of mobile devices in a given area is directly correlated with the number of people in the same area. This is corroborated by De Meersman et al. (2016), where it is shown that mobile phone data is a valuable data source for statistical counting of people.

This chapter is organized as follows: Sect. 11.2 identifies and discusses related work; Sect. 11.3 presents the proposed architecture of a typical installation using our sensors; then, in Sect. 11.4, we describe our proposed Wi-Fi detection algorithm, which tackles the MAC address randomization issue; on Sect. 11.5, we describe

<footnote n="1">¹ RESETTING is an acronym for “Relaunching European smart and Sustainable Tourism models Through digitalization and INnovative technologyGies”.</footnote>
the technologies used in our solution; then, on Sect. 11.6, we present the setup and discuss the results obtained from a field validation; finally, on Sect. 11.7, we draw some conclusions and outline future work.

## 11.2 Related Work

Crowd counting by detecting trace elements from mobile devices' wireless activity can be performed either by the use of passive or active sniffing methods. However, only passive methods that monitor wireless traffic in a non-intrusive manner are acceptable, because active methods can cause network and user disruptions, as well as legal infringements. Many passive methods employ probe request capturing, which are messages periodically sent by devices to announce their presence to surrounding APs (Access Points), allowing a fast connection upon reaching a known network. These messages are sent in bursts and are unencrypted, meaning that they can be simply captured using passive sniffing techniques, and contain the device's MAC address. The probe request frame structure is presented in Fig. 11.3.

Earlier detection approaches relied on the device's real MAC address that was sent in the SA (Source Address) of these frames. In this case, the number of devices was simply equal to the number of different MAC addresses. However, when devices send their real MAC address, they may be easily tracked. To solve this privacy vulnerability, since 2014, manufacturers started to implement MAC address randomization on their devices. It consists of assigning to probe requests randomly generated virtual MAC addresses changing over time. Thus, the real MAC address remains unknown, protecting the user's identity and making it much more difficult to track. Unfortunately, this has led to inaccurate crowd counting and has hampered many solutions adopted until then. Moreover, the MAC address randomization process is dependent on the manufacturer and the operating system of the device, which also makes it a much more complex procedure to circumvent.

The difference between a real MAC address (globally unique) and a virtual MAC address (locally administered) is in the 7th bit of the first byte of the MAC address, as shown in Fig. 11.4. Therefore, we can simply distinguish these two types of MAC addresses by only checking this bit.

The implementation of the MAC address randomization added a level of complexity to uniquely identify devices. Therefore, the research has advanced toward the exploration of other properties and fields of the probe request frames,

<figure><img src="image_96.png" /><figcaption>Fig. 11.3 Probe request frame (based on Institute of Electrical and Electronics Engineers)</figcaption></figure>
<figure><img src="image_97.png" /><figcaption>**Fig. 11.4** Difference between a real and a virtual MAC address</figcaption></figure>

since the MAC address is no longer a reliable option just by itself for accurate crowd counting. In spite of this, probe request frames still disclose other weaknesses that can be exploited for counting the number of devices. Several strategies have been adopted to mitigate the impact of randomization, as follows:

* **SSIDs**<fnref n="2" /> **Comparison:** based on comparing the known networks (in terms of SSIDs) to a device, an information that is contained in the probe requests (Berenguer et al. 2022).

* **Fingerprinting:** based on generating a unique identifier (fingerprint) from other fields in probe request frames. The contents to generate this fingerprint are usually obtained from IEs (Information Elements) conveyed in the frame body of probe requests (Bravenec et al. 2022; Vega-Barbas et al. 2021).

* **Fingerprinting + Clustering:** this strategy relies not only on fingerprinting from IEs but also on other properties from these messages, such as the SEQ (Sequence number), burst size, or IFTA (Inter-Frame Time Arrival) from probe request frames. A clustering algorithm considering these properties simultaneously can be applied, where each cluster will represent a unique device (Cai et al. 2021; Covaci 2022; He et al. 2022; Torres-Sospedra et al. 2023; Uras et al. 2020, 2022).

In Berenguer et al. (2022), the PNL (Preferred Network List), which contains the SSIDs from the known networks of a device sent in probe requests, is used for counting the number of devices in a location and distinguishing residents from visitors in the city of Alcoi in Spain. The authors claim an accuracy of 83% in detection, with some reported overestimations and incongruencies.

Most approaches rely, however, on applying fingerprinting techniques to uniquely identify mobile devices. The work reported in Vega-Barbas et al. (2021) used a network of sensors to estimate the number of persons in a given location based on IEs fingerprinting. The system was tested in public events with a considerable density of people, with a claimed accuracy close to 95%. Another

<footnote n="2">² SSID (Service Set IDentifier) is a sequence of characters that uniquely names a Wi-Fi network.</footnote>
work described in Bravenec et al. (2022) used the same approach, considering not only the IEs but also the PNL and the recurrence of the same randomized MAC address to generate device fingerprints at a conference in Lloret de Mar, Spain, but precision is not reported.

Some other studies not only considered IEs for fingerprinting but also clustered this information along with other properties or patterns from probe requests. For this purpose, many studies used clustering algorithms that consider a combination of different features from probe requests. In Cai et al. (2021), not only probe requests but also beacons and data packets were used to count the number of devices in given locations. This method was tested with a dataset purposefully generated for the scope of this work, reaching an accuracy of 75%; however, it was not tested in a real crowded scenario. The work reported in He et al. (2022) considered IEs, SEQ, and the RSSI (Received Signal Strength Indicator) from probe requests with a neural network for estimating crowding levels in a shopping mall in Hong Kong, reaching an accuracy of slightly over 80%.

The studies reported in Uras et al. (2020, 2022) clustered fingerprinting from IEs, the incremental speed of the SEQ, the burst frequency, and the IFTA. The authors first tested the algorithm at the University of Cagliari's Campus (Uras et al. 2020), achieving an accuracy of about 91%. A follow-up to this work Uras et al. (2022) tested the algorithm first in a controlled environment, reaching an accuracy of 97%, and further inside buses in Italy for an Automatic Passenger Counting system, with a precision of 75%. Another work reported in Covaci (2022) used the same approach considering the IEs for fingerprinting and also used a clustering algorithm for combining the generated fingerprints with burst sizes and the IFTA in the canteen of the University of Twente, with an accuracy of 90%. The work described in Torres-Sospedra et al. (2023) combined fingerprints from RSSI values of Wi-Fi APs and BLE (Bluetooth Low Energy) beacons with several clustering algorithms variants for indoor positioning, achieving a precision of around 93%.

Table 11.1 summarizes the previous approaches for crowd counting, clarifying the adopted strategies to mitigate MAC address randomization and the obtained precision.

## 11.3 Proposed System Architecture

The proposed system architecture of the STToolkit is presented in Fig. 11.5, where sensors count the number of devices in their vicinity and periodically report the crowding information to a cloud server. The latter also has other components for making downlink communication transparent and providing uplink services for rendering the crowding information and creation of notification policies.
<table><caption>**Table 11.1** Approaches for crowd counting, tackling MAC address randomization</caption><thead><tr><th>Authors</th><th>Packet-type capturing</th><th>Strategies for MAC address randomization</th><th>Real scenario appliance</th><th>Precision</th></tr></thead><tbody><tr><td>Berenguer et al. 2022</td><td>Probe Requests</td><td>SSIDs Comparison</td><td>Alcoi (Spain)</td><td>83%</td></tr><tr><td>Vega-Barbas et al. 2021</td><td>Probe Requests</td><td>Fingerprinting</td><td>Public events</td><td>90%</td></tr><tr><td>Bravenec et al. 2022</td><td>Probe Requests</td><td>Fingerprinting</td><td>Conference at Lloret del Mar (Spain)</td><td>Not available</td></tr><tr><td>Cai et al. 2021</td><td>Beacons Data packets Probe Requests</td><td>Fingerprinting + Clustering</td><td>Not available</td><td>75% (with simulated data)</td></tr><tr><td>He et al. 2022</td><td>Probe Requests</td><td>Fingerprinting + Clustering</td><td>Shopping mall in Hong Kong</td><td>80%</td></tr><tr><td>Uras et al. 2020</td><td>Probe Requests</td><td>Fingerprinting + Clustering</td><td>Campus of the Univ. of Cagliari</td><td>91%</td></tr><tr><td>Uras et al. 2022</td><td>Probe Requests</td><td>Fingerprinting + Clustering</td><td>Buses in Italy</td><td>75%</td></tr><tr><td>Covaci 2022</td><td>Probe Requests</td><td>Fingerprinting + Clustering</td><td>University of Twente campus</td><td>90%</td></tr><tr><td>Torres-Sospedra et al. 2023</td><td>Beacons</td><td>Fingerprinting + Clustering</td><td>Not available</td><td>93%</td></tr></tbody></table>

### 11.3.1 Crowding Data Collection

Each crowding sensor includes a detector responsible for passively capturing mobile devices' trace elements for each wireless technology (Wi-Fi and Bluetooth) in the sensor vicinity, an anonymized local database, where all gathered information is stored, and a detection engine, responsible for counting the number of devices by analyzing the information contained in the local database and reporting the crowding information to the cloud server. Each sensor can perform only Wi-Fi or Bluetooth detection, or both simultaneously, and can quickly switch between the technologies to be used for detection.

In this edge computing approach, data collection and crowding level measurement generation are performed locally in each sensor, so that only the number of devices detected is sent to the cloud server. So, the information to be passed is minimal, not requiring a high sampling rate for data transmission, and also protecting nodes from outside threats, since the communication line prevents the majority of attack types. Furthermore, limiting data exchange not only reduces communication costs, but also eases protection complexity for the node, and makes it easier to guarantee user privacy. Also regarding user privacy, all gathered data is anonymized before being stored in the local database.
<figure><img src="image_98.png" /><figcaption>Fig. 11.5 Component diagram of the crowding detection STToolkit</figcaption></figure>

11.3.2 Communication and Services

To better address installation location requirements and connectivity limitations, a flexible deployment regarding uplink technologies has been considered. Data can be uploaded to the cloud server by using a variety of communication protocols, such as Wi-Fi or LoRaWAN (Long Range Wide Area Network).

If Wi-Fi is available on-site, data can be uploaded directly to the *Message Server* via the MQTT (Message Queuing Telemetry Transport) protocol, a lightweight method of carrying out messaging, using a publish/subscribe model, widely used for IoT (Internet of Things) applications. This option can be applied straightforwardly in indoor tourism scenarios, for instance, in a museum, which generally provides a Wi-Fi network to visitors.

In outdoor scenarios, such as public parks or city squares, where overtourism situations can also arise, Wi-Fi coverage may not be available. Since sensors must upload crowding information, other approaches rely on mobile operators'
communication, which may be an expensive option, usually with monthly fees depending on the number of sensors used, each using a SIM card.

To mitigate this problem, we offer the option for uploading data via LoRaWAN, a standard of the International Telecommunication Union that provides a low-cost and scalable alternative that is feasible for our application, since sensors only communicate a small amount of data, i.e., the number of detected devices. For this, sensors must be equipped with a LoRa board and corresponding antenna to communicate the crowding information to a LoRaWAN gateway that, in turn, will route the information to the cloud server via the MQTT protocol. Regarding coverage, there are a few LoRa networks, designed for IoT appliances, that can be used for uploading data, like The Things Network open collaborative network or the, also crowdsourced, Helium network, a decentralized wireless infrastructure supported by blockchain. The Helium network adopted for this solution is the fastest growing IoT network with LoRaWAN compatibility that provides a large coverage in many countries in Europe, such as those involved in the RESETTING project. Figure 11.6 shows the Helium network coverage provided by Hotspotty in cities where our STToolkit may be deployed in the context of the RESETTING project, such as Lisbon, Barcelona, Tirana, and Heraklion, the capital of the Greek island of Crete.

<figure><img src="image_99.png" /><figcaption>**Fig. 11.6** Helium network coverage in (a) Lisbon, (b) Barcelona, (c) Tirana, and (d) Heraklion
(Compiled by the authors from Hotspotty 2023)</figcaption></figure>
Regarding the Helium network, an SME can choose between two alternatives for uploading crowding information: using Helium with a third-party LoRaWAN service provider, such as Helium-IoT, or using Helium with a private LoRaWAN server.

As shown in Fig. 11.5, the *Message Server* is the only entrance point for all messages in the cloud server, independently of the communication protocol used for uploading the crowding information. This provides transparency since all messages are received in the cloud server via the MQTT protocol independently of the communication technology adopted for uploading the crowding information.

Furthermore, in areas with low or no Wi-Fi or Helium network coverage, it is also possible to acquire equipment for that purpose, such as a Wi-Fi mesh system, which will allow expanding the Wi-Fi network coverage, or a Helium hotspot, for grating Helium network coverage for uploading data via the LoRaWAN protocol.

### 11.3.3 Visualization and Notifications

The cloud server also has several components to make downlink communication transparent and provide several uplink services that can be used by Smart Tourism Tools to understand the crowding levels in areas where each sensor is placed, with a clear and simple perspective. Possible crowding services are:

* Rendering of temporal information, as seen in Fig. 11.10

* Rendering of geographic information, as seen in Fig. 11.11

* Notification policies, e.g., when crowding threshold levels are reached

* Raw data for custom-made integrations, e.g., spatial visualization using a BIM (Building Information Model), as seen in Fig. 11.12

## 11.4 Proposed Wi-Fi Detection Algorithm

MAC address randomization performed by mobile device manufacturers, due to user privacy concerns, has made the identification of a mobile device a much more difficult task and, consequently, more difficult to accurately perform device counting. Therefore, an algorithm was developed for the detection of mobile devices through Wi-Fi, tackling the MAC address randomization issue using a fingerprinting technique, presented in Fig. 11.7. The explanation of each step of the proposed algorithm is presented below. A similar algorithm is also envisaged for Bluetooth detection since the randomization problem is also pertinent to this technology.

The *WiFiCrowdingDetector*, seen in Fig. 11.5, is responsible for processing each Wi-Fi packet captured by the sensor. The first operation performed is a packet-type identification (data packets, probe requests, or other type). Data packets will be used for counting the number of devices connected to an AP and probe requests for
<figure><img src="image_100.png" /><figcaption>Fig. 11.7 Proposed Wi-Fi detection algorithm</figcaption></figure>

counting the number of devices not connected to any AP. The packet type can be obtained by checking the Frame Control field, presented in Fig. 11.3. Packets that are not either data packets or probe requests will be immediately dropped since they are not relevant for device counting.

Regarding data packets, only the UE (User Equipment) part of the MAC address needs to be accounted for. First, it is necessary to locate it in the frame, which is performed by checking the DS (Device Status) information in the frame header, since the UE MAC address position may vary according to the direction of the frame. These MAC addresses can be directly counted as single devices because when a device is connected to an AP, the MAC address is kept constant throughout the connection and, therefore, will not change randomly. For this reason, after the UE MAC address extraction, it is directly stored in the sensor's anonymized local database.

Regarding probe requests, the first operation performed is aimed at distinguishing its Source Address between a real and a virtual MAC address. This is performed by checking the 7th less significant bit of the 1st octet of the MAC address, as already
<table><caption>**Table 11.2** Probe request's information elements used to create device fingerprint</caption><thead><tr><td>Information element</td><td>IE ID</td><td>IE length</td><td>Description</td></tr></thead><tbody><tr><td>Supported rates</td><td>1</td><td>&lt;8</td><td>Data transfer rates supported by the device</td></tr><tr><td>Extended supported rates</td><td>50</td><td>&lt;256</td><td>Other bit rates supported by the device</td></tr><tr><td>DS parameter set</td><td>3</td><td>1</td><td>Device's channel setting when sending a probe request</td></tr><tr><td>HT capabilities</td><td>45</td><td>26</td><td>Compatibility with the 802.11n standard</td></tr><tr><td>VHT capabilities</td><td>191</td><td>12</td><td>Compatibility with the 802.11ac standard</td></tr><tr><td>Extended capabilities</td><td>127</td><td>&lt;256</td><td>Other device capabilities</td></tr><tr><td>RM-enabled capabilities</td><td>70</td><td>5</td><td>Information for measuring radio resources</td></tr><tr><td>Interworking</td><td>107</td><td>&lt;9</td><td>Interworking service capabilities of the client</td></tr><tr><td>Vendor specific</td><td>221</td><td>&lt;256</td><td>Vendor-specific information (e.g., device manufacturer)</td></tr></tbody></table>

shown in Fig. 11.4. To follow the trace of a real MAC address, a device classification is applied, aimed at only counting MAC addresses that belong to mobile devices. This is done by checking the address's OUI (Organizational Unique Identifier):<fnref n="3" /> if the OUI matches one of the known mobile manufacturers, obtained from the Wireshark manufacturer database, the MAC address should be considered as a mobile device, and it must be counted and stored in the local database; otherwise, the MAC address is not considered as a mobile device and is discarded.

To follow the trace of a virtual MAC address, a fingerprinting technique must be performed to uniquely identify devices that use MAC address randomization. For this, the IEs contained in the frame body of the probe request are analyzed. For each IE, the entirety of its information is considered, including the IE ID, Length, and Value bytes. For those IEs with substantially varying values across probes emitted from the same device (e.g., DS Parameter Set), only the bytes of IE ID and Length are analyzed. Table 11.2 shows the IEs used for the fingerprinting technique. After analyzing all IEs, a hash function is applied to all its contents. As a result, a 64-bit footprint is generated for each probe request and stored in the local anonymized database. Then, all the devices that are trying to connect to a Wi-Fi network, by sending probe requests to discover available networks in proximity with a virtual MAC address, are uniquely identified by the footprint. So, each footprint should be counted as one mobile device using MAC Address randomization that is trying to connect to a Wi-Fi network. To avoid counting the same device twice, if the

<footnote n="3">³ OUI is a part of the MAC address identifying the network adapter vendor.</footnote>
same MAC address is captured in both data packets and probe requests, it is only accounted for once.

Then, the Detection Engine will periodically count the number of devices detected within a sliding window of X minutes, i.e., the number of devices detected in the last X minutes, and upload that information to the cloud server. Both the sliding window period and data sampling rate can be independently and easily configured by the user. Since we intend to provide real-time or near-real-time data availability, the data sampling rate of our sensors needs to comply with this requirement. So, we have chosen a 5-minute period for the data sampling rate, as it is a sufficient time period for providing near-real-time data availability. Also, the same 5-minute period was chosen for the sliding window, so that each crowding measurement could comprise all detected devices within each sliding window.

The number of devices detected is the sum of (i) the number of devices connected to an AP, obtained from the UE MAC addresses captured in data packets, plus (ii) the number of different devices not connected to any AP, obtained from the MAC addresses from probe requests with real MAC addresses, plus (iii) the number of different footprints from probe requests with virtual MAC addresses.

## 11.5 Adopted Technologies

The developed STToolkit uses a variety of open-source software technologies, installed in off-the-shelf hardware available at affordable costs. Figure 11.8 presents the UML deployment diagram proposed for the STToolkit concerning all technologies adopted in our solution.

For the operating system of our sensors, we have opted for a Kali Linux distribution, which comes with a large number of preinstalled network tools that can be easily used for detecting devices in different technologies. For the local database, a SQLite database was chosen for storing all gathered data, which requires low memory usage, while meeting all other requirements. For data anonymization, the sensors use the t1ha library that provides several terraced and fast hash functions. In particular, we have opted for the t1ha0 hash function, as it is one of the fastest available at the library.

To perform Wi-Fi detection, the required hardware is a Wi-Fi card that supports monitor mode, which allows the board to capture all network traffic in its proximity. We have chosen the Alfa Network AWU036AC board for our sensor, which provides high performance at a low cost, having two antennas for dual-band detection (2.4 GHz and 5 GHz) without interfering with Bluetooth devices. As for the sniffing software, we have chosen the Aircrack-ng tool, an open-source software with several different applications for detecting devices. In particular, we use airmon-ng for enabling the monitor mode in the Wi-Fi board, and airodump-ng for capturing raw Wi-Fi frames. For Bluetooth detection, we have selected the Ubertooth-One board and corresponding BlueZ package that contains tools and frameworks for Bluetooth usage in Linux.
<figure><img src="image_101.png" /><figcaption>Fig. 11.8 Deployment diagram for the crowding detection STToolkit</figcaption></figure>

For receiving all messages, our cloud server uses the Mosquitto MQTT, a lightweight message broker that implements the MQTT protocol.

For the data ingestion of all measurements sent by sensors, a database is necessary. This database has to be lightweight, capable of querying data rapidly from timestamps, and also capable of providing support for data visualization platforms to observe the results in real-time. That is why we chose InfluxDB, a time-series database focused on IoT applications, for our CrowdingDB component. For the CrowdingCollector, responsible for pushing all messages received in the Message Server via MQTT protocol to the CrowdingDB in the appropriate format, we chose Telegraf, an open-source plugin-driven server agent for collecting and reporting metrics from devices.
<table><caption>**Table 11.3** Prototype hardware components and respective functions</caption><thead><tr><td>Component</td><td>Function</td></tr></thead><tbody><tr><td>Raspberry Pi 3/4</td><td>Coordinate and process</td></tr><tr><td>Alfa Network AWUS036AC</td><td>Wi-Fi detection</td></tr><tr><td>Ubertooth-One</td><td>Bluetooth detection</td></tr><tr><td>Raspberry Pi IoT LoRa pHAT</td><td>Upload via LoRaWAN (if necessary)</td></tr></tbody></table>

<figure><img src="image_102.png" /><figcaption>**Fig. 11.9** Sensor cases: (a) large version with no exposed antennas; (b) small version with exposed antennas</figcaption></figure>

Finally, for data visualization, we chose *Grafana*, an open-source analytics and monitoring tool compatible with several databases, including *InfluxDB*. This framework can be used for creating custom dashboards with graphs and panels for viewing, with different spatiotemporal levels of granularity, the crowding information. Additionally, *Grafana* can be used for creating notification policies, allowing users to receive alerts according to the crowding levels via a diversity of contact points.

A prototype was developed whose hardware components and respective functions are illustrated in Table 11.3. The prototype uses custom-designed cases adapted to deployment locations, either with exposed antennas or not, as shown in Fig. 11.9. These prototype versions have been deployed at several locations at our university campus to test the operation and performance of the STTToolkit, which is further described in the next section.

Furthermore, as our sensor's processing unit is a single computer board, namely, a Raspberry Pi, there are multiple options for powering our sensor, either directly from a battery or even via USB ports or Power over Ethernet (PoE), even though the most straightforward and convenient alternative should be to directly connect it to a mains power supply through a transformer.
## 11.6 Field Validation and Discussion

The prototype described in the preceding section was designed and implemented to withstand all the scenarios where the sensors may be deployed.

To test and validate the STToolkit architecture, sensors have been placed at several spots across Iscte's campus, and crowding information has been collected since September 2022.

The sensors were deployed both indoors and outdoors, in places with different crowding patterns, such as areas with a large pedestrian flow, internal and external passages between buildings, and places for prolonged stays, such as a large study hall and the university library.

This field experiment has been conducted with the sole purpose of assessing the perception of the crowding phenomena in the university campus, rather than the accuracy regarding the real number of people at each location. It focused on perceiving crowding patterns and tendencies, such as time breaks between classes, lunch periods, and highly populated events. The aim was to assess how sensors could perceive relative variations throughout the days across the several locations of the campus and how quickly the sensors were able to detect them.

The accuracy was addressed in other contexts in a more controlled environment (Santos 2023), where the detections from sensors were compared with the real number of people, obtained through direct observation during a public event, to assess the effectiveness of the solution.

The crowding data has been used for visualization, using a variety of temporal dashboards, and maps that highlight the geographic distribution of crowding at each location where the sensors have been deployed. Data has also been used for spatial visualization in the form of heatmaps and also, for a more realistic view, using avatars on top of Iscte's BIM (Building Information Model).

Dashboards allow users to select time ranges for crowding data temporal visualization, to perceive people's concentration and flows during specified periods, and to identify highly populated events. Figure 11.10 shows a comparison of crowding levels during a normal day of classes at Iscte's campus, at the selected spots where sensors have been deployed.

In addition to the temporal rendering of the information, data has also been used for spatial visualization in the form of heatmaps, to grant users a better perception of people distribution at several locations where the sensors are deployed. This can be seen in Fig. 11.11, where it is possible to perceive the crowding hotspots from our sensors deployed at Iscte's campus at a given time.

Moreover, raw crowding information can also be easily used by third-party integrations. To validate this, we built a walking avatar animation upon Iscte's BIM, to achieve a more realistic perception of space occupancy, as shown in Fig. 11.12, for one of the campus buildings. There, the number of detected devices, obtained in real time from sensors, determines the number of ingress and egress avatars in their areas of detection. This last experience was performed during the International
<figure><img src="image_103.png" /><figcaption>Fig. 11.10 Comparing crowding at Iscte’s campus</figcaption></figure>

<figure><img src="image_104.png" /><figcaption>Fig. 11.11 Crowding hotspots at Iscte’s campus</figcaption></figure>

Posters & Demos Workshop on Smart Tourism held by the RESETTING project at Iscte in January 2023.

Furthermore, it is also possible to create notification policies, where alerts can be triggered if predetermined crowding thresholds are exceeded, by using several contact points such as email, Telegram, Google Chat, Microsoft Teams, Slack, or PaperDuty, enabling users to make just-in-time decisions facing overtourism situations. These alerts can be easily configurable by using the *Grafana* tool, also used for spatiotemporal visualization of crowding information.
<figure><img src="image_105.png" /><figcaption>Fig. 11.12 Crowding visualization based on avatars upon the campus BIM</figcaption></figure>

## 11.7 Conclusions and Future Work

Overtourism deteriorates the visiting experience of tourists, the quality of life of residents, as well as the environment. By monitoring it, tourism professionals can identify areas of concern and put measures in place to lessen its negative effects, encouraging better tourism practices to ensure that tourism benefits both tourists and locals while preserving natural and heritage resources.

For monitoring overtourism, a low-cost approach based on mobile devices' activity has been developed. The sensors, equipped with off-the-shelf hardware available at affordable costs, perform real-time detection of trace elements of mobile devices' wireless activity, mitigating MAC address randomization, and crowding values are put together in a cloud server. Alternative communication channels for uploading the crowding information, namely, via Wi-Fi or LoRaWAN protocols, allow for addressing local connectivity limitations at the installation location of sensors. In addition, scalability is provided by maintaining the hardware costs low, by using open-source software, and by the simplicity of the installation and configuration of each sensor. Regarding the RESETTING project, an SME must choose the option that best fits its needs and requirements for implementing its system. To help with this purpose, the STToolkit will include a sensor deployment calculator for SMEs to estimate the most cost-effective uplink alternative according to the installation location of each sensor.

The crowding information can then be analyzed by destination managers to understand the crowding levels in areas where each sensor is placed in a clear and simple perspective, either by dashboards for temporal or spatial visualization of crowding information or using the raw data for custom-made integrations.
Furthermore, notification policies can be created when overtourism situations occur, opening the possibility of implementing just-in-time mitigation actions required by the nature of these circumstances, as they may be sudden and unpredictable.

Preliminary tests have been conducted for this solution. A prototype version of the crowding sensor was deployed at several spots on Iscte's campus, in typical usage scenarios with a high flow and/or extended presence of people, such as the university library, a large study hall, and two passageways. Crowding information has been collected and used to monitor people's flow and detect high-crowding events on campus.

In the short term, this ST Toolkit will be deployed at the Pena Palace, one of the most iconic tourism sites in Portugal, surrounded by a large walkable park, flagellated by overtourism all year round. The objectives will be promoting alternative routes for tourists within the park, limiting their number in sensitive areas, and making the tourism offer in this area more sustainable. Our detection approach will then contribute to reducing the overwhelming feeling of pressure in critical hotspots, thus leading to a greater visiting experience for tourists who visit this attractive tourist site.

Furthermore, a second prototype version of sensors is also envisaged. The latter will include new boards with greater performance, new antennas with higher gains for larger detection ranges, directional antennas for performing detection in specific areas, custom-designed heatsinks for the processing units to achieve the best possible performance, as well as new custom-designed cases.

Further details on the sensors, including demos of setting up and configuring the edge nodes and the cloud server, can be found online at the RESETTING@Iscte site.

**Acknowledgments** This work has been developed in the scope of the RESETTING project, funded by the European COSME Programme (EISMEA), under grant agreement COS-TOURINN 101038190. The cloud-based infrastructure (computing and storage) used was provided by the INCD, Funded by FCT and FEDER under project 01/SAICT/2016 nº022153. The current work has also been supported by Fundação para a Ciência e Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through national funds and, when applicable, co-funded by European Union (EU) funds under the project UIDB/EEA/50008/2020.

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<figure><img src="image_106.png" /></figure>
Part IV

**Experience Reports of Smart Life**
**Applications**
Chapter 12
# Leuven: A Smart City Experience

<figure><img src="image_107.png" /></figure>

Check for
updates

Lieve Heyrman, Tim Guily, Rébecca Deneckère, Elena Kornyshova,
and Ramona Elali

**Abstract** Leuven is the eighth-largest city in Belgium with more than 100,000 inhabitants. It is a leading region in the creation of starts-ups, research centers, and applications on health and climate. Numerous organizations in the Leuven region and the city administration itself are currently cooperating in order to work out advanced ideas around Smart City technology, which is also a crucial step in Leuven's goal to become climate neutral by 2030. For this particular goal, the organization Leuven 2030 was founded. Leuven 2030 strives for a climate-neutral future for Leuven, representing inhabitants, companies, civil society organizations, knowledge institutions, and public authorities. They do this by combining science (scientific framework), social power (bringing people together around projects), and storytelling (inspiring stories about the steps taken).

**Keywords** Smart City · Smart City Strategy · Smart City Projects

**12.1 Introduction**

Leuven received the award of “European Capital of Innovation” in 2020. The prestigious award from the European Commission rewards the city that makes the best use of innovation to improve the quality of life of its inhabitants in creating new and groundbreaking solutions to public challenges. The European Commission

L. Heyrman · T. Guily
Department of Economics and Trade, Smart City Leuven, Leuven, Belgium
e-mail: lieve.heyrman@leuven.be; tim.guily@leuven.be

R. Deneckère · R. Elali (✉)
Centre de Recherche en Informatique, Université Paris 1 Panthéon-Sorbonne, Paris, France
e-mail: rebecca.deneckere@univ-paris1.fr; ramona.elali@univ-paris1.fr

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
praised a unique symbiosis of empathy in leadership and structural collaboration between citizens, knowledge institutions, and local organizations to create a brighter future, for itself and far beyond.<fnref n="1" /> The city of Leuven applied for the award in close collaboration with Leuven 2030 and Leuven MindGate.

More recently, in April 2022, Louvain was identified as one over one hundred climate-neutral and smart cities to pursue ambitious goals to slash emissions rapidly and pioneer innovative approaches with citizens and stakeholders.<fnref n="2" /> These cities will be supported by _NetZeroCities_ to bring together all key players within a city, including citizens, academia, and businesses, and implement transformative processes and innovative actions, with the objective of reaching climate neutrality by 2030. The opportunities are to contribute to the European Green Deal ambition to reduce gas emissions by at least 55%, to offer cleaner air, safer transport, and less congestion to citizens, to lead in climate and digital innovation, making Europe attractive for investments from innovative companies and skilled workers.

## 12.2 Smart City Strategy in Leuven

The Smart City unit of Leuven was initiated more than 5 years ago by the head of the Department of Economics and Trade, who noticed within her international network an upcoming relevance in the “smart use of technology” for local policies. From that moment on, a Smart City vision, framework, and strategy gradually developed and modified under the influence of continuous insights and lessons learned through our own experience with Smart City projects but also as a result of local and international Smart City networks that grew over time.

There are several smart key drivers that occur and are important in city policy. (See Fig. 12.1).

First, we can see that there is a change in the demographics that is occurring in the city. People living in the city are becoming older, they live longer, and, in consequence, the number of citizens is growing.

We have rapid urbanization as all these people need to be able to live somewhere. The housing market is very tight in Leuven as houses are very expensive.

All these people use energy, they need to use transportation, and it gives us strain on the traffic and the pollution. Environmental concerns are global, but they are also present at city level.

Climate change over the planet means that the water supplies are going down, and this is a concern also at the city level as there is a high amount of population and we all use these resources from nature. As these resources become scarcer, we have problems finding energy or water resources.

<footnote n="1">¹ https://leuven.be/en/capital-of-innovation</footnote>

<footnote n="2">² https://eurocities.eu/latest/the-100-climate-neutral-and-smart-cities-by-2030/</footnote>
<figure><img src="image_108.png" /><figcaption>**Fig. 12.1** Leuven Smart City drivers</figcaption></figure>

There is also a technical aspect. A lot of people, and more and more people in the future, are using the Internet. This technology and others evolve very fast. This has been an important aspect to take into account when we set up our Smart City strategy.

These are the challenges that the city of Leuven, like other cities, are facing, and we tried to contribute with our Smart City strategy to address all these aspects.

Smart City Leuven uses connected, innovative technologies in the public space to generate and use the gathered data to gain insights into the functioning of the city and into the functioning of all these key drivers. The final goal is to improve the quality of city life so that people are happy to live in the city and feel well supported. We look for how Smart City can contribute to that goal.

Smart Cities also make a turn to data-driven policymaking. Before, policymaking was more based on people's experience, with trials and errors. Now, we combine this information with sensor data to identify if changes are beneficial or not. That is the direction chosen with the Leuven Experience, with gathering and use of data to support the policymaking of the city. This support in data-driven policymaking is provided via several modi: via monitoring and providing insights, via simulation and prediction, and via automatization and control.

At the start of Smart City Leuven, five targets could be defined (See Fig. 12.2):

* Optimization of flows. It can be general, like mobility, energy, waste, and economy.

* Sublime city experience. This represents how you experience the city as a visitor and also as a resident. It has to do with tourism and its economy.

* Harbor and grow talent. There are projects to support students to develop their talent.

* Smart health and Wellness. We focus here on environmental factors such as noise, heat, and so on.
<figure><img src="image_109.png" /><figcaption>Fig. 12.2 Overview of Leuven Smart City projects</figcaption></figure>

* Smart City services. Internally but also related to visitors and residents living in the city to offer more smart services. This has more to do with the digitalization that is always growing.

## 12.3 Smart City Projects in Leuven

Figure 12.2 shows an overview of the Leuven Smart City projects, related to these targets. Also, the main focus of the projects throughout the different targets is defined: data of course, how it can make the infrastructure and connectivity smarter, and what the innovation attitude is. Also, some projects specifically focus on “citizen science” by involving our citizens in data gathering for the city’s benefit.

Over time, the need arose to rearrange the Smart City targets, since Smarter City services concern more with digitalization and IT projects, and harbor and grow talent is also handled in other city projects. Therefore, the current main three targets of the Leuven Smart City strategy are smart health and wellness, optimizing flows, and sublime city experience. We will go further into detail on each one (Fig. 12.3).

### 12.3.1 Optimizing Flows

**Shop & Go Project** The Shop & Go project is concerned with the optimization of flows and shared mobility. In some Leuven areas, you do not need any parking disc, parking ticket, or resident's card. Sensors on the floor of the parking area record
<figure><img src="image_110.png" /><figcaption>**Fig. 12.3** Selected Leuven Smart City projects</figcaption></figure>

your hour of arrival, and if you exceed the parking time, a parking attendant will arrive on site and you will receive a parking fee.

**Hoppin/eHUBS** Hoppin is a project embedded in a European project. It combines shared mobility and packaging. When shopping online, you can choose to have your package delivered to some specific place. This project combines different facilities related to shared mobility and delivery of packages into one eHUBS spot. eHUBS are on-street locations that bring together e-bikes, e-cargo bikes, and/or e-cars, offering users a wide range of options to experiment with and use in various situations. The idea is to give a high-quality and diverse offer of shared electric mobility services to dissuade citizens from owning private cars, resulting in cleaner, more liveable, and pleasant cities. The eHUBS in Leuven are characterized by the Hoppin polls (digital and analogs), which refer to the branding of eHUBS foreseen by the Flemish strategy that aims at deploying 1000 eHUBS in the Belgian region by the end of 2024.

**Mobility as a Service** A service in which different mobility services are offered to the user through one application. In this way, the user has access to the various mobility services. By using this application, the user can plan his transport modes. First steps toward this application were taken in a project funded by the Flemish government, and a follow-up project is currently ongoing.

**Citizen Science—WeCount Project** WeCount<fnref n="3" /> is a European project that enables citizens to initiate a policymaking process with fully automated measurement data in the field of mobility and air quality.

<footnote n="3">³ https://www.we-count.net/</footnote>
The community building process of WeCount was based on previous participatory processes, whether initiated by the local government or by the citizens themselves. Steps were taken to gain a good understanding of the local mobility context, supported by an existing rich participatory context organized by the city government and initiated by citizens.

Low-cost sensors are placed, by citizens, on their house's front windows. This sensor contains a small camera, and it counts what passes by. In particular, it can differentiate bikers, cars, trucks, and pedestrians, so it is used as a traffic counter.

This is a nice example of how citizens can contribute to the city's policymaking by gathering the data themselves. They were just asked to commit to this project, and then they received a low-cost sensor. The only thing they had to do was to plug it in and connect it to their Internet, and so the data came in and was used, in this case as one of the data sources for a new local circulation plan. This data gathering benefited the citizens themselves, as the studied area is very small; however, there are a lot of small roads but also a lot of people (families and children). There was a problem with the traffic and the use of the streets, and people were very concerned about how the traffic would be guided through their streets. This project allows them to be able to do something about gathering data themselves.

### 12.3.2 Smart Health and Wellness

Smart health and wellness is one of the main target strategies that we have for Smart City. Two citizen projects within this target will be presented in the next session: Leuven.cool and Night Noise project.

**Project Leuven.cool** It is one of the citizen science projects<fnref n="4" /> currently running in the city. In collaboration with the KU Leuven (who has the lead in this project), the Royal Meteorological Institute, and climate Leuven2030, this project studies the heat island effect in the city. The project aims to map the moderating effect of green elements in the city and in the gardens of citizens. The urban microclimate is created by the interaction between the urban surface and the atmosphere. The physical processes that occur are the result of energy and water exchange between the urban surface and the atmosphere, generating differences following the layout of the urban landscape. This project was set up after a heating up in the city with a higher temperature inside than in the green areas and urban surroundings had been noticed. We wanted to discover how likely the presence of green areas and water zones could have a beneficial impact on the heat island effect.

<footnote n="4">⁴ https://www.exello.net/nl/kennisdeling/leuvencool</footnote>
Over 100 weather stations are placed at different public places and private gardens in the city center and its surroundings (real time maps directly accessible online<fnref n="5" />).

Based on the data provided by these weather stations, a heat map and a network map were created, where the temperature was represented through a color code in order to be able to compare the temperature within the city center with the surrounding areas. The data obtained from those stations are used for scientific and research purposes but also at the city policy level as it allows to monitor heat spots that can be used as a starting point for beneficial interventions such as creating more green areas and introducing more water spots.

**Night Noise Project** The purpose of this project is to tackle the nightly noise using technology and nudging. Leuven is a lively city, and there are around 50,000 students staying and studying, which gives a nice atmosphere. However, students like to go out and have some fun, the same as the local residents. Hence, we have a lively nightlife, which may produce nightly noise in transit streets, which are the routes taken by students or residents while going home. Thus, this causes a problem for the local residents living in these streets because they are disturbed in their sleep. Different decisions were taken to tackle this problem, such as police control and direct intervention, by giving penalties in a repressive way but also by creating campaigns in a preventive way, with limited effect. Tackling night noise is challenging because it occurs only for a relatively short period. When a group of people passes through and makes noise, by the time a local resident calls the police to complain about the noise and the police come, the noisy people are already gone! In addition, the noisy people are not always aware that other people are sleeping in these streets, as at night the streets are isolated, which may give the feeling that no one lives there; hence, for them, there won't be any problem in making some noise.

Therefore, in this project, we combine technology and nudging techniques as a possible solution for this problem. There are three targets:

1. *Get insights into the nightly noise* with more objective data because we don't know how likely the reported noise is loud, disturbing, or annoying or when it is occurring, on which days, or at which times. So we need to get more insights about the night problem itself to objectify the first target.

2. *Classify sources of nightly noise.* There are different sources of night noises. Some of the noise is produced by traffic; then, it is difficult to have the police intervene or to use nudging techniques in such a case. Some other noises can be produced by humans directly such as singing and shouting, breaking glass or punching a garbage bag with the leg, playing music on a loud sound, etc. For this source of noise, nudging techniques might have a positive influence.

3. *Nudge to influence behavior.* Nudging is a motivation technique to stimulate and/or influence people in a subtle way to perform expected behavior. So it is

<footnote n="5">⁵ https://leuven.cool/</footnote>
<figure><img src="image_111.png" /><figcaption>**Fig. 12.4** Night noise identification</figcaption></figure>

used to direct people to the expected behavior, in this case being more silent when passing through the streets at night.

The VLAIO City of Things is a recurrent funding initiative of the Flemish government and is funding the Night Noise Project. We set up this research project in cooperation with the local police and the local prevention service. The project focusses on a specific street called Naamsestraat nr.40 until 100. After a positive evaluation, this project will be resubmitted for new funding to extend to other streets hindered by nightly noise in Leuven.

Several noise sensors are placed in a 300-meter area between the Heilige Geestcollege and the arts center STUK. These sensors do not record conversations, but they do analyze all nocturnal sounds: what the volume is, how high or low does it sound, how long does it last, and if it comes from traffic, people, or music. There is also a meter in the city park, inaccessible overnight, to compare the noise measurements with a quiet zone. In addition, the residents are asked to report via an app when they experience night noise (what kind of noise, the time, the duration of the noise, whether their window was open or not). By combining the noise analysis results with residents' reports, the system can better identify which sounds are perceived as disturbing (Fig. 12.4).

In the next step, we found a way to classify the sources of nightly noise with an AI-trained model. The image below represents sensor data obtained between 10:00 p.m. and 6:00 a.m., with the source of noise peaks classified as either human voice shouting or a traffic. Preliminary data analysis showed that part of the noise peaks come from an ambulance since a hospital is located in this street. However, it is difficult to control this type of night noise; therefore, this project focusses on sources of noises on which we can intervene (Fig. 12.5).
<figure><img src="image_112.png" /><figcaption>**Fig. 12.5** Night noise data analysis</figcaption></figure>

As in the image above, we are able to convert the data into a harmonica index to find out the intensity of the noise. Green indicates low intensity, orange indicates medium intensity, and red represents high intensity of noise. The first diagram is from a sensor in Naamsestraat. We can see how the orange color is dominant. The second diagram is from the sensor attached inside the park quiet zone. We can notice how the green color is dominant here. In addition, we can detect, from both diagrams, when the peak time and day are for the noise.

Since we have data from the sensors, we can process these data for nudging purposes in our Smart City Data Platform.

### 12.3.3 Sublime City Experience

**Smart Sporting Cities** The last project is the smart sporting cities that consist of monitoring sports and exercise behavior in the public space using AI technology. Sport Vlaanderen and the city of Leuven want to gather insights about the growing group of recreative athletes (athletes who work outside clubs) to strengthen the policy around them and thus encourage even more residents to exercise and exercise. The project, which was realized with the support of the innovative Public Procurement Program (PIO), will be launched in two pilot gardens in Leuven (Fig. 12.6).

In the first experimental garden in de Bruul Park, the sports infrastructure of the park is specifically monitored. The green park in the center of Leuven has a multipurpose lawn, a football cage, and a sports wall, where tennis and urban fitness can take place. The technology will have a permanent place until the summer of 2024 to monitor sports behavior on a permanent basis.

The second testing garden is the Philips site at the Leuvense ring. Mobile technologies are tested there to allow the measurement equipment to be moved to other sites after a certain period of time. Thanks to a solar panel and 4G/5G connection, these applications are independent of existing power and data facilities in public spaces. The Philips site focuses on the multipurpose lawn and the bar park between the sports center Sportoase and the Leuvense ring.
Fixed set-up - Bruulpark

<figure><img src="image_113.png" /><figcaption>**Fig. 12.6** Smart sporting city in Leuven</figcaption></figure>

Mobile set-up - Philipssite

<figure><img src="image_114.png" /></figure>

**12.3.4 Smart City Data Platform**

All data coming from the different types of sensors (city wireless environment, waste management, parking, urban mobility) have been integrated into the Smart City Data Platform (Fig. 12.7). It is then possible to standardize the data between all sensors to have a cross-sharing experience of information of streams to provide an efficient, effective, and scalable platform (which can be used in other projects and in other cities), to have governance with a cost-sharing mechanism, and to share data between all cities. This integration of sensor data for different purposes is now possible even if it is not yet operational today.

We have two main layers in this platform: the IOT layer and the data layer. In the data layer, the data is being stored on Azure; thus, we can do some analysis on the data to get insights.

In the second phase, the city tested various nudging techniques. Nudges are psychological techniques that often unconsciously and in a simple way encourage people to change their behavior. For example, (Fig. 12.8) when one of the noise meters catches nighttime noise, public lighting can be dimmed. A message can also be projected on the ground between 11:00 p.m. and 5:00 a.m. in order to make noise makers aware that they are waking someone up. The city chose to first experiment with the dimming or the intensification of the public lighting in the project area of the Naamsestraat. The noise sensor detects noise peaks with the label “human shouting,” or the residents could use the app to report the night noise. Then, an AI algorithm processes this data, and a trigger is sent through the Smart City Data Platform in order to dim or intensify public lighting. Consequently,
<figure><img src="image_115.png" /><figcaption>**Fig. 12.7** Leuven Smart City Data Platform</figcaption></figure>

<figure><img src="image_116.png" /><figcaption>**Fig. 12.8** Data Platform usage for night noise management</figcaption></figure>

a different atmosphere is created in the streets, which might indirectly influence people's behavior.

These so-called incidental nudging techniques are combined with more semi- and permanent nudges such as floor stickers and projections on streets indicating that it is a sleeping zone. Preliminary results of the project suggest a positive effect of the nudging techniques on nightly noise; however, more data need to be gathered to draw more conclusive conclusions.
**12.4 Conclusion**

The Leuven Experience is still an ongoing project that will integrate a lot of other sub-projects in the years to come. For most of the projects, there was an analysis on the data to analyze for impact: for the nightly noise project, we made a statistical analysis to measure the impact on the nightly noise of the intervention. For the project mobility, we measured mobility streams at different moments and use this on our traffic model to measure impacts of decisions and interventions. We didn't plan to do any surveys as they are quite costly in terms of money and staff (and not repeatable over the course of time).

**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_117.png" /></figure>

Chapter 13
# Enhancing the Visitor Experience with Immersive Technologies and Gaming: The Monserrate Use Case

Carlos Portugal, David Vaz, Miguel Sales Dias, Pedro Trocado, Alcina Prata, and Fernando Brito e Abreu

**Abstract** Immersive technologies, such as augmented reality (AR) and virtual reality (VR), are effective technologies being implemented across diverse fields. We propose an AR/VR-based Web app to be applied in the cultural tourism field with the objective of enhancing the visitors' experience of the Monserrate Palace, part of a UNESCO cultural landscape located in Sintra, Portugal, managed by Parques de Sintra - Monte da Lua SA.

This chapter overviews the state of the art on immersive technologies in cultural
heritage sites and the visitor experience at these sites. Then, it exposes the palace's
historical and cultural significance and some of the challenges it faces in engaging
its visitors. The potential applications of immersive technologies in the palace are
explored, including interactive storytelling and immersive experiences that allow
for a higher level of visitor engagement. Next, it addresses potential challenges
and limitations of deploying the proposed solution in the palace, such as technical
constraints and preservation concerns. It then concludes with a reflection on the
significant potential of AR and VR in enriching the immersive experience of
Monserrate Palace, offering new possibilities for engaging with cultural heritages
and creating a memorable and enjoyable visit for modern audiences.

**Keywords** Smart tourism · Augmented reality · Virtual reality · Gaming · 360° Imaging · Tracking · Cultural heritage site · Monserrate Palace · RESETTING

C. Portugal (✉) · D. Vaz · M. Sales Dias · F. Brito e Abreu

Iscte - Instituto Universitário de Lisboa, Lisboa, Portugal

e-mail: carlos_portugal@iscte-iul.pt; david_vaz@iscte-iul.pt; miguel.dias@iscte-iul.pt;

fba@iscte-iul.pt

P. Trocado

Parques de Sintra - Monte da Lua S.A., Sintra, Portugal

e-mail: pedro.trocado@parquesdesintra.pt

A. Prata

IPS - Instituto Politécnico de Setúbal, Setúbal, Portugal

e-mail: alcina.prata@esce.ips.pt
**13.1 Introduction**

Cultural heritage sites are an important part of the identity and history of each nation, making their preservation and sharing crucial to uphold the traditions and their customs. In recent years, new technologies have been introduced to these sites to enhance the visitors' experience (Jung et al. 2020; Koeva et al. 2017). In this chapter, we will focus on immersive technologies, such as virtual reality (VR) and augmented reality (AR), that can enrich the experience of the visitors. Their objective in this context is to provide a high degree of interactivity, allowing users to engage with their content in a meaningful and memorable way, and in more specific cases, VR provides an elevated sense of presence in virtual worlds and allows its users to interact with virtual objects, mimicking real-life situations more accurately than other media, while AR enhances the user's perception of reality by overlaying virtual elements onto the real world. Thanks to these features, immersive technologies offer a more immersive and engaging user experience (Chiu et al. 2019) while also enhancing the learning experience of its users (tom Dieck et al. 2016). With all these benefits, these technologies can bring innovation in various fields, such as healthcare, entertainment, education, training, tourism, etc.

Given its broad implementation and adoption in various fields, it is often no surprise that immersive technologies have also been extended to tourism and, in the case of this chapter, into cultural heritage sites. One way these have manifested is through virtual tours where users experience the real-world location in an immersive simulated world. Another way is through reconstruction of physical heritage artifacts/rooms into virtual ones, allowing the visitor to interact with them while also preserving them. Furthermore, augmented virtual guides and information overlays have been widely used to provide visitors with greater amounts and quality of information about the sites they visit.

While these technologies have already begun to be implemented in cultural heritage sites, there are still challenges to be addressed. Our main goal is to design on-site immersive experiences that are both engaging and rewarding for visitors in order to improve tourism experiences.

In this chapter, we report a case of usage of immersive technologies designed for Monserrate Palace, part of a UNESCO cultural landscape located in Sintra, Portugal, aiming at enhancing the visitor experience. This initiative is being carried out under the framework of the COSME RESETTING project.<fnref n="1" />

This chapter is structured as follows. In Sect. 13.2, we present the findings of our literature review, which highlights factors affecting cultural heritage site experiences and the role of immersive technology in enhancing visitor engagement. In Sect. 13.3, we provide a historical background of the Monserrate Palace, with a rich history dating back to before Sintra's reconquest. In Sect. 13.4, we detail the design of the “Monserrate in the Cook family era” gaming app, highlighting the game design, the

<footnote n="1">¹ Relaunching European smart and Sustainable Tourism models Through digitalization and innovative technologies.</footnote>
user-centered prototyping approach, and the preliminary results of our proposed techniques that enable the immersive visitor experience. In Sect. 13.5, we draw conclusions and describe our plans for further research.

## 13.2 Literature Review

### 13.2.1 *Visitor Experience at Cultural Heritage Sites*

Several studies have shed light on the various factors that contribute to a positive visitor experience at cultural heritage sites. One of the key factors identified is the quality of the visitor experience, perceived value, satisfaction, and sustainable behavior. For instance, one paper found that the quality of experience directly influences the perception of value and satisfaction, ultimately impacting behavioral intentions (Chen et al. 2010), while another discovered that perceived quality and emotions play direct roles in determining visitor satisfaction, with the individual's mood state serving as a moderator along the cognitive path to satisfaction (de Rojas and Camarero 2008).

In addition to these findings, Buonincontri et al. (2017) proposed a conceptual framework that integrates visitors' heritage experiences, their attachment to heritage sites, and their engagement in sustainable behavior. This framework highlights the interconnections between visitors' experiences, emotional connections to the site, and their inclination toward sustainable practices, ultimately shaping their overall experience.

Moreover, Abuamoud et al. (2014) conducted a study focusing on the factors influencing tourists' willingness to travel to cultural heritage sites. They discovered that aspects such as education, variety of sites, multiple destinations, cost, and the reasons for visiting significantly impact tourists' decision-making process. These findings underscore the importance of considering diverse elements when designing and promoting cultural heritage sites to attract visitors.

Furthermore, Alazaizeh et al. (2019) explored the role of tour guide performance in enhancing visitor sustainable behavior, consequently contributing to a positive visitor experience. The study revealed that tour guides have both a direct and indirect effect on visitors' sustainable behavior, thereby shaping their overall experience.

By considering these research findings, cultural heritage site managers can better understand and address the key factors that influence visitor experiences. Focusing on factors such as quality, perceived value, satisfaction, emotional engagement, sustainability, and the role of tour guides can lead to a more holistic and enriching experience for visitors, ensuring their lasting enjoyment and appreciation of the cultural heritage site.
### 13.2.2 Immersive Technologies in Cultural Heritage Sites

Immersive technologies, such as VR and AR, have the potential to enhance visitors' engagement with cultural heritage sites and improve learning outcomes. Additionally, VR and AR can aid in the preservation and conservation of heritage sites by digitally documenting and safeguarding fragile structures or artifacts.

A systematic review conducted by Yung and Khoo-Lattimore (2017) found that the most common use of these technologies was in marketing and in tourism education, as well as highlighting some gaps and challenges in the adoption of such technologies, these being the awareness of the technology, usability, and time commitment to become proficient.

Another paper identified AR-related factors that influence the user's satisfaction and, with those factors, presented a user experience model with product features for a user-centered interface design for AR applications (Han 2017).

Some other studies focused on the examination of the learning outcomes on the use of these technologies. One of them compared a control group whose tour was conducted through traditional approaches while the experimental group conducted its tour with the aid of an AR system using image-based recognition. The experimental group exhibited significant differences and improvement in the learning outcomes (tom Dieck et al. 2016). Another study found that just providing instructions to the visitors with a mobile application made their experience more valuable and recommends the use of new approaches to visitor engagement and experience enhancement (Chiu et al. 2019).

An interesting use case found was a Web portal with the integration of high-resolution spherical panoramas and information representing the cultural heritage in Bulgaria (Koeva et al. 2017) that concluded that the visual and metric qualities of those panoramas are sufficient for many applications, namely, tourism, documentation, and demonstration of cultural objects.

The effectiveness of a VR/AR application named *JejuView* was evaluated to advertise the cultural heritage of Jeju Island to tourists. The authors found that when using immersive media, consumers are more focused on the hedonic value than on the usefulness of the medium (Jung et al. 2020).

These technologies can be used to recreate life inside historical sites, as Bruno et al. (2010) and Gaitatzes et al. (2001) discuss the use of VR in cultural heritage, including the creation of immersive virtual environments and interactive virtual archaeology projects. Additionally, Noh et al. (2009) provides an overview of AR in virtual heritage systems, which can be used to reconstruct historical buildings and monuments. Similarly, Younes et al. (2017) contributes to the discourse by presenting a case study on the construction of a computerized model of the Roman Theater of Byblos, which includes both VR and AR applications.

Overall, these studies suggest that immersive technologies can be effective tools for enhancing visitors' engagement with cultural heritage sites and improving learning outcomes. However, there is still a need to be cautious about certain aspects of the application to ensure the success of its implementation.
### 13.2.3 Gamification

Gamification is the process of applying game mechanics, such as points, badges, leaderboards, etc. Saleem et al. (2022) to non-game contexts, aiming at motivating users' behavior and enhancing participation and productivity. This strategy has gained attention from researchers and has become popular as a method for engagement in various contexts (Aulia et al. 2022; Mora et al. 2015).

One of these contexts is education, where gamification has been used to generate more engaging educational environments. Saleem et al. (2022) reports on the increase in acceptance of gamification in e-learning and indicates that the most common game elements used in this context are points, leaderboards, badges, and levels.

Other contexts that have adopted gamification are tourism and hospitality. Pasca et al. (2021) identified five themes in tourism and hospitality, where gamification is relevant: edutainment, sustainable behavior, engagement factors, service provider-generated content, and user-generated reviews. The cross-analysis of these themes also revealed some key components of gamification mechanics, including affordances, behavioral and psychological outcomes, and benefits.

Hamari et al. (2014) suggests that gamification does work, but with some caveats. These authors claim that the majority of the reviewed studies yielded positive effects from the adoption of gamification. However, most of the quantitative studies concluded on positive effects that exist only in part of the considered relationships between the gamification elements and studied outcomes.

In conclusion, gamification is a promising approach to motivate user behavior and enhance participation and productivity in various scenarios, including education, tourism, and hospitality. However, the effectiveness of gamification is dependent on the context in which it is being implemented and on the characteristics of the users.

## 13.3 Monserrate Palace Brief Historical Background

The Monserrate Estate is believed to have had a chapel on its grounds before D. Afonso Henriques's reconquest of Sintra back in 1147. In 1540, Gaspar Preto, a cleric, commissioned the construction of a chapel dedicated to Nossa Senhora de Monserrate, which at that time belonged to the Hospital of Todos-os-Santos in Lisbon. The property was leased to the Melo e Castro family in 1601 and purchased by D. Caetano de Melo e Castro in 1718. The property was kept by caretakers until 1755 when the great Lisbon earthquake left the houses uninhabitable. In 1790, the property was leased to Gerad De Visme, a wealthy English merchant, who built the first neo-Gothic palace and demolished the sixteenth-century chapel. William Beckford leased the property from De Visme in 1793 and invested heavily in the palace and gardens. The property remained in the hands of the Melo e Castro family
until 1863 when it became the property of Francis Cook, a wealthy English textile millionaire. Cook made Monserrate the family's summer residence and employed a considerable amount of people to care for the house, park, and family. He also bought neighboring properties and the Convento dos Capuchos, becoming a major employer in the surrounding area. In 1947, the Cook family sold the property due to financial difficulties, and it was purchased by Saúl Fradesso da Silveira de Salazar Moscoso Saragga, a Lisbon antiques' dealer, who sold it to the Portuguese state in 1949 along with 143 hectares of the Tapada de Monserrate. In this sale, the valuable contents of the palace were scattered during an auction (Neto 2017, 2015).

This palace has also hosted another project to enhance the visiting experience, which was *FalaComigo*. This project involved the creation of interactive virtual characters, namely, Sir Francis Cook and a fictional butler named Edgar Smith. The visitors could interact with these characters and other audiovisual content through touch and speech, using a touch table, an interactive kiosk located in some palace rooms, and a smartphone app, which would help capture the visitor's attention. The project provided the visitors with a new attractive and interactive way to acquire the history of the palace (Neto and Neto 2012) (Fig. 13.1).

The Monserrate Palace is rectangular in plan and mostly symmetrical on its two axes (see Fig. 13.2). The two ends of the building are circular towers whose interiors are appropriately circular rooms. One of these towers can be understood as the face of the palace since it is the first face we see when arriving at the palace (assuming

<figure><img src="image_118.png" /><figcaption>**Fig. 13.1** Monserrate Palace entrance</figcaption></figure>
<figure><img src="image_119.png" /><figcaption>Fig. 13.2 Room layout of the ground floor of the Monserrate Palace</figcaption></figure>

we come from somewhere other than the garden). Inside this first tower, we find the main atrium that functioned as the main entrance for those arriving at the palace for the first time and gave access to the main bedroom. In the opposite tower is the music room. This tower has no access to the outside, effectively being understood as the end of the palace. The large corridor that connects these two towers serves as the main axis of circulation, also giving access to the various rooms located between the two ends of the palace towers. These rooms all have the same morphology, a rectangular plan whose larger sides have windows (on one side) and a door to the central corridor (on the other side). These four rooms are arranged in pairs, symmetrically facing the corridor: the dining room and library and the living room and billiard room. Between these four rooms, we have the palace's central point, the midpoint of the corridor that connects the circulation space to the two side entrances. This point is an octagonal plan space, and, in the center, we find a fountain (whose plan is an offset from the larger octagon). The palace is oriented with its major sides perpendicular to the northwest and minor ones to the southeast.

## 13.4 Designing an Immersive Game for the Monserrate Palace

The Monserrate palace faces some challenges in appealing the visitors with an on-site experience that could recreate the indoor spaces at the time of the Cook Family, since such spaces are now essentially empty, ever since the 1949 auction. In this context, we designed a solution based on an immersive gaming user experience (UX), named *Monserrate in the Cook family era app*. This gaming app was designed specifically for use on the smartphones of visitors inside the Palace of Monserrate, to complement and enrich the visit. The focus of the game will be to convey what it
would be like to live in the palace in the era of the Cook family, at the beginning of
the twentieth century.

When visiting the palace, a visitor is confronted with a lack of furniture. Analyzing historical photographs,<fnref n="2" /> taken inside and outside the palace around 1902 (David Knights-Whittome 1902), made available to the authors by the Monserrate palace, we can see the fully furnished first-floor rooms and the Cook family in an outdoor enclosure of the palace. From these photos, we get a more faithful view of the Cook era, something that has been lost today, in part due precisely to the lack of furniture (and obviously the residents themselves, the Cooks). Although these photos portray a more faithful perspective of the palace, they are just photos. There is always a discrepancy between seeing the world from two-dimensional photos and experiencing the world in three dimensions, i.e., physically. For this, the introduction of immersive environments (VR and AR) with a mobile (smartphone)-based user experience, within a gaming framework, is suggested.

Given the availability of early-twentieth-century photos for certain rooms, the
development of our solution will be focused primarily on the rooms located on the
first floor of the palace. This can be observed in Fig. 13.2, depicting the room layout
of the first floor, with locks indicating the specific rooms where the solution will be
implemented.

**13.4.1 Gaming Experience Proposal**

After some discussions and brainstorming with tour guides, specialists of the romantic period of the mid-nineteenth century, and palace curator and bibliographic review, we came up with some ideas that could be explored in the game.

One of the ideas that stood out as we reflected on the world illustrated in the 1902 photos, compared to the real world of today, is the day-to-day experience of the Cook family when they were visiting the Palace of Monserrate during vacation, in the dawn of the twentieth century. We can thus focus the basic theme of the game on an event that occurred daily such as “the Cook family mealtime.”

The Cook family's way of life inside the palace was quite particular, with customs that no longer exist today and in accordance with the upper-class British lifestyle of the time. Some of the spaces were segregated by gender. For example, the “Billiard Room” was dedicated to men, as a smoking room too, and the “Living Room” (“Indian Room”) to women in the family. The life of this family was thus much more “ritualized” than the family life of today.

Our gaming experience explores the following scenario. At noon, the Cook
family returns from an early morning walk through the Monserrate gardens,
enjoying the exotic nature and the panoramic views of the surrounding landscape,

<footnote n="2">² These photographs are part of the Monserrate Palace private collection and were taken by David Knights-Whittome, at the Palácio de Monserrate, in the early twentieth century (circa 1902).</footnote>
<figure><img src="image_120.png" /><figcaption>**Fig. 13.3** Proposed visitors’ trajectory</figcaption></figure>

and enters the palace through the “Garden Entrance,” where some members of the family meet others coming down from the upper floor by the monumental staircase. Together and in a single line, they make their way through the “South Gallery” and into the “Dining Room,” passing by the “Library,” a room set aside for studying and reading, where Sir Cook is also busy dispatching business matters. Sir Cook joins the group, and together they enter the “Dining Room.” After a pleasant light lunch, the family leaves the room and walks through the “South Gallery” toward the “Octagon” and then through the “North Gallery” and divides into two groups: the female group goes to the “Indian Room,” left, where they socialize and discuss social topics, and the male group goes to the “Billiard Room,” right, where they discuss contemporary political and business topics, in a relaxed atmosphere, and where they also play billiards and smoke cigarettes and cigars. Later, both family groups gather again in the “Music Room” to enjoy the rest of the day while appreciating the music of that era.<fnref n="3" />

For a game that addresses this theme, the visitor may be invited to walk through the rooms, starting from the south wing, going into the “Library” first, followed by the “Dining Room,” “Indian Room,” and “Billiard Room,” and finishing in the “Music Room,” according to the ritualized order already mentioned. Visitors will also have the freedom to enter any room of their choice and deviate from the suggested path if they prefer. Once a visitor enters one of the previously mentioned rooms, he/she will be prompted to complete certain challenges. Figure 13.3 provides an overview of the proposed app’s sequence, and Fig. 13.4 depicts the Business Process Model flowchart of the proposed solution.

<footnote n="3">³ An example could well be Antonín Dvorák’s “Serenade in G major, Op. 95” on piano and violin, very popular at the time, played by the palace’s resident pianist.</footnote>
<figure><img src="image_121.png" /><figcaption>Fig. 13.4 Business Process Model and Notation (BPMN) flowchart of the proposed solution</figcaption></figure>

### 13.4.1.1 First Challenge: Match 1902 Photos That Correspond to the Visited Room

In this challenge, when entering one of the rooms of the palace, the visitor is invited to use the app to browse through the gallery of the palace's historical photos. The player will have to choose the photo that corresponds to the room he or she is in, from auditory and visual clues of the room today. For example, when entering the "Billiard Room," the visitor would have access to a sound ambiance (either in egocentric mode—only for a given visitor—or exocentric, for all the visitors present in the room), reflecting what is going on in the room. As an example, in the billiard/smoking room, there could be the sound of men talking to each other and the billiard balls hitting the corners of the table. As far as visual clues are concerned, to be able to identify the correct picture, the player will have to pay some attention to the room in front of him, noticing the details of the ceiling, the floor, and the walls, since there is no furniture. There are some rooms where the choice is easier. For example, the library is the only room with bookshelves lining the walls, and the music room is the only one that has a different shape from the other rooms. Other examples are not so clear to identify, such as the living room, which will have to be identified based on the ornamentation of the walls. Each time the player chooses the correct picture, he or she receives a reward that corresponds to a brief overview of the historical and cultural significance of the room, enabling the visitor to assimilate information that may have been neglected otherwise.

### 13.4.1.2 Second Challenge: Discover the Photographer's Position

In this challenge, after choosing the photo that corresponds to the respective room, the game focuses on getting the visitor to understand what the pose (position and orientation) of the photographer was when he took the photo. The visitor moves around the room and uses his/her cell phone as a "sonar": as he/she approaches the correct position and orientation, the smartphone will vibrate more intensely until success is achieved. To accomplish this feedback interaction, there will be indicators situated inside each room to get the initial position of the visitor, which, combined with the sensors of the visitor's device, enable the tracking of the user throughout
the room. When arriving at the required pose and position, the visitor will receive a reward, which is a VR experience based on 360° images on the smartphone, where he/she will observe a panoramic image of the current room merged with the image(s) of the room collected in 1902. The visitor is then invited to participate in the next challenge.

### 13.4.1.3 Third Challenge: Discover Room Curiosities

In this third challenge, the visitor must discover a set of curiosities and associated information, which can be found in the room of the previous challenge. Thus, the app would invite the visitor to discover “where the curiosities in this room are.” As an example, the music room has some wall busts that belong to muses and goddesses. By pointing the phone at different parts of the room, the player would observe points of interest and could click on the screen to reveal information about what he or she sees. For example, if the visitor points the phone at one of the busts previously mentioned and clicks on the screen to find out who they are, the app will display the pertinent information. After finding these curiosities, the player is rewarded with an AR experience, where a virtual 3D scene will augment the physical space. The challenge in realizing this idea has to do with the fact that we select curiosities and objects that have an important connection to the Cook family’s past and the era we are portraying.

## 13.4.2 User Interface Design

We adopted a user-centered design approach. In fact, to demonstrate the feasibility of our solution and to get feedback on the proposed design and functionalities, we start by designing a user interface prototype. The prototype serves as a visual representation of our ideas and allows us to validate our assumptions and gather feedback from experts and end-users (visitors) and make informed decisions toward the development of a minimum viable product of our gaming app. All this occurs before the beginning of the software development of the solution, so mistakes or wrong assumptions do not become costly to modify, since it is much cheaper in terms of resources to modify the user interface prototype than to modify the end product.

A low-fidelity paper prototype was first designed to get a broad idea of how the interface would look like, which can be seen in Fig. 13.5, and it was shown to two experts who had the chance to interact with it, via a “human-computer” interaction paradigm (Rettig 1994).

After collecting some feedback, we improved the prototype and developed a high-fidelity prototype version with the help of Figma, a user interface design tool that also allows for the creation of interactive prototypes that can be seen in Fig. 13.6. The high-fidelity prototype allows us some degree of interaction such as
<figure><img src="image_122.png" /><figcaption>Fig. 13.5 Low-fidelity user interface prototype</figcaption></figure>

<figure><img src="image_123.png" /><figcaption>Fig. 13.6 High-fidelity user interface prototype</figcaption></figure>
<figure><img src="image_124.png" /><figcaption>**Fig. 13.7** Color palette</figcaption></figure>

navigating the existing pages and some limited animations; this will be quite helpful when performing tests with the end-users since it will be much closer to the end product.

The prototype has been designed with the user in mind, and it aims to provide a simple and natural user experience. Some design choices made were, for example, the color palette that is illustrated in Fig. 13.7, derived from colors present in the palace while maintaining a pleasing visual experience, keeping things simple and easy to use, minimizing the learning curve and the implementation of some feedback mechanisms.

### 13.4.3 Proposed Software Architecture

#### 13.4.3.1 Toolkit Architecture

In Fig. 13.8, there is the component diagram of the STToolkit that will be used to build the proposed solution. We will use Unity and WebXR. Unity is a game engine that provides powerful tools for creating and rendering 3D and 360° imaging environments that can be explored in VR and AR user experiences, while WebXR is an API for Web content and apps that need to interface with VR and AR software and hardware. By combining these tools, our solution will only require end-users to access the envisaged immersive experience via a Web browser.

#### 13.4.3.2 Proposed Software Deployment

Our solution's system architecture is illustrated in Fig. 13.9. A server will host the Web service as well as the database, which means that users can access the content of the app without downloading an application to their devices. The client will need to provide data from its camera and from its inertial measurement unit, which uses a combination of accelerometers and gyroscopes to calculate changes in position and orientation and will support a graphical user interface, also featuring user experience with AR and VR based in 360° imaging.
<figure><img src="image_125.png" /><figcaption>Fig. 13.8 Component diagram of proposed XR STToolkit</figcaption></figure>

<figure><img src="image_126.png" /><figcaption>Fig. 13.9 Deployment diagram of proposed solution</figcaption></figure>
<figure><img src="image_127.png" /><figcaption>Fig. 13.10 Results of colorization tests</figcaption></figure>

### 13.4.4 Feasibility Study

In preparation for the full software development of the proposed Web app, several preliminary tests were conducted, some of them involving image-stitching and colorization of historical photographs. In more detail, using a high-quality camera and specialized software PTGui, a series of images of the interior of the palace were captured by the authors in arbitrary poses and then stitched together to create a seamless panorama, which then was also merged with the historical photographs, made available to the authors. Another test, represented in Fig. 13.10, entailed the use of Photoshop's neural filter to automatically colorize the historical photograph.

In Fig. 13.11, we depict the results of applying both automatic colorization and image-stitching in panorama images of the library room, comparing situations before and after applying the effect. Results are promising, but we can see some limitations of this approach. We do not have yet knowledge of the original pose of the 1902 photograph nor the camera's intrinsic parameters, and today's situation image panorama was captured in an ad hoc manner. Therefore, image distortion is perceived. This will be corrected with better 360° imaging capture and also an estimation of the 1902 photograph camera pose and its intrinsic parameters, which is underway.

## 13.5 Conclusions and Future Work

In this chapter, we have described the steps that have been taken to attempt to reach our main goal, specifically with the design of a solution for the Monserrate Palace, part of a UNESCO cultural landscape located in Sintra, Portugal, more specifically, a Web gaming app.
<figure><img src="image_128.png" /><figcaption>**Fig. 13.11** Sections of the resulting panorama from image-stitching tests</figcaption></figure>

We performed a literature review that shows that immersive technologies (VR and AR), when properly implemented, can be very beneficial to the visitor experience at cultural sites, by providing tourists with a more engaging and interactive experience, and can enhance their appreciation of the site's cultural and historical significance.

With this analysis at hand, we proceed to design an immersive gaming app to enhance visitor experience at the largely unfurnished Monserrate Palace. Referred to as the “Monserrate in the Cook family era,” the gaming app uses historical photos and modern smartphone technology to recreate the daily life of the Cook family, in the early twentieth century. Visitors will play the game on-site, navigating the palace's spaces and completing challenges that include matching photos to rooms, locating the photographer's pose, and discovering room curiosities. Successful completion of tasks rewards players with insights into the room's historical and cultural significance and AR experiences. User-centered design and expert feedback guided the app's development and prototype testing, and a feasibility study using image-stitching and colorization of historical photos yielded promising results. The game combines education and entertainment, offering a unique, engaging, and interactive tour of the palace.

Current work involves the collection of 360° imaging on-site with proper camera equipment, the estimation of the 1902 photograph camera poses, and its intrinsic parameters of available photos taken in the palace rooms, to improve our image-stitching process. In the context of our user-centered design approach, we are
going to proceed in the near future, with expert-reviewed usability studies using
popular approaches from the literature, which will drive the improvement of the
user experience (Jakob Nielsen 2012).

The beta version of the gaming app, including the backend and frontend will also be subject to an end-user study, prior to the deployment of the proposed solution in the palace, to help us gauge the visitors' impressions of the palace. Additionally, the study helps define end-users' levels of satisfaction and engagement with the current traditional visiting experience, as well as providing insights into the visitors' attitudes and opinions toward the use of immersive technologies and identifying the barriers to the adoption and use of the proposed solution. Final refinements in the app will then result in its deployment on-site to serve the end-users visiting the Monserrate Palace (Jakob Nielsen 2012).

Continued efforts will be required to identify areas for improvement and optimization of the app, in order to better align with the needs and expectations of visitors. This will involve conducting visitor experience surveys and actively seeking feedback to gather valuable insights.

Overall, our work represents a step toward the development and implementation of immersive technologies in cultural heritage sites. By embracing these technologies and finding innovative ways to integrate them into the visitor experience, we can ensure that the visitors are able to appreciate and learn from cultural heritages (tom Dieck et al. 2016) in new and exciting ways.

The app and supporting libraries will be mostly developed by adopting open-source software, by means of a reusable toolkit that can be leveraged by SMEs in the tourism sector to easily implement AR/VR-based experiences, as stated in the work plan of the RESETTING project. Essentially, this app will act as a testing ground for the toolkit, evaluating its viability, usability, task satisfaction, and user experience.

**Acknowledgments** This work was developed in the scope of the RESETTING project, funded by the COSME Programme (EISMEA) of the European Union under grant agreement No.101038190.

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Conclusion

Smart Life embraces various Smart applications, such as Smart home, Smart city, and Smart transportation, each comprising numerous Smart artifacts. For instance, for Smart transportation, we can cite Smart vehicles, Smart ships, Smart railways, and so on. By unifying these fields under the same umbrella of Smart Life, we aim to provide an integrated vision of Smart application systems developed for the purpose of an enriched life experience.

Throughout this book, we have presented an in-depth exploration of the Smart Life concept and its various applications. We studied several fundamental aspects in understanding the concept of Smart Life and its taxonomy, evolution, and ethical considerations. We presented four conceptual contributions in Smart environment, Smart home, Smart city, and Smart tourism. Three innovative applications on Smart viticulture, Smart elevators, and Smart crowd detection were detailed. Finally, we described two experience reports on Smart city and Smart tourism.

The domain of Smart Life is vibrant and marked by substantial innovation. It holds immense promise for significantly improving the quality of life. The main challenge here is to find an equilibrium between the maximization of benefits for people and the planet and solving multiple critical issues, including concerns about privacy, security, dependency, identity theft, and the potential for abuse and misuse of technologies.

Moving forward to Smart Life Engineering will provide valuable insights to address this challenge. We are willing to investigate deeper the key problems within the field of Smart Life and find chunks of solutions to architect a Smart Life building. Within this huge ambition, our next step is to discover whether common
methodological foundations can be established across the diverse areas of Smart Life. By doing so, we aim to foster a more coherent and integrated approach to Smart Life, ultimately enhancing its potential to deliver careful, sustainable, secure, and enriched experiences for all beings on Earth.

Elena Kornyshova
Rébecca Deneckère
Sjaak Brinkkemper