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Sustainable Energy Production in Smart Cities

Author

Listed:
  • Ramiz Salama

    (Department of Computer Engineering, AI and Robotics Institute, International Research Center for AI and IoT, Near East University, Mersin 10, Nicosia 99138, Turkey)

  • Fadi Al-Turjman

    (Artificial Intelligence Engineering Department, AI and Robotics Institute, International Research Center for AI and IoT, Near East University, Mersin 10, Nicosia 99138, Turkey
    Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey)

Abstract

Finding a method to provide the installed Internet of Things (IoT) nodes with energy that is both ubiquitous and long-lasting is crucial for ensuring continuous smart city optimization. These and other problems have impeded new research into energy harvesting. After the COVID-19 pandemic and the lockdown that all but ended daily activity in many countries, the ability of human remote connections to enforce social distancing became crucial. Since they lay the groundwork for surviving a lockdown, Internet of Things (IoT) devices are once again widely recognised as crucial elements of smart cities. The recommended solution of energy collection would enable IoT hubs to search for self-sustaining energy from ecologically large sources. The bulk of urban energy sources that could be used were examined in this work, according to descriptions made by researchers in the literature. Given the abundance of free resources in the city covered in this research, we have also suggested that energy sources can be application-specific. This implies that energy needs for various IoT devices or wireless sensor networks (WSNs) for smart city automation should be searched for near those needs. One of the important smart, ecological and energy-harvesting subjects that has evolved as a result of the advancement of intelligent urban computing is intelligent cities and societies. Collecting and exchanging Internet of Things (IoT) gadgets and smart applications that improve people’s quality of life is the main goal of a sustainable smart city. Energy harvesting management, a key element of sustainable urban computing, is hampered by the exponential rise of Internet of Things (IoT) sensors, smart apps, and complicated populations. These challenges include the requirement to lower the associated elements of energy consumption, power conservation, and waste management for the environment. However, the idea of energy-harvesting management for sustainable urban computing is currently expanding at an exponential rate and requires attention due to regulatory and economic constraints. This study investigates a variety of green energy-collecting techniques in relation to edge-based intelligent urban computing’s smart applications for sustainable and smart cities. The four categories of energy-harvesting strategies currently in use are smart grids, smart environmental systems, smart transportation systems, and smart cities. In terms of developed algorithms, evaluation criteria, and evaluation environments, this review’s objective is to discuss the technical features of energy-harvesting management systems for environmentally friendly urban computing. For sustainable smart cities, which specifically contribute to increasing the energy consumption of smart applications and human life in complex and metropolitan areas, it is crucial from a technical perspective to examine existing barriers and unexplored research trajectories in energy harvesting and waste management.

Suggested Citation

  • Ramiz Salama & Fadi Al-Turjman, 2023. "Sustainable Energy Production in Smart Cities," Sustainability, MDPI, vol. 15(22), pages 1-25, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16052-:d:1282339
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    References listed on IDEAS

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    2. Lorenzo Becchi & Elisa Belloni & Marco Bindi & Matteo Intravaia & Francesco Grasso & Gabriele Maria Lozito & Maria Cristina Piccirilli, 2024. "A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems," Sustainability, MDPI, vol. 16(23), pages 1-21, November.

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