Open Access

Review

Article ID: 2334

Navigating the future of smart cities: Addressing IoT challenges through blockchain solutions

by Yasaman Ghaderi, Mohammad Reza Ghaderi

Information System and Smart City, Vol.5, No.1, 2025;

As urbanization accelerates, smart cities are increasingly turning to innovative technologies to enhance city management, governance, and citizen engagement. This paper explores the application of blockchain technology in smart cities, particularly its interaction with the Internet of Things (IoT). Despite significant strides in utilizing blockchain for specific applications, existing frameworks often focus on narrow sectors, such as energy management or data security, lacking a holistic integration across municipal functions. This research identifies a critical gap in existing literature: the need for a comprehensive blockchain framework that connects multiple urban sectors, facilitates secure data exchange, and empowers citizens through decentralized systems. The proposed framework underscores the potential of blockchain to create a transparent, efficient, and citizen-friendly urban environment by implementing decentralized identity management, smart contracts for public services, and innovative citizen engagement platforms. By addressing this gap, the study contributes to the discourse on smart city development by providing an adaptable model that can be tailored to the unique needs of diverse urban environments.

Open Access

Commentary

Article ID: 2213

Explaining the role of reducing energy consumption in strengthening healthy cities in smart cities

by Hadi RezaeiRad, Seyedeh Zahra Akbarian

Information System and Smart City, Vol.5, No.1, 2025;

Different dimensions and interventions can lead to the outcome of being a healthy, smart city. One of them is the smart management of energy, via a smart infrastructure—the system component that certainly should be considered in a smart city. Improving the quality of life is one of the big aims of smart cities. For this reason, the management of energy is one of the factors that directly and indirectly affect it and public health. The present study is a descriptive-analytical type, and through a meta-synthesis methodology, the analysis explores past studies on decreasing energy consumption and moving toward healthy and smart city initiatives. The conclusion provides actionable recommendations for realizing this vision. Studies indicate that achieving smart, healthier cities requires the integration of smart grids and renewable energy, the implementation of data-driven energy management systems, and the promotion of citizen engagement and behavioral change.

Open Access

Article

Article ID: 2356

Statistical approach to the diagnosis of the dynamics of urban mobility under the influence of the road congestion situation in the city of Douala, Cameroon

by Frédéric Laurent Esse Esse, Cyrille Mezoue Adiang, Moussa Sali, Fabien Kenmogne, Blaise Ngwem Bayiha, Gilbert Tchemou, Emmanuel Yamb Bell

Information System and Smart City, Vol.5, No.1, 2025;

Smooth movement is an essential function and an indicator of a healthy city. Cities being engines of growth, congestion is a real cancer for the country’s economy. In this sense, linking travel habits and the increase in the level of congestion in a city is very important. The objective of this work is to diagnose, using a statistical approach, the dynamics of urban mobility influenced by the traffic congestion situation in the city of Douala. For this, the study focuses on two aspects; the first aspect concerns the multivariate descriptive analysis of motorists’ travel habits. The methods used involve first submitting surveys to motorists in vehicle technical inspection centers and processing the data in IBM SPSS statistical software. Follow-up of the analysis of the correlation between the congestion level and some required solutions. The second aspect focuses on the principal component analysis (PCA) that is performed. The determination of Kaiser-Meyer-Olkin (KMO) indices, the Bartlett significances, and the Pearson correlation coefficients are also done. The results show that the travel habits of motorists create a massive use of roads at certain specific time slots, in addition to extra-municipal trips mainly oriented towards the city center and the administrative district due to the monocentric situation of the city, which contributes to the increase in the level of congestion in the city. The correlation shows that there is significance between the level of congestion and the solutions considered, but this correlation is more or less moderate, which shows that the solutions considered can be used in the short term to alleviate congestion in the city.

Open Access

Article

Article ID: 2253

Energy-optimizing machine learning-driven smart traffic control system for urban mobility and the implications for insurance and risk management

by Chizoba P. Chinedu, Queensley C. Chukwudum, Eberechukwu Q. Chinedu

Information System and Smart City, Vol.5, No.1, 2025;

Heavy traffic during peak hours, such as early mornings and late evenings, is a significant cause of delays for commuters. To address this issue, the prototype of a dual smart traffic light control system is constructed, capable of dynamically adjusting traffic signal duration based on real-time vehicle density at intersections, as well as the brightness of the streetlights. The system uses a pre-trained Haar Cascade machine learning classifier model to detect and count vehicles through a live video feed. Detected cars are highlighted with red squares, and their count is extracted. The vehicle data is then transmitted to an Arduino microcontroller via serial communication, facilitated by the pySerial library. The Arduino processes this information and adjusts the timing of the traffic lights accordingly, optimizing traffic flow based on current road conditions. A novel approach involves optimizing energy usage through real-time data integration with the power grid. Street lighting is then dynamically adjusted at night times—brightening during high-traffic periods and dimming during low-traffic times. The brightness levels are set at 30%, 50%, 75%, and 100% based on the number of cars detected, with above 50% indicating the presence of cars. This adaptive control enhances energy efficiency by reducing energy consumption while maintaining road safety. The simulated and experimental results are provided. The former demonstrated a lower accuracy compared to the latter, particularly during the transition to the green light, across all traffic density levels. Additionally, the simulation was only capable of representing discrete lamp brightness levels of 0%, 50%, and 100%, in contrast to the experimental results, which showed a clear differentiation between 50%, 75%, and 100% brightness levels. Details of the model limitations are outlined with proposed solutions. The implications of the optimized system for auto insurance, liability coverage, and risk management are explored. These are areas that are rarely addressed in current research.

Open Access

Article

Article ID: 1737

Data-driven insights: Unravelling traffic dynamics with k-means clustering and vehicle type differentiation

by Anwar Mehmood Sohail, Khurram Shehzad Khattak, Zawar Hussain Khan

Information System and Smart City, Vol.4, No.1, 2024;

Urban traffic poses persistent challenges, necessitating innovative approaches for effective traffic flow analysis and management. This research adopts a data-driven methodology, employing different algorithms such as K-Means clustering, multiple linear regression to analyse real-world traffic flow. The study utilizes road traffic data collected over seven days, spanning seven hours each day, comprising traffic count, vehicle speed, and categorization by vehicle type. Through rigorous data preprocessing and K-Means clustering, the research identifies distinct traffic clusters, revealing patterns beyond average counts and speeds. Notably, the differentiation of vehicle types within clusters provides nuanced insights into transport mode interactions. The findings contribute to the traffic flow analysis field and offer practical implications for informed urban traffic management strategies. Understanding traffic dynamics aids in developing effective congestion mitigation measures. The study concludes by highlighting potential areas for future research and improvements in optimizing traffic dynamics, emphasizing the importance of data-driven approaches in addressing urban traffic challenges.

Open Access

Article

Article ID: 1777

Study on traffic efficiency of driver groups at different green light time based on entropy change model

by Xiaojuan Li, Xinliang Guo, Jingwen Zhu, Aijuan Li, Yicheng Mao, Zilin Zhang

Information System and Smart City, Vol.4, No.1, 2024;

The difference in psychological behavior when drivers cross the road has a certain impact on the efficiency of crossing the road. On the basis of analyzing the subjective and objective factors of drivers, the entropy change model of green light time is established and verified. The model can simply judge the time when the driver faces different traffic lights, so as to effectively calculate the drivers driving speed, analyze the traffic situation, and improve the traffic efficiency.