Search Results (272)

The broadening adoption of interconnected systems within smart city environments is fundamental for the progression of digitally driven economies, enabling the refinement of city administration, the enhancement of public service delivery, and the fostering of ecologically sustainable progress, thereby aligning with global sustainability benchmarks. However, the pervasive distribution of Internet of things (IoT) apparatuses introduces substantial security risks, attributable to the confidential nature of processed data and the heightened susceptibility to cybernetic intrusions targeting essential infrastructure. Commonly, these devices exhibit deficiencies stemming from restricted computational capabilities and the absence of uniform security standards. The resolution of these security challenges is paramount for the full realization of the advantages afforded by IoT without compromising system integrity. Cryptographic protocols represent the most viable solutions for the mitigation of these security vulnerabilities. However, the limitations inherent in IoT edge nodes complicate the deployment of robust cryptographic algorithms, which are fundamentally reliant on finite-field multiplication operations. Consequently, the streamlined execution of this operation is pivotal, as it will facilitate the effective deployment of encryption algorithms on these resource-limited devices. Therefore, the presented research concentrates on the formulation of a spatially and energetically efficient hardware implementation for the finite-field multiplication operation. The proposed arithmetic unit demonstrates significant improvements in hardware efficiency and energy consumption compared to state-of-the-art designs, while its systolic architecture provides inherent timing-attack resistance through deterministic operation. The regular structure not only enables these performance advantages but also facilitates future integration of error-detection and masking techniques for comprehensive side-channel protection. This combination of efficiency and security makes the multiplier particularly suitable for integration within encryption processors in resource-constrained IoT edge nodes, where it can enable secure data communication in smart city applications without compromising operational effectiveness or urban development goals. Full article

The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy optimization. We used real-world transit data from the General Transit Feed Specification (GTFS) to model the maintenance scheduling and energy management problem as a Markov Decision Process. This included important operational metrics like peak-hour demand, train arrival times, and station stop densities. A custom reinforcement learning environment mimics the changing conditions of metro operations. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) sophisticated deep reinforcement learning techniques were used to identify the optimal policies for decreasing energy consumption and downtime. The PPO hyperparameters were additionally optimized using Bayesian optimization by implementing Optuna, which produces a far greater performance than baseline DQNs and basic PPO. Comparative tests showed that our improved DRL-based method improves the accuracy of predictive maintenance and the efficiency of energy use, which lowers operational costs and raises the dependability of the service. These results show that advanced learning and optimization techniques could be added to public transportation systems in cities. This could lead to more sustainable and smart transportation management in big cities. Full article

The integration of artificial intelligence (AI) technologies is reshaping diverse domains of human activity, including natural resource management, urban and rural planning, agri-food systems, industry, energy, education, and healthcare. However, the impact of AI on the sustainability of local ecosystems remains insufficiently systematized. This highlights the need for a comprehensive review that considers spatial, sectoral, and socio-economic characteristics of regions, as well as interdisciplinary approaches to sustainable development. This study presents a scoping review of 198 peer-reviewed publications published between 2010 and March 2025, focusing on applied cases of AI deployment in local contexts. Special attention is given to the role of AI in monitoring water, forest, and agricultural ecosystems, facilitating the digital transformation of businesses and territories, assessing ecosystem services, managing energy systems, and supporting educational and social sustainability. The review includes case studies from Africa, Asia, Europe, and Latin America, covering a wide range of technologies—from machine learning and digital twins to IoT and large language models. Findings indicate that AI holds significant potential for enhancing the efficiency and adaptability of local systems. Nevertheless, its implementation is accompanied by notable risks, including socio-economic disparities, technological inequality, and institutional limitations. The review concludes by outlining research priorities for the sustainable integration of AI into local ecosystems, emphasizing the importance of cross-sectoral collaboration and scientific support for regional digital transformations. Full article

The smart city era has already begun and its societal and environmental implications in urban development are expected to be huge. In this context, Internet of Things (IoT) technologies have become the major path towards novel e-Government practices, to improve citizens’ quality of life, increase the efficiency of infrastructure and services, promote sustainable economic growth, and integrate multiple city sectors, creating an interconnected and smart urban environment. This work offers an up-to-date survey of smart city definitions, their development framework, their characteristics, and their areas of application. Furthermore, it provides the current state of smart city applications in Greek municipalities and a proposed comparison among them, in terms of well-accepted key performance indicators, while it comments on their suitability in the context of e-Government and the challenges that must be faced regarding their efficient implementation. Full article

The Multi-Objective Optimization Problem (MOOP) in Wireless Sensor Networks (WSNs) is a challenging issue that requires balancing multiple conflicting objectives, such as maintaining coverage, connectivity, and network lifetime all together. These objectives are important for a functioning WSN safety-critical applications, whether in environmental monitoring, military surveillance, or smart cities. To address these challenges, we propose a novel bio-inspired Bird Flocking Node Scheduling algorithm, which takes inspiration from the natural flocking behavior of birds migrating over long distance to optimize sensor node activity in a distributed and energy-efficient manner. The proposed algorithm integrates the Lyapunov function to maintain connected coverage while optimizing energy efficiency, ensuring service availability and reliability. The effectiveness of the algorithm is evaluated through extensive simulations, namely MATLAB R2018b simulator coupled with a Pareto front, comparing its performance with our previously developed BAT node scheduling algorithm. The results demonstrate significant improvements across key performance metrics, specifically, enhancing network coverage by 8%, improving connectivity by 10%, and extending network lifetime by an impressive 80%. These findings highlight the potential of bio-inspired Bird Flocking optimization techniques in advancing WSN dependability, making them more sustainable and suitable for real-world WSN safety-critical systems. Full article

In recent years, several emerging transport modes have appeared in cities all over the world and have been widely adopted by commuters and travelers. This leads to strong growth and popularity of multi-modal transport and Mobility as a Service (MaaS) in cities. These emerging transport modes have not only received much attention from service providers and practitioners but have also attracted researchers in related communities. These are reflected in the growing number of published papers related to research issues of multi-modal mobility transport in cities. The factors that have been driving the strong growth of the number of published papers related to the emerging multi-modal transport in cities are the deficiencies of effective solution methods to accommodate the needs of users in cities with multi-modal transport modes. Although the existing literature is still deficient in offering seamless end-to-end multi-modal mobility transport services, it provides valuable sources and clues for finding the potential future research subjects/issues/directions. In this study, we attempt to identify potential research directions based on a review of the existing literature on multi-modal mobility transport. By searching the WOS database, we analyze the profile and trends of research directions related to multi-modal mobility. The results of this study pave the way for the assessment of research subjects/issues/directions under the umbrella term of multi-modal mobility transport. This review paper significantly reduces the time required for readers to identify prospective research subjects, issues, or directions without delving into the literature. Full article

Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. Full article

The smart-city revolution has been promoted as the next step in urban development, leveraging technology to achieve enhanced development standards amid the increasingly complex challenges of urbanization. However, despite the implementation of more efficient urban services, issues regarding their tangible effects and impact on people’s lives remain unresolved. In this context, the concept of intelligent cities is seen as a necessary evolution of the smart-city paradigm, positioning human factors as the driving forces behind urban technological evolution. This integrative concept embodies advanced technology to enhance essential urban functions, with sustainability, equity, and resilience as macro-development goals. This study reviews the multifaceted dimensions of intelligent cities, from designing and deploying smart infrastructure to implementing citizen-centric decision-making processes. Additionally, it critically examines the digital divide and highlights the importance of equitable development policies as essential for enabling transformative urban change. By linking technological advancement to social issues, this article provides practical insights and case studies from the cities of Helsinki, Barcelona, and Buenos Aires, demonstrating that smart-city initiatives are still failing to bridge the equity service distribution gap. This comprehensive assessment approach ultimately serves as a reference for future evaluations of intelligent urban transformations. Full article

This study evaluates the progress of Korea’s National Strategic Smart City Program (NSSCP), a flagship R&D initiative, in advancing sustainable and intelligent urban development on a global scale. Utilizing the United Nations’ United for Smart Sustainable Cities (U4SSC) framework, which integrates both sustainability and smartness in city development, this research examines the program’s alignment with global standards. The findings reveal that the NSSCP contributes to the attainment of the Sustainable Development Goals (SDGs) in areas such as health, energy, innovation, and sustainable communities. It also effectively addresses key dimensions of smart cities, including smart living, environmental stewardship, mobility, and economic vitality. Despite these achievements, this study identifies critical challenges, such as the absence of robust evaluation tools and an overemphasis on quantitative targets. This research is important in advancing the discourse on smart city development, offering insights into the efficacy of smart services and systems through the lens of the NSSCP’s cloud-based open data hub model. Full article

With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. CIM represents an advanced urban management paradigm that integrates Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to establish a multidimensional digital framework for comprehensive urban data management and intelligent decision making. While the existing research has primarily focused on technical architectures, governance models, and application scenarios, a systematic exploration of CIM’s data-driven characteristics remains limited. This paper reviews the evolution of CIM from a data-centric view introducing a research framework that systematically examines the data lifecycle, including acquisition, processing, analysis, and decision support. Furthermore, it explores the application of CIM in key areas such as smart transportation and digital twin cities, emphasizing its deep integration with big data, artificial intelligence (AI), and cloud computing to enhance urban governance and intelligent services. Despite its advancements, CIM faces critical challenges, including data security, privacy protection, and cross-sectoral data sharing. This survey highlights these limitations and points out the future research directions, including adaptive data infrastructure, ethical frameworks for urban data governance, intelligent decision-making systems leveraging multi-source heterogeneous data, and the integration of CIM with emerging technologies such as AI and blockchain. These innovations will enhance CIM’s capacity to support intelligent, resilient, and sustainable urban development. By establishing a theoretical foundation for CIM as a data-intensive framework, this survey provides valuable insights and forward-looking guidance for its continued research and practical implementation. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

Smart cities are equipped with a vast number of IoT devices, which help to collect and analyze data to improve the quality of life for urban people by offering a sustainable and connected environment. However, the rapid growth of IoT systems has issues related to the Quality of Service (QoS) and allocation of limited resources in IoT-based smart cities. The cloud in the IoT system also faces issues related to higher consumption of energy and extended latency. This research presents an effort to overcome these challenges by introducing opposition-based learning incorporated into Golden Jackal Optimization (OL-GJO) to assign distributed edge capabilities to diminish the energy consumption and delay in IoT-based smart cities. In the context of IoT-based smart cities, a three-layered architecture is developed, comprising the IoT system, the Unmanned Aerial Vehicle (UAV)-assisted edge layer, and the cloud layer. Moreover, the controller positioned at the edge of UAV helps determine the number of tasks. The proposed approach, based on opposition-based learning, is put forth to offer effective computing resources for delay-sensitive tasks. The multi-joint symmetric optimization uses OL-GJO, where opposition-based learning confirms a symmetric search process is employed, improving the task scheduling process in UAV-assisted edge computing. The experimental findings exhibit that OL-GJO performs in an effective manner while offloading resources. For 200 tasks, the delay experienced by OL-GJO is 2.95 ms, whereas Multi Particle Swarm Optimization (M-PSO) and the auction-based approach experience delays of 7.19 ms and 3.78 ms, respectively. Full article

Effective municipal solid waste (MSW) management is crucial for urban sustainability, especially in fast-growing cities, like Hangzhou, China. This study examines residents’ willingness to pay (WTP) for the following five key MSW measures: differentiated waste charging, smart recycling points, on-site organic waste recovery, volunteer-based waste sorting supervision, and a community self-governance fund. Based on a survey of 521 residents across 13 districts, we use logistic and interval regression models to identify factors influencing WTP and payment amounts. Key findings include the following: Higher-income and more educated residents prefer cost-efficient, technology-driven solutions, like smart recycling and differentiated charging. Newcomers (≤5 years of residence) show higher WTP and greater sensitivity to environmental information, highlighting the need for targeted outreach. Providing explicit environmental benefits (e.g., waste reduction, increased recycling) significantly boosts WTP rates and payment levels. Community characteristics matter—residents in high-density areas favor waste charging, while those in older neighborhoods support volunteer programs and self-governance funds. Policy implications center on targeted outreach, transparent fee structures, and incentive programs to foster public trust and enhance participation. Although MSW management in Hangzhou remains predominantly government-led, select collaboration with private enterprises (e.g., in specialized recycling services) may offer additional efficiency gains. By aligning these measures with localized preferences and demographic patterns, Hangzhou—and other quickly urbanizing regions—can develop robust and inclusive MSW systems that contribute to broader sustainable development objectives. Full article

The issue of legal easements is a relatively rarely discussed topic among the professional public, and yet, even today, legal easements create space for the development of smart cities. Legal easements are restrictions on property rights that arise directly from the law, which means that the possible disagreement of the owner of the property concerned is irrelevant. The aim of this scientific study is to provide, based on a study of legislation, case law, and professional and scientific articles, sufficient information on this legal institution, which has its basis in the Civil Code. The scientific study also examines in detail the issue of legal easements and their role in the context of sustainable land use and infrastructure development in smart cities. In the study, we test the stated hypothesis that “Legal easements, as a legal instrument, effectively promote sustainable land use and infrastructure development in smart cities by enabling the integration of renewable energy, eco-mobility and green infrastructure without negatively impacting property rights, thus contributing to reducing conflicts between private property and public interest”. We use a number of scientific methods of research to analyse the current legal situation and the possibilities for the application of legal easements in the context of smart cities, including legal analysis, the comparative method, the method of synthesis, deduction, and historical interpretation. In particular, the methods in question were used to examine, historically describe and compare the current legislation on easements and their use in the management of urban space and infrastructure. The main results of the research include a detailed overview of the current legal status of easements and their limitations, which affect the possibilities of their application in the conditions of smart cities. The results suggest that if easements are effectively implemented they can make a significant contribution to optimising space, regulating access to public services, and protecting natural resources. This tool has the potential to enhance the quality of life in cities and promote sustainable urbanism through adaptive planning and management of urban space. Full article

A wireless-powered communication network (WPCN) provides sustainable power solutions for energy-intensive Internet of Things (IoT) devices in remote or inaccessible locations. This technology is particularly beneficial for applications in smart transportation and smart cities. Nevertheless, WPCN experiences performance degradation due to severe path loss and inefficient long-range energy and information transmission. To address the limitation, this paper investigates an intelligent reflecting surface (IRS)-enhanced multi-cell WPCN integrated with non-orthogonal multiple access (NOMA). The emerging IRS technology mitigates propagation losses through precise phase shift adjustments with symmetric reflective components. Asymmetric resource utilization in symmetric downlink and uplink transmissions is crucial for optimal throughput and quality of service. Alternative iterations are employed to optimize time allocation and IRS phase shifts in both downlink and uplink transmissions. This approach allows for the attainment of maximum sum throughput. Specifically, the phase shifts are optimized using two algorithms called semidefinite relaxation (SDR) and block coordinate descent (BCD). Our simulations reveal that integrating the IRS into multi-cell NOMA-WPCN enhances user throughput. This surpasses the performance of traditional multi-cell WPCN. In addition, the coordinated deployment of multiple hybrid access points (HAPs) and IRS equipment can expand communications coverage and network capacity. Full article