IET Smart Cities最新文献

When urban landscapes erupt into civil unrests, smart technologies that are intended to help preserve social order may become prime sites of contention. Integrating critical data studies and research on networked social movements, this article examines the underexplored contours of networked disobedience to smart city development – that is, direct action by self-mobilised and self-organised digitally connected citizens and activists to subvert or disrupt the dominant structure of the datafied smart city – during a large-scale protest movement. The case of Hong Kong's smart lampposts is analysed to explicate a distinct technopolitical contention that emerged in the digital age, focusing on three key aspects: (1) citizens' digital curation of folk theories, which perpetuated a consensus of discontent over the installation of smart city technology, (2) the articulation of a digitised network of counter-power that provided a mediation opportunity structure for mobilisation and intervention, and (3) the crowdsourcing of disobedient practices of data activism aimed at sabotaging or evading the smart city technology. The article illustrates how seemingly ordinary issues of urban datafication can be repurposed to (re)produce political contention and the ways in which controversies over smart city development may fuel adversarial citizen–state engagement with repercussions for data-driven urban governance.

LoRa has proven to be an ideal solution for Internet of Things networks and applications that require long-distance communications, such as those related to smart cities or precision agriculture. Its low cost combined with the wide availability of LoRa-compatible devices make it possible to easily deploy a large number of sensors capable of collecting and transmitting key information for new services and applications. However, the process of adding new devices into a Long Range Wide Area Network (LoRaWAN) network represents a significant challenge on a large scale, as each device must be individually configured and manually registered to join the network. This manual approach is costly and impractical when it comes to deploying a very large number of devices. To address this problem, this paper proposes two deployment strategies (semi-automatic and automatic) to simplify and streamline the process of activating and registering LoRaWAN devices. These strategies facilitate the deployment of large-scale devices in smart cities, and their adoption can significantly enhance the deployment of LoRaWAN devices. Experimental results clearly demonstrate the benefits of our solution. Specifically, for 500 devices, the semi-automatic deployment is 3.75 times more efficient, and the automatic deployment is an impressive 394.87 times faster than the manual deployment.

We would like to remove the following citation from the paper: The in-text citation for this is [86].

Saad Alqahtani, A.: FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks. J. Supercomput. 78, 9438–9455 (2022). https://doi.org/10.1007/s11227-024-05975-4.

We submitted our paper on the 30th of December 2023 prior to the retraction of the referenced work which was on the 14th of Feb 2024. We had an older version of the paper saved on our computer and did not check the status at the time during the revision process, meaning we did not notice the retraction. We apologise for this oversight.

The ubiquitous presence of smartphones and the ever-expanding Internet of Things are generating a treasure trove of data on human movement. We harness the power of Artificial Intelligence to extract knowledge within this data, in particular for predicting people flows and density in a Smart City. This predictive ability holds immense potential for a multitude of applications, from optimising people flow to streamlining event planning, while offering a powerful tool for pre-emptive identification of situations that may lead to crowd disasters. In this paper, we tackle two crucial aspects of people mobility using data from public events and an Italian mobile phone network: to predict both event attendance and future crowd density in specific areas. The event details (location, time etc.) are automatically gathered and stored in a structured format. Next, we handle these problems are treated in a “supervised learning” setting, and various state-of-art Machine Learning techniques are tested to find the best model for each task. The obtained models will be encapsulated into a Policy Support System contributing to foster planning actions of mobility services.

In an area of increasing digitalisation and rapid technological development, information management is becoming essential for the effective functioning of modern organisations and cities. One of the new trends is the gradual expansion and use of drones in parts of the smart cities concept. In security, drones are used to protect public order, monitor traffic in cities and municipalities, and control sub-activities falling under the competence of police forces. In rescue work, drones allow us to efficiently and quickly manage accidental emergencies in hard-to-reach areas. At the same time, in the transport sector, drones have become an important component for the efficient delivery of parcels, reducing overall costs and time savings. This paper systematically overviews the importance of drones in various fields such as security, transportation, rescue operations or parcel delivery. Drones are becoming key elements for public order, crisis management and enhancing the quality of life of citizens in smart cities due to their capabilities of rapid response, monitoring hard-to-reach locations and autonomous data collection. Particular attention is then paid to comparing the use of drones in the Czech Republic and abroad and their benefits in the field of innovative technologies that help improve the safety and efficiency of urban services. The article also analyses the technical specifications of drones and their role in the public and private sectors, considering the legislative framework for their use. Overall, the article offers an overview of how drones contribute to integrating modern technologies into the daily functioning of smart cities and improving residents' quality of life.

Traffic microsimulation models are crucial for intelligent transportation systems evaluation, but careful parameter calibration is required for credible pre- and post-ITS comparisons. However, the back-box and stochastic nature of the system make calibration challenging. Sensitivity analysis (SA) helps to identify influential parameters, but scenario dependency limits its generalisability. Metrics such as root mean squared relative error (RMSRE) can oversimplify the stochasticity in traffic data, compromising calibration quality. Furthermore, calibration for specific key performance indicators (KPIs) does not ensure the reliability of other KPIs. The authors propose the simultaneous calibration of driving behaviour parameters without prior sensitivity information. They demonstrate the effect of the error metric and objective function facets on the calibration efficiency and parameter convergence consistency. Results indicate that employing SA to identify influential parameters is unnecessary. Each parameter converges to a stable point by responding directly to the information within the objective function or due to the interactions with other parameters. Therefore, simultaneous calibration of multiple KPIs and maintaining the stochasticity structure in the data—enhanced calibration efficiency and parameter convergence consistency. Additionally, using probabilistic dissimilarity metrics that consider the entire distribution, such as the Wasserstein distance, outperform the K–S distance and RMSRE.

The evolution of smart cities marks a profound shift in urban life globally, where new technologies enhance efficiency, sustainability, and the quality of life for residents. At the forefront of this transformation are Artificial Intelligence (AI) and the Internet of Things (IoT), driving cities into a new era of innovation. AI and IoT connect devices and infrastructure, enabling cities to process vast amounts of data efficiently. These technologies have already revolutionised various aspects of daily life. IoT, for example, powers intelligent systems in logistics, healthcare, and automotive technology.

In line with the trend of advancing urban technologies, this Special Issue aims to present the latest advancements and explore the opportunities and challenges of integrating these technologies into city infrastructure. It provides policymakers, urban planners, and stakeholders with critical insights into how these innovations shape the future of our cities. By sharing best practices, we highlight the potential of AI and IoT to foster smarter, sustainable, and more liveable cities. This issue underscores the importance of integrating these technologies into city planning and development, empowering stakeholders to drive positive change and build resilient urban communities.

The issue contains a curated selection of five papers, each offering groundbreaking insights into how AI and IoT are revolutionising urban living. From air quality prediction to cybersecurity and digital twin cities, these studies showcase diverse applications that are shaping the future of smart cities worldwide.

All of the papers selected for this Special Issue showcase the diverse and transformative potential of AI and IoT technologies in shaping the future of smart cities. From optimising air quality prediction using advanced hybrid models to enhancing cybersecurity through machine learning-driven approaches, each study contributes unique insights and practical solutions. Additionally, research on digital twin cities, ICT acceptance models, and art-based interventions underscores the interdisciplinary nature of smart city development, emphasising community engagement and sustainable urban planning. These findings collectively highlight the pivotal role of technological innovation in fostering resilience, efficiency, and inclusivity within urban environments. As smart cities continue to evolve, the lessons and advancements presented in this issue provide valuable guidance for policymakers, urban planners, and researchers striving to build more intelligent and liveable cities worldwide.

Smart Traffic Light Systems play an important role in urban traffic management. They often rely on cameras and sensors to collect traffic data. However, these methods are limited in terms of vehicle occupancy and queuing. Effective traffic management remains a challenge in urban areas owing to traffic congestion and emergencies. A new system called ADSTLS (Adaptive and Dynamic Smart Traffic Light System) is proposed, which handles traffic management at an intersection and effectively solves the cumbersome problem of traffic congestion while ensuring priority for emergency vehicles. ADSTLS provides fault tolerance to its components and works reliably in most failure situations. Therefore, traffic data is collected from cameras, and useful traffic information is extracted using computer vision and image processing. The proposed system also uses the Weight Chicken Swarm Optimisation algorithm for decision-making to reduce congestion and average vehicle waiting time significantly. ADSTLS was applied to a real case study of EL-Hidhab Setif city intersection. The approach's effectiveness was confirmed by thorough experiments, resulting in a noteworthy decrease in the average vehicle waiting time (31 s) and queue occupation rate (33.82%) across all simulated traffic scenarios. Furthermore, compared to other car types, emergency vehicles usually had much shorter wait times.

An attention-based long short-term memory (ALSTM)-fast model predictive control (MPC) thermal regulation system for buildings is presented. The proposed system is developed to address the challenges associated with traditional heating, ventilation, and cooling (HVAC) control systems, often designed with fixed setpoints and static control strategies, leading to poor performance and suboptimal energy efficiency. The ALSTM-Fast MPC system, on the other hand, performs the integration of deep learning and optimisation algorithms to predict the thermal behaviour of buildings and optimise the HVAC system control for thermal comfort and energy efficiency. The ALSTM-Fast MPC system was implemented and evaluated on a real-world data collected from a building automation system. Additionally, extensive experiments were conducted to analyse the system's performance. The results demonstrated the system's adaptability to changing thermal dynamics and occupancy patterns and its ability to achieve robust and efficient thermal regulation. As a result, a solution for optimising HVAC control in buildings is provided by the proposed ALSTM-Fast MPC system.