Smart Cities Research Articles

Purpose This paper aims to explore the complexities of risk management and value-for-money (VFM) frameworks in the context of smart city projects developed through public–private partnerships (PPPs). The research aims to address the challenges associated with pricing dynamic risks in smart infrastructure while proposing an adaptive framework that integrates both financial and non-financial performance metrics. Design/methodology/approach This theoretical study develops a risk costing framework for smart city PPPs by applying financial models such as options pricing theory, capital asset pricing model and portfolio theory. It uses mathematical modeling techniques, including expected value analysis, utility functions and discount rate calculations, to conceptualize risk as a tradable asset and simulate optimal risk allocation scenarios without collecting empirical data. Findings The research reveals that traditional PPP risk management models are insufficient for smart city projects due to rapid technological change, cybersecurity risks and multi-stakeholder coordination challenges. An adaptive risk management approach, incorporating mid-project adjustments and continuous evaluation, is essential to achieving project success. The findings also underscore the importance of expanding VFM frameworks beyond financial efficiency to include social, environmental and technological outcomes. Practical implications The paper offers actionable recommendations for policymakers and private sector stakeholders, including adaptive contract clauses, governance structures for risk monitoring and mechanisms to promote innovation while mitigating vendor lock-in risks. It emphasizes the need to align risk-sharing strategies with long-term sustainability goals. Originality/value This study bridges theoretical and practical gaps between theoretical financial frameworks and the operational realities of smart city PPP projects by integrating advanced financial models with real-world smart city experiences. It contributes to the emerging discourse on smart city development by proposing a dynamic framework that balances risks, returns and sustainability objectives within PPPs.

The increasing emphasis on smart city development has placed digital citizen participation at the forefront of sustainable urban governance. This study examines the role of digital citizen participation (DCP) in enhancing the social sustainability of smart city initiatives in Lafia Metropolis, Nasarawa State, Nigeria. Using a mixed-method approach comprising structured questionnaires, digital participatory tools, and stakeholder interviews, the research investigates the relationship between citizen engagement, digital inclusivity, and urban sustainability outcomes. The study integrates theoretical perspectives from Arnstein’s Ladder of Participation (1969) and Nam and Pardo’s Smart City Framework (2011) to assess participation depth, inclusivity, and institutional responsiveness. Simulated results, supported by empirical evidence from existing literature and field context, reveal that approximately 68% of respondents perceive digital participation platforms (such as online town halls, mobile feedback systems, and GIS- based urban mapping) as effective in influencing municipal planning and service delivery. However, disparities in digital literacy, internet accessibility, and institutional transparency remain significant barriers. The findings underscore that DCP strengthens social cohesion, trust, and accountability, thereby fostering social sustainability within urban governance frameworks. The study recommends that local governments adopt inclusive ICT policies, capacity-building programs, and open-data initiatives to deepen citizen involvement in decision-making. By embedding digital participation into the smart city agenda, emerging urban centers like Lafia can accelerate sustainable urban transformation and equitable governance aligned with UN Sustainable Development Goals (SDGs) 11 and 16.

Efficient real-time computer vision-based passenger flow analysis is increasingly important for the management of intelligent transportation systems and smart cities. This paper presents the design and implementation of a system for real-time object detection, tracking, and people counting in tram stations. The proposed approach integrates YOLO-based detection with a lightweight tracking module and is deployed on an NVIDIA Jetson Nano device, enabling operation under resource constraints and demonstrating the potential of edge AI. Multiple YOLO versions, from v3 to v11, were evaluated on data collected in collaboration with Metropolitano de Tenerife. Experimental results show that YOLOv5s achieves the best balance between detection accuracy and inference speed, reaching 96.85% accuracy in counting tasks. The system demonstrates the feasibility of applying edge AI to monitor passenger flow in real time, contributing to intelligent transportation and smart city initiatives.

Abstract. The evolving low-altitude economy enables Unmanned Aerial Vehicles (UAVs) to gather diverse sensor data, including RGB images, 3D point clouds, and inertial measurements, offering untapped potential for environmental mapping. Traditional urban 3D modeling methods often face delays in updates and scalability issues. This paper introduces a novel UAV-based collaborative mapping framework that integrates heterogeneous data from multiple UAVs to efficiently reconstruct dynamic urban environments. The framework employs advanced visual recognition and optical character recognition (OCR) for semantic feature extraction, complemented by LiDAR inertial odometry for precise map construction. To address challenges posed by sparse LiDAR data in indoor settings, a temporal alignment mechanism is employed to generate synchronized keyframes, enhancing data coherence. Additionally, camera-LiDAR calibration combined with cross-modal registration and semantic-guided point cloud stitching boosts system robustness. Experimental results demonstrate that feature-guided point cloud registration, bolstered by semantic alignment, surpasses traditional methods, achieving efficient mapping and offering a scalable solution for urban 3D modeling, applicable in real-time urban planning and smart city development.

The increasing adoption of smart home technologies introduces significant cybersecurity and forensic challenges. This necessitates a shift from traditional reactive digital forensics to a more proactive approach to safeguarding these environments. This research is situated within Saudi Arabia’s ambitious digital transformation, as outlined in Vision 2030, which promotes the development of smart cities and homes. The unique technological landscape and national initiatives in Saudi Arabia require tailored cybersecurity solutions. Existing models are often too theoretical, generic, or overly specialized, lacking practical validation and comprehensive integration for modern IoT ecosystems. There is a pronounced lack of a scalable, validated framework designed explicitly for proactive digital forensic readiness in smart homes. The study employs a mixed-methodology approach, combining a PRISMA systematic literature review with Design Science Research (DSR) to develop and validate the Smart Proactive Forensic Metamodel for Smart Homes (SPFMSH). The developed SPFMSH was tested against realistic cyberattack scenarios, including unauthorized access and intrusion, data exfiltration, and device hijacking by ransomware. In each scenario, the model demonstrated its capability to proactively detect threats, automatically preserve forensic evidence, and provide structured investigative timelines. This validation proved its effectiveness in transforming security incidents into forensically sound investigations within the Saudi smart home context. SPFMSH delivers a practical, holistic framework that addresses the limitations of previous models, moving beyond theory to offer an implementable solution. Its development is a significant step towards enhancing national cybersecurity resilience and supporting the secure adoption of smart home technologies in alignment with Saudi Vision 2030.

Smart City Planning through Digital Twins: Enhancing Urban Resilience and Sustainability

Abstract Traffic management and street lighting optimization are increasingly dependent on intelligent systems for smart cities. The main objective of this paper is to design the Intelligent Traffic and Lighting Systems (ITLS) using the dream optimization algorithm (DOA) for Wireless Sensor Networks (WSNs). The system assimilates single-lane roads, moving vehicles, sensor-equipped streetlights, and a centralized control station. The fitness function of DOA optimizes network performance by balancing energy consumption, reducing congestion, and stabilizing vehicle speed variations. The network adjusts to changing traffic conditions, optimizing routes and lighting efficiency. The MATLAB simulation shows that DOA surpasses traditional rule-based systems by refining traffic flow while reducing energy usage. The performance of our proposed approach, DOA-ITLS, is compared with existing techniques like IB-SEC and KFFOA-PDES in terms of network lifetime, packet delivery, throughput, and energy efficiency. The protocol enhances packet delivery by 50% and extends network lifetime by effectively delaying node failures. DOA-ITLS is found to be a scalable, robust, and energy efficient solution for urban traffic and lighting control. By enhancing data delivery and system responsiveness, this framework makes urban mobility more sustainable and efficient.

An interpretable generalized additive neural networks for electricity theft detection in smart cities using balanced data and intelligent grid management

A multi-agent architecture for context sources integration in smart cities

Enhancing smart grid security in smart cities: A review of traditional approaches and emerging technologies

The extensive use of information and communication technology (ICT) has transformed every aspect of life as the world moves closer to ‎digitalization. There is no denying that ICT has changed how people communicate, live, work, and study. The Internet of Things, or things, ‎is a powerful combination of critical ICT technologies, with integrated hardware, software, and other technologies for connecting and ‎sharing data with other systems and devices over the Internet. An Internet of Things (IoT) device is any electronic device that can be used in ‎a wide range of social contexts, including connected industries, transportation, healthcare, smart supply chains, smart farms, smart cities, ‎smart grids, and many more. This includes wearable technology and hardware. The Internet of Medical Things (IoMT) is a real-world ‎application of the Internet of Things (IoT) in the healthcare sector, enabling patients to receive better healthcare and enjoy a higher quality of ‎life. It provides seniors and patients with real-time healthcare services, support, and caregiving using Internet-enabled smart devices. The ‎current coronavirus disease (COVID-19) has increased the demand for remote patient care due to a paucity of resources and healthcare ‎facilities, in contrast to the enormous global demand for these services and facilities. Therefore, COVID-19 has played a significant role in ‎the shift of the present healthcare system towards remote care. Even with advancements in the healthcare sector and the apparent benefits of ‎integrating it with IoT, moving all forms of communication online is a logical next step. It opens the door for potential security lapses in the ‎ongoing IoMT communication, providing adversaries with unauthorized access to vital health data that could be misused for malicious ‎intent‎.

ABSTRACT This study proposes an Ant Colony Optimization (ACO) approach for detecting covariance in 6G-enabled Internet of Things (IoT) networks, which is crucial for optimizing performance and resource allocation. Traditional methods struggle with the dynamic, high-speed demands of 6G. Inspired by ant behavior, ACO uses dynamic agents to explore covariance structures, with pheromone-based guidance enhancing solution quality. The method involves three steps: constructing initial solutions, refining paths through local search, and updating pheromones to achieve convergence. A smart city simulation compares Ant Colony Optimization (ACO) to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Hill Climbing (HC). Results show that ACO outperforms these models, achieving a precision of 0.6, a recall of 70.10%, a specificity of 98.01%, an accuracy of 87.50%, and an F1 score of 65.96%. These findings highlight ACO’s effectiveness in optimizing resource management and decision-making for smart cities.

Integrated Corvus - North West America Mackenzie Valley Wolf Inspired Optimization and Hybrid Quasi Opposition Based White Headed Buffalo Weaver - Indian Leopard Cat Swarm Algorithm for Power Loss Diminution in Electrical Grid Transmission Network in Smart City

Big data and Internet of Things applications in smart cities: Recent advances, challenges, and critical issues

From Integrated Circuits to Smart Cities: Two Inspiring ISTPs Were Held at IMS 2025 [MTT-S Society News

Top-level smart city traffic management system with radio frequency identification – based road event detection

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure.

A federated vehicle re-identification benchmark and performance optimization using dual-phase contrastive dynamic aggregation.

Remaking the smart city through the COVID-19 pandemic: Seoul, Singapore, Taipei

Transforming urban neighborhoods from ‘bed towns’ to eco-friendly smart cities in Korea