IET Smart Cities最新文献

The interest in smart city initiatives is continuously growing, and so is the interest in measuring their smartness and sustainability. The value of a Smart City is proved by comparing its results with sustainability goals and by measuring the benefits delivered to stakeholders, citizens and city authorities. For this reason, supporting the monitoring and evolution of smart cities is a challenging and crucial task. This can be accomplished by using key performance indicators (KPIs), which, in turn, inform the decision-making processes of smart city governance. However, ethical implications in KPIs evaluation may be hidden within the measurement process. For example, low trustworthiness of data sources used to collect KPIs input parameters can affect evaluation results without KPIs experts being aware of these implications. Ethical implications that could represent a perceived violation of an ethical value should be highlighted when KPIs results are proposed to stakeholders. In this paper, we have integrated an ethical risk traceability mechanism into MIKADO, a tool that we designed for assessing KPIs in smart cities. This mechanism involves both KPIs and ethics experts in the evaluation process, ensuring that the KPI assessment results—presented through dashboards obtained through code generation—include warnings about potential ethical issues. The extended framework has been evaluated by experts in the field during a dedicated focus group, who confirmed its usefulness in identifying ethical implications in the evaluation of smart city KPIs, with the aim of tracing ethical risks, and supporting ethics experts in carrying out this task.

Kumar, V., et al. Sense (and) the city: from Internet of Things sensors and open data platforms to Urban observatories. IET Smart Cities. 6(4), 291–311 (2024). https://doi.org/10.1049/smc2.12081.

‘This work has been funded in part by EPSRC Grant EP/P016782/1 and an Industrial CASE award supported by BT.’ was incorrect. This should have read: ‘This work has been funded in part by EPSRC Grant EP/P016782/1 and an EPSRC Industrial CASE award [EP/R511857/1] supported by BT.’

We apologize for this error.

The smart city concept integrates various collaborative services to enhance urban living. However, these services introduce significant security concerns, especially in authentication, authorisation, and access control (AAA). To address these security challenges, researchers must design and implement frameworks that safeguard data exchange between smart services. This paper offers a taxonomy-based review of current solutions, focusing on how emerging technologies like Blockchain, artificial intelligence (AI), quantum computing, and hybrid approaches address AAA concerns. We evaluate these technologies based on key factors such as confidentiality, integrity, availability (CIA), trust, privacy, and scalability. Through a systematic review of literature from 2017 to 2024, we classify and assess methods that strengthen authentication, optimise access control, and refine authorisation processes to mitigate risks in data sharing. A major contribution of this paper is the integration of case studies, demonstrating real-world applications of these technologies in smart city contexts. Additionally, we explore the applicability of these solutions, highlighting their challenges and future potential. This research also outlines future directions for building secure, efficient, and scalable smart city ecosystems, ultimately facilitating the development of adaptable frameworks for smart city services.

‘Zaha’ is a retrieval-augmented generation question answering system integrated with The World Avatar knowledge graph, designed to support urban planning and smart city initiatives by enabling intuitive natural language queries for complex urban data. Zaha facilitates the querying of diverse domains within the urban environment, offering an accessible and effective tool for urban data analysis. By simplifying access to complex dataset, Zaha addresses a critical barrier in urban planning and management: the need for technical expertise to query data effectively. Urban data, encompassing geospatial, environmental, and regulatory information, is pivotal in informing decision-making processes. However, challenges such as data silos and the technical complexity of query tools and languages hinder the accessibility and utilisation of urban data. The integration of Zaha with The World Avatar knowledge graph further mitigates the issue of data silos by unifying urban data from diverse sources and formats into a single framework. Leveraging knowledge graph technology, Zaha facilitates efficient data retrieval based on the relationships defined between entities. By bridging the gap between data and users, Zaha empowers urban planners and other stakeholders to access and query complex urban data intuitively, enabling them to make informed decisions without requiring technical expertise.

Microgrids represent a viable approach for supplying energy at an optimised cost. In residential microgrids, neighbours with different personality traits can collaborate in diverse manners. Further, diverse decision-making among neighbours with varying personality traits can influence their collaborative efforts. This paper investigates how neighbours with diverse personality traits collaborate in residential microgrids as a part of a smart city, facilitating energy sharing and cost optimisation in energy supply. The proposed approach in this paper incorporates social behaviour uncertainties into the model and organises cooperation within a game-theoretic framework. This framework seeks to optimise and equitably distribute energy among neighbours while minimising costs, incentivising cooperation and penalising noncooperation based on individual behavioural traits. Furthermore, the proposed approach is implemented in a residential test microgrid using the GAMS CPLEX solver, analysing two distinct scenarios that account for the influence of energy storage on energy transactions. The proposed method establishes average discount and punishment coefficients, enhancing its adaptability to various microgrid configurations. The study also investigates the tradeoff between utility-set energy prices and optimal pricing for neighbours. Simulations show that cooperation enables neighbours to meet excess demand, reducing overall microgrid costs by up to 80%.

This conceptual paper delves into the validity of product-service systems (PSS) in driving smart city development. Existing research often treats smart cities as primarily technology-driven constructs, overlooking how organisational measures and strategies such as PSS can contribute to their sustainability ambitions and, thus, smart city initiatives. Through an integrative literature review of selected 145 academic papers across smart cities, sustainability, innovation, digitalisation and product-service systems, this paper introduces a framework that maps PSS onto six core dimensions of the Smart City concept—dimensions identified from the previous research, never before presented together. The findings reveal that PSS enables organisations to transcend beyond isolated organisation focused sustainability driven solutions into wider smart urban surroundings and capabilities. The framework emphasises how PSS can contribute to environmental goals, governance reforms, social inclusion, and economic resilience, thus, being able to provide value for both private and public actors, and citizens. The paper addresses a key research gap and calls for empirical validation to further study how PSS can contribute as a scalable and designable enabler of smart city development.

Drive-by sensing is a promising concept that employs public transport as a mobile sensing platform to achieve high spatio-temporal coverage for urban sensing tasks. At the same time, the low-cost nature of mobile IoT sensors necessitates their more frequent calibration to ensure data accuracy and reliability. Manual or lab-based calibration of a large number of mobile sensors may no longer be feasible and thus new approaches for automatic calibration are needed. Most prior work on optimal mobile sensor deployment focuses on coverage aspect without considering the sensor calibration. In this study, we present a joint approach for optimising the placement of bus-based sensors for maximising the total unique sensing area and combining the optimal reference sensors geo-placement for maximising k-hop calibrate requirements on the selected routes. A metric-based system developed in our model uses geographical set operations which includes both spatial and temporal joins to quantify the contribution of each bus route and rank them accordingly. We formulate the coverage optimisation problem as a mixed integer linear program (MILP) solve it with a greedy algorithm, and demonstrate this method’s potential using real-world bus-transit data from Toronto, Canada and Manchester, UK. Our approach involves a metric-based system which quantifies each bus route unique coverage contribution for determining an optimal set of bus routes and bus stops for bus-based and reference sensor deployment, to minimise sensor network costs and maximise spatio-temporal coverage. The comparison with a random baseline algorithm indicates that our method outperforms in terms of deployment and coverage efficiency. Our results also include the potential of our weighted method in improving drive-by sensing for air quality monitoring by comparing it with a separate benchmark scheme with different criteria.

This survey synthesises state-of-the-art advancements in Reconfigurable Intelligent Surfaces (RIS) and their transformation in urban wireless networks, focusing on addressing the challenges inherent to 5 G/mmWave communications. The authors begin by evaluating path loss, blockage, and interference in dense urban environments. The analysis comprehensively represents various RIS design, including phase-shift optimisation, material selection, and control mechanisms based on PIN and varactor diodes, whereas considering integration challenges such as scalability, energy efficiency, and real-world deployment constraints. The authors present an in-depth case study of RIS implementation in a smart city mmWave scenario, utilising both analytical modelling and simulation experiments to quantify performance improvements in beam steering and network reliability. This study highlights the potential of RIS to mitigate traditional propagation limitations and provide directions for future research, standardisation efforts, and the evolution of 6G wireless networks.

With advancements in technology, natural scene text recognition (STR) has become a critical yet challenging field due to variations in fonts, colours, textures, illumination, and complex backgrounds. This research study focuses on optical character recognition (OCR) with a case study on Iranian signposts, traffic signs, and licence plates to convert text from images into editable formats. The proposed method combines a preprocessing stage, leveraging resizing, noise reduction, adaptive thresholding, and colour inversion, which significantly enhances image quality and facilitates accurate text recognition, with a deep-learning pipeline. The process begins with the CRAFT model for text detection, addressing limitations in Persian/Arabic alphabet representation in datasets, followed by CRNN for text recognition. These preprocessing techniques and the CRAFT component result in notable performance improvements, achieving 98.6% accuracy with training error rates reduced from 13.90% to 1.40% after 20 epochs. Additionally, the system's effectiveness is validated through Persian/Arabic-specific OCR criteria at both the character and word levels. Results indicate that preprocessing and deep learning integration improve reliability, paving the way for future applications in intelligent transportation systems and other domains requiring robust STR solutions. This study demonstrates the potential for further enhancements in OCR systems, particularly for complex, script-based languages.

Japan has set a decarbonisation goal in the Plan for Global Warming Countermeasures, which aims to reduce greenhouse gas emissions by 46% compared with those in 2013 by 2030. To achieve this goal, the Japanese government established a regional decarbonisation roadmap in 2021. In pursuing the roadmap, 74 municipalities were appointed as Japan's decarbonisation-leading regions. In this study, we make the projection to clarify whether the goal will be met by performing the following steps. First, we classified the regions on the basis of their specific energy structure and examined their decarbonisation policies. Next, we performed a cluster analysis of their energy structure and energy-saving methods in the civil sector, residential sector, sectors outside the civil sector and transportation sector. The results of the analysis indicate that in terms of energy-use characteristics, the proportion of the municipalities selected is representative of Japan. However, we find that if the carbon neutrality plans of these regions are implemented and extended nationwide, the 2030 goal will not be met. Moreover, we found that within the civil sector, regions are more inclined to achieve carbon neutrality in business and public facility areas than in residential areas. This study urges Japan to be more aggressive in its global warming mitigation strategies and identifies residential areas as the most promising sector to put more effort into.