ABSTRACTThis 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.

Abstract The smart city framework has become a key approach to addressing urbanisation challenges over the last 2 decades. While KPIs have been developed for various smart city dimensions, security and safety remain underexplored. This paper addresses this gap through a systematic review of KPIs. The study examines how urban security and safety smartness is assessed, focusing on three questions: (RQ1) What indicators measure urban security and safety smartness? (RQ2) In which smart city dimensions are these KPIs located? (RQ3) How are these KPIs defined and quantified? Using PRISMA guidelines, databases including Web of Science, Scopus, and IEEE Xplore were searched, yielding 2369 sources. After screening, 38 studies were analysed. A total of 182 unique KPIs were identified and categorised into crime prevention and control (53), perceptions of safety (11), emergency and disaster management (50), and cybersecurity (68). Most KPIs focus on city outcomes, with fewer addressing smart technology functionalities. Definitions and measurement approaches lack consensus. This review identifies gaps in defining and measuring smart urban security and safety. Standardising KPIs and incorporating technology‐specific metrics are key directions for future research.

Abstract 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.

ABSTRACT 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.

ABSTRACTJapan 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.

ABSTRACT 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.

ABSTRACTDrive‐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.

ABSTRACTMicrogrids 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%.

ABSTRACT‘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.

ABSTRACT 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.