Search Results (29)

This study examines the potential of satellite internet technologies to enhance crisis management in urban evacuation scenarios in Italy, with a specific focus on the Starlink system as a case study. In emergency situations, traditional mobile and WiFi networks often become inaccessible, significantly impairing timely communication and coordination. Reliable connectivity is therefore imperative for effective rescue operations and public safety. This research analyzes how satellite-based internet can provide robust, uninterrupted connectivity even when conventional infrastructures fail. The study discusses operational advantages such as rapid deployment, broad coverage, and scalability during disasters, as well as key constraints including line-of-sight requirements, environmental sensitivity, and regulatory challenges. Empirical findings from the deployment of Starlink during an actual urban evacuation event in Italy indicate that latency dropped below 200 ms and sustained upload/download speeds averaged approximately 50 Mbps—up to three times faster than ground networks in disrupted zones. By evaluating both benefits and limitations, this paper provides a comprehensive understanding of the integration of satellite internet services within Italian emergency response systems, aiming to improve the performance of urban evacuation strategies. Full article

Rapid urbanization and aging population present global challenges for smart cities, especially for equitable pandemic response and age friendly urban transitions. This paper through a two-round Delphi study assessed Amman’s efficiency in pandemic response focusing on digital inclusion for older adults and critical barriers to age-friendly urbanism. The results indicate moderate satisfaction with Amman’s overall pandemic response alongside significant limitations, particularly in digital equity for older adults. Key systemic barriers included compromised air quality, inadequate public transportation, notably poor public Wi-Fi, and deficient digital infrastructure. Furthermore, political and financial obstacles, such as high living costs and low governance transparency, significantly hindered progress. Experts prioritized solutions emphasizing improved physical accessibility, expanded green spaces, and enhanced digital literacy. This study underscores the urgent need for integrated, multi-dimensional strategies, including participatory governance and targeted digital inclusion programs, to foster sustainable and equitable smart city development that enhances resilience and inclusiveness for aging populations in post pandemic urban planning contexts. Full article

Smart city research often emphasises technology while neglecting how governance structures and resources influence outcomes. This study compares Tier 1 (Sydney, Melbourne, Brisbane, Adelaide) and Tier 2 (Geelong, Newcastle, Hobart, Sunshine Coast) Australian cities to evaluate how urban scale, economic capacity, governance complexity, and local priorities influence smart-enabled resilience. We analysed 22 official strategy documents using a two-phase qualitative approach: profiling each city and then synthesising patterns across technological integration, community engagement, resilience objectives and funding models. Tier 1 cities leverage extensive revenues and sophisticated infrastructure to implement ambitious initiatives such as digital twins and AI-driven services, but they encounter multi-agency delays and may overlook neighbourhood needs. Tier 2 cities deploy agile, low-cost solutions—sensor-based lighting and free public Wi-Fi—that deliver swift benefits but struggle to scale without sustained support. Across the eight cases, we identified four governance archetypes and six recurring implementation barriers—data silos, funding discontinuity, skills shortages, privacy concerns, evaluation gaps, and policy changes—which collectively influence smart-enabled resilience. The results indicate that aligning smart technologies with governance tiers, fiscal capacity, and demographic contexts is essential for achieving equitable and sustainable outcomes. We recommend tier-specific funding, mandatory co-design, and intergovernmental knowledge exchange to enable smaller cities to function as innovation labs while directing metropolitan centres towards inclusive, system-wide transformation. Full article

Human–computer interaction (HCI) drives innovation by bridging humans and technology, with human activity recognition (HAR) playing a key role. Traditional HAR systems require user cooperation and infrastructure, raising privacy concerns. In recent years, Wi-Fi devices have leveraged channel state information (CSI) to decode human movements without additional infrastructure, preserving privacy. However, these systems struggle with unseen users, new environments, and scalability, thereby limiting real-world applications. Recent research has also demonstrated that the impact of surroundings causes dissimilar variations in the channel state information at different times of the day. In this paper, we propose an unsupervised multi-source domain adaptation technique that addresses these challenges. By aligning diverse data distributions with target domain variations (e.g., new users, environments, or atmospheric conditions), the method enhances system adaptability by leveraging public datasets with varying domain samples. Experiments on three public CSI datasets using a preprocessing module to convert CSI into image-like formats demonstrate significant improvements to baseline methods with an average micro-F1 score of 81% for cross-user, 76% for cross-user and cross-environment, and 73% for cross-atmospheric tasks. The approach proves effective for scalable, device-free sensing in realistic cross-domain HAR scenarios. Full article

With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems—such as Bluetooth, ZigBee, or FM radio—Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification. Full article

The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, without the actual implementation of the automatic passenger counting system (APC), but with a proposal based on image processing and machine learning techniques and concepts, since it represents one of the most used approaches. The research explores various technologies and algorithms, like card swiping, infrared, weight and ultrasonic sensors, RFID, Wi-Fi, Bluetooth, LiDAR, thermos cameras, including CCTV cameras and traditional computer vision methods, and advanced deep learning approaches, highlighting their strengths and limitations. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The research also analyzed the current General Data Protection Regulation (GDPR) that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field. Full article

This article addresses gaps in knowledge about whether or not smart street furniture could enhance the relationship between people and place, and whether it improves the design, amenity and management of public open space. An Australian design team, comprising a local council, a street furniture manufacturer, and academics, designed, built, piloted, and assessed a new piece of smart street furniture called a ‘ChillOUT Hub’. This Hub is an integrated street furniture system, designed for public open spaces. It is enabled with ‘smart’ technology features such as Wi-Fi, mobile device charging stations, plus infrastructure usage and environmental sensors. The Hub aims to support social health, improve microclimatic conditions, and provide equitable access to technology. Street survey processes were undertaken with both ‘users’ and ‘non-users’ of the Hubs. The findings help to identify what value digitally enhanced street furniture actually has in open space and how that value is perceived by the public. The Council and Hub users overwhelmingly appreciated the newly designed street furniture and its smart amenities. Non-users clarified why they did not use smart street furniture and discussed the option of having digital amenities in public spaces more generally. Full article

Global food security is a critical issue today, strained by a wide range of factors including global warming, carbon emissions, sociopolitical and economic challenges, traditional workforce decline and population growth. Technical innovations that address food security, like agricultural robotics, are gaining traction in industry settings, moving from controlled labs and experimental test facilities to real-world environments. Such technologies require sufficient network infrastructure to support in-field operations; thus, there is increased urgency to establish reliable, high-speed wireless communication networking solutions that enable deployment of autonomous agri-robots. The work presented here includes two contributions at the intersection of network infrastructure and in-field agricultural robotics. First, the physical performance of a private 5G-SA system in an agri-robotics application is evaluated and in-field experimental results are presented. These results are compared (using the same experimental setup) against public 4G and private WiFi6 (a newly emerging wireless communication standard). Second, a simulated experiment was performed to assess the “real-time” operational delay in critical tasks that may require quick turnaround between in-field robot and off-board processing. The results demonstrate that public 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication; that private 5G-SA greatly outperforms public 4G in all performance metrics (as expected); and that private WiFi6, though limited in range, is a fast and very reliable alternative in specific settings. While a single wireless solution does not currently exist for the agricultural domain, multiple technologies can be combined in a hybrid solution that meets the communications requirements. Full article

Indoor localization and navigation have become an increasingly important problem in both industry and academia with the widespread use of mobile smart devices and the development of network techniques. The Wi-Fi-based technology shows great potential for applications due to the ubiquitous Wi-Fi infrastructure in public indoor environments. Most existing approaches use trilateration or machine learning methods to predict locations from a set of annotated Wi-Fi observations. However, annotated data are not always readily available. In this paper, we propose a robot-aided data collection strategy to obtain the limited but high-quality labeled data and a large amount of unlabeled data. Furthermore, we design two deep learning models based on a variational autoencoder for the localization and navigation tasks, respectively. To make full use of the collected data, a hybrid learning approach is developed to train the models by combining supervised, unsupervised and semi-supervised learning strategies. Extensive experiments suggest that our approach enables the models to learn effective knowledge from unlabeled data with incremental improvements, and it can achieve promising localization and navigation performance in a complex indoor environment with obstacles. Full article

The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing. Full article

Knowing and modeling mobility in smart city spaces is important for both planning and managing city resources. The optimization of public resources and the improvement of their management are some of the main concerns in the development of sustainable urban development policies. This study proposes the application of several artificial intelligence methodologies to support mobility planning based on data provided by public Wi-Fi infrastructures in the city. Considering that Wi-Fi networks provide high-frequency data about the devices under their coverage radius, three classification techniques are proposed: by frequency of occurrence of the devices, by estimation of the mode of transport, and by estimation of the most common travel routes. As a case study, the city of Palma (Mallorca, Spain), an international tourist destination where mobility is of singular importance, is selected. This study shows the results obtained from a Wi-Fi network with wide coverage that is integrated into the urban space. It provides novel and updatable information on the mobility model of the city by taking advantage of public high-frequency monitoring resources. Full article

Sensor networks using the Internet of Things (IoT) are gaining momentum for real-time monitoring of the environment. Increased use of natural resources due to a rise in agriculture production, manufacturing, and civil infrastructure poses a challenge to sustainable growth and development of the global economy. For sustainable use of natural resources (including air, soil, and water), data-driven modeling is needed to understand and simulate contaminant transport and proliferation. Different logging devices are specifically designed to integrate with environmental sensors that send real-time data to the cloud using IoT systems for monitoring. The IoT systems use an LTE network or Wi-Fi to transmit air, water, and soil quality data to the cloud networks. This seamless integration between the logging devices and IoT sensors creates an autonomous monitoring system that can observe environmental parameters in real-time. Various federal organizations and industries have implemented the IoT-based sensor network to monitor real-time air quality parameters (particulate matter, gaseous pollutants), water quality parameters (turbidity, pH, temperature, and specific conductance), and soil parameters (moisture content, soil nutrients). Although several organizations have used IoT systems to monitor environmental parameters, a proper framework to make the monitoring systems reliable and cost-efficient was not explored. The main objective of this study is to present a framework that combines a sensing layer, a network layer, and a visualization layer, allowing modelers and other stakeholders to observe a progressive trend in environmental data while being cost-efficient. This efficient real-time monitoring framework with IoT systems helps in developing robust statistical and mathematical models. The sustainable development of smart cities while maintaining public health requires reliable environmental monitoring data that can be possible by the proposed IoT framework. Full article

This study assessed the applicability of geolocation data provided by public Wi-Fi infrastructures as information sources that can contribute to urban planning and management. We focused particularly on modeling and monitoring real-time mobility and congestion using geolocation capabilities of Wi-Fi public networks in Smart cities. The proposed methodology combines a detailed geographic analysis of the space with high-frequency indicators generated from network data. This study emphasizes the importance of Wi-Fi infrastructures as noninvasive monitoring systems, and describes how network data can be applied to generate useful indicators for urban planning and management. The methodology was empirically implemented in the city of Palma (Balearic Islands, Spain), where the social distance level was measured to identify conflicting areas. We demonstrate how the proposed solution can estimate pedestrians’ density efficiently and precisely through high-frequency monitoring (5 min or less) and the construction of comprehensive indicators. In this context, we suggest several public policies that can be implemented by using this methodological approach to monitor dynamic patterns of pedestrian mobility, especially during health crises or during high tourist seasons. Full article

This paper presents a scalable IoT architecture based on the edge–fog–cloud paradigm for monitoring the Indoor Environmental Quality (IEQ) parameters in public buildings. Nowadays, IEQ monitoring systems are becoming important for several reasons: (1) to ensure that temperature and humidity conditions are adequate, improving the comfort and productivity of the occupants; (2) to introduce actions to reduce energy consumption, contributing to achieving the Sustainable Development Goals (SDG); and (3) to guarantee the quality of the air—a key concern due to the COVID-19 worldwide pandemic. Two kinds of nodes compose the proposed architecture; these are the so-called: (1) smart IEQ sensor nodes, responsible for acquiring indoor environmental measures locally, and (2) the IEQ concentrators, responsible for collecting the data from smart sensor nodes distributed along the facilities. The IEQ concentrators are also responsible for configuring the acquisition system locally, logging the acquired local data, analyzing the information, and connecting to cloud applications. The presented architecture has been designed using low-cost open-source hardware and software—specifically, single board computers and microcontrollers such as Raspberry Pis and Arduino boards. WiFi and TCP/IP communication technologies were selected, since they are typically available in corporative buildings, benefiting from already available communication infrastructures. The application layer was implemented with MQTT. A prototype was built and deployed at the Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), using the existing network infrastructure. This prototype allowed for collecting data within different academic scenarios. Finally, a smart sensor node was designed including low-cost sensors to measure temperature, humidity, eCO₂, and VOC. Full article

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors. Full article