IET Smart Cities最新文献

Understanding associations between sustainable development and the smart city is essential to achieve sustainable smart cities. Dubai has emerged as an example in the Middle East for adopting smart technologies to enhance urban living, with initiatives ranging from digital governance to intelligent transportation systems. However, the associations between sustainable development and smart city implementation in Dubai is limited. This study aims to investigate the application of the smart city in Dubai, assessing the smart city implementation in terms of sustainable development by applying the system archetypes to assess the implementation of the smart city in Dubai in terms of sustainable development issues. After the identification of the system archetypes, it is found that the implementation of smart city initiatives such as e-government and clean transportation are in line with sustainable development issues such as low-carbon emissions and less air pollution. Moreover, this study shows that the structure of the Limits to Growth archetypes has dominated the smart city development in Dubai. This means that the development of Dubai has had critical issues such as persistent traffic congestion and a polluted atmosphere. The findings stress that the smart city application in Dubai is a good exemplar that the smart city can be a sustainable smart city altogether. The second point is although the smart city enables us to achieve a sustainable smart city, the implementation of the smart city should be monitored regularly, especially if reinforcing loops dominate balancing loops as seen in the case of traffic congestion. This study contributes to enhancing the decision-making process of policymakers, industry stakeholders, government authorities and business managers regarding the implementation of smart initiatives as well as for city planners to achieve a sustainable smart city in other regions.

In large-scale deployments, the Internet of things (IoT) and wireless sensor networks (WSNs) often face challenges in transmitting collected data to the base station due to limited network coverage. Unmanned aerial vehicles (UAVs) can extend this coverage by flying to remote WSN areas and communicating with aggregator nodes (CH-nodes) to retrieve data. Designing UAV-assisted data collection systems therefore requires a careful consideration of both UAV and WSN constraints. This article proposes an energy-efficient approach for UAV-based data collection in IoT/WSNs. The problem is formulated to jointly optimise system cost and energy consumption while accounting for communication power, mission duration, and data importance. The solution proceeds in two steps. First, aggregator nodes are selected using clustering based on residual energy and inter-node distances to minimise system costs. Second, the UAV trajectory is generated using a Lévy flight strategy that follows the positions of the selected aggregators. Although this trajectory may be slightly longer than that produced by a deterministic TSP route, it increases the amount of collected data and prolongs both UAV and WSN lifetime by ensuring timely visits to distant cluster heads. Simulation results confirm the efficiency and robustness of the proposed method compared with existing solutions.

Urban environments often pose challenges for individuals with mobility impairments due to inadequate pedestrian infrastructure. In addition, the lack of accurate mapping of accessibility features limits the ability to monitor and address these constraints effectively. This paper introduces a framework for Automating City Accessibility Mapping using AI (ACAMAI), that is, provides an AI-assisted pipeline for the automated identification and geolocation of urban accessibility constraints using Google Street View (GSV) panoramas. The ACAMAI pipeline comprises two main stages: (i) training a YOLOv8 object detector to recognise accessibility-related features, such as curb ramps, missing ramps, obstacles and surface problems, in 2D sidewalk images; and (ii) scanning 360° GSV panoramas by extracting multiple perspective views to be analysed by the trained model. The model was trained on a combination of international (Project Sidewalk Dataset—PSD) and local (Porto Dataset—PTD) datasets, achieving high performance across classes, including 91% recall and 85% precision for curb ramps. In the panorama scanning stage, using a fine angular iterative step (2°) maximised the recall, reaching 90% for curb ramps and 93% for obstacles in a locally annotated dataset (GSV Panorama Porto Dataset—GSV-PPD). Although this improved detection coverage, it also led to a high number of redundant predictions, which contributed to a reduced overall precision. Finally, identified constraints are georeferenced and mapped onto OpenStreetMap (OSM), supporting scalable and inclusive urban planning.

The emergence of advanced home technology and the incorporation of distributed energy resources (DERs) have markedly heightened the necessity for energy management solutions that balance technical performance with economic efficiency in smart residential microgrid (SRMG). In the absence of effective collaboration and energy interactions among smart homes, imbalances in the SRMG load profile may occur, risking violations of technical standards. This study introduces a decentralised framework for SRMG that includes diverse smart homes engaged in peer-to-peer (P2P) energy interactions. The framework is designed to minimise variations in the SRMG load profile while also reducing expenses for smart homes, all while ensuring resident comfort through P2P interactions. The home energy management (HEM) system seeks to optimise energy costs by utilising DER capabilities to facilitate P2P interactions and maintain bidirectional communication with the SRMG operator (SRMGO). Continuous data sharing between the SRMGO and HEM systems is crucial for optimising the load profile in a decentralised framework. This enables about a 4.25% reduction in load profile deviations without raising energy costs, showing that decentralised P2P energy interactions improve load management in SRMG and cost stability in smart homes. Simulation results generated using general algebraic modelling system (GAMS) software demonstrate that integrating P2P energy strategies within a decentralised framework can effectively fulfil both the technical requirements of the SRMG and the financial goals of individual smart homes.

NetZeroCities programme supports 100 European cities on their path to climate neutrality by 2030, thus showing the way for the whole continent to become climate neutral by 2050. As of now, 92 cities have outlined actions and investments to achieve this goal but time is running out. The timely implementation of the required actions depends on further efforts related to funding, capability improvement, evaluation, stakeholder engagement and upscaling.

Mapping socio-ecological dynamics reveals how human and natural systems interact over time, supporting informed planning and balanced regional development. By detecting patterns in these interactions, mapping supports policymakers in navigating complex transitions and guiding sustainable regional planning. These transitions are particularly evident in regions experiencing synchronised coal mine and coal-fired power plant closures. Despite ongoing rehabilitation efforts worldwide, few studies explore how socio-ecological factors interact and evolve or employ mapping as an integrative tool. Directly addressing this gap, this study innovatively introduces a framework that treats coal mines and power plants as a connected nexus, analysing their regional impacts through an integrated mapping approach. This framework combines geospatial mapping with exploratory, causal, and predictive modelling to analyse spatiotemporal shifts in post-mining landscapes. Applied to the Latrobe Valley in Australia, the framework reveals the closure caused sharp declines in income and nighttime light intensity, with no immediate recovery in native vegetation. Projections indicate that without early intervention, the Valley risks deepening regional socioeconomic decline. Translating multifaceted data into an analytical format enables stakeholders to see through complexity, understand interconnected socio-ecological dynamics across phases, and coordinate governance to manage regional changes for balanced development strategies.

This paper presents a hybrid human-centred sustainable smart city framework that integrates three key dimensions: Sustainability, Health & Wellbeing and Technologies. The proposed framework uses a hybrid index to evaluate smart city performance across these dimensions, applying a quantitative methodology based on min–max normalisation. By conducting an in-depth comparative analysis of smart city initiatives in Hong Kong and Riyadh, the study illustrates the adaptability of the hybrid framework in distinct urban contexts. Hong Kong’s efforts in integrating (Internet of Things) IoT-based monitoring systems, digital health platforms, and active transportation highlight the focus on health and wellbeing in space-constrained urban environments. In contrast, Saudi Vision 2030 emphasises renewable energy deployment, large-scale smart infrastructure projects, and sustainability measures in Riyadh. Despite the differences in economic and regulatory environments, both cities demonstrate the effectiveness of embedding human-centred principles in urban development. The findings underscore that a balanced integration of technology, sustainability and wellbeing is essential for building resilient, inclusive, and sustainable smart cities, serving as a model for future urban development globally.

Digital twinning is an advanced technology that involves creating virtual replicas of various physical systems. In smart cities, digital twins serve as digital representations that model and simulate various urban elements, such as environment protection (e.g., air quality), critical infrastructure, transportation networks and other urban management processes. It has recently gained considerable attention for its transformative potential, enabling city authorities to visualise and analyse complex city dynamics for better-informed decision-making. Therefore, this paper proposes a simplified layered architecture for smart city digital twins. The layers of the proposed architecture cover the range of operations required by the functionality of the digital twin and the interaction between them, from data transfer or synthesis to big data streaming and intelligent analytics. The paper also introduces an open-source software tool that realises the proposed architecture, with each layer designed as an independent Python module for easy integration and maintenance. Three case studies are used to demonstrate the capabilities of the tool. One use case addresses short-term forecasting of the air quality index, whereas the other use case targets the detection of an individual's respiratory condition based on data received from wearable devices. The third case combines the other two cases to offer a warning system for residents with medical conditions based on air quality. The results of the case studies show the tool's ability to effectively handle environment and eHealth-related use cases and combine them for the welfare of smart city residents, leading to a more resilient health-focused urban landscape.

Statistics indicate that many road accidents stem from driver negligence, such as collisions with parked cars, motorcyclists or pedestrians in blind spots. Although numerous studies address bus driver behaviour, most focus on freeways rather than urban streets. This paper introduces a safety assessment system (SAS) utilising existing onboard cameras on buses, eliminating the need for additional sensors. The SAS evaluates city bus drivers' behaviour when entering and exiting bus stops (ELBS), considering three key factors: entry velocity, head turn frequency to assess surroundings, and estimated distance from the road boundary upon arrival (DBR). Leveraging image and global positioning system (GPS) data, the system establishes risk levels for each scenario, aiding in identifying safe driving practices. To validate its feasibility, a professional survey was conducted, confirming alignment between the designed scoring scale and survey results.

Smart cities, characterised by their extensive use of IoT devices, aim to enhance urban living through improved efficiency, sustainability and quality of life. However, the widespread integration of IoT technology introduces significant cybersecurity challenges, including vulnerabilities to cyberattacks, data breaches and infrastructure resilience issues. Addressing these challenges is critical to realising the full potential of smart city initiatives. Intrusion detection systems (IDS) play a vital role in safeguarding smart city environments. Numerous studies have explored various IDS methodologies, yet the dynamic and complex nature of smart city IoT networks demands continuous advancements. In this article, we present a novel IDS approach that leverages machine learning techniques to enhance the detection and prevention of cyber threats in smart cities. Utilising the UNB CIC IoT 2023 Dataset, we develop and evaluate multiple models, including Random Forest Classifier, Decision Tree Classifier, KNN and AdaBoost. Our proposed IDS framework emphasises real-time threat detection ensuring both low latency and high accuracy. Through comprehensive data preprocessing and rigorous model training, our system demonstrates exceptional performance in identifying and neutralising cyber threats. The findings from this research reveal significant improvements in the security and privacy of smart city IoT infrastructures highlighting the effectiveness of integrating advanced AI methodologies.