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<p>Advanced technologies are increasingly becoming integrated into conventional cities, with the Internet of Things (IoT) as a critical enabler of smart cities. IoT provides a platform where the transport systems are connected, and context information is gathered from these networks. Nevertheless, the transportation sector has been one of the most influential sectors in global economic and social development. It is one of the leaders in the negative impact on the environment, and therefore, it is one of the priority areas of sustainability. The existing transport systems are confronted with some significant issues, such as security, traffic control, routing, infrastructure, evacuation, and security. All these problems can be solved using IoT-based sustainable high-speed transportation networks. Therefore, policymakers and researchers have become concerned about seeking sustainable solutions to transportation. Thus, energy consumption and time requirements remain a barrier to the development of efficient IoT-based transportation systems. These challenges can only be met by analyzing energy harvesting methodologies, low-power devices, and efficient communication protocols. In response to these problems, this research presents a new strategy to minimize energy usage and travel. Because the problem of building a sustainable IoT-based urban transportation network occurs in varied contexts, this research proposes a new approach to reduce energy consumption and travel time by employing a well-known K-Nearest Neighbor and Crow Search Algorithm (CSA-KNN). Due to the simple search method and the slow convergence of the CSA, this paper combines it with the KNN. CSA adapts the life-of-crows metaheuristic algorithm to design ecologically sound IoT-based urban transportation networks, while KNN is a data mining technique predominantly used for categorization, demonstrating significant efficacy in classifying data according to the k-value. KNN is known for its effectiveness in local search, while CSA is designed for global optimization. Integrating both approaches facilitates a more thorough investigation of the solution space, balancing between exploiting promising regions and exploring diverse areas. The proposed method was implemented using MATLAB based on the evening rush hour dataset in Santander. According to the findings, the recommended method is more efficient than the existing techniques. It can improve the travel time and the energy metric by 5.02% and 2.48%, respectively, compared to other state-of-the-art algorithms.</p>

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A novel sustainable approach for developing internet of things-based transportation networks based on a hybrid energy-aware algorithm

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Yongxing Lin,
  • Yan Shi

摘要

Advanced technologies are increasingly becoming integrated into conventional cities, with the Internet of Things (IoT) as a critical enabler of smart cities. IoT provides a platform where the transport systems are connected, and context information is gathered from these networks. Nevertheless, the transportation sector has been one of the most influential sectors in global economic and social development. It is one of the leaders in the negative impact on the environment, and therefore, it is one of the priority areas of sustainability. The existing transport systems are confronted with some significant issues, such as security, traffic control, routing, infrastructure, evacuation, and security. All these problems can be solved using IoT-based sustainable high-speed transportation networks. Therefore, policymakers and researchers have become concerned about seeking sustainable solutions to transportation. Thus, energy consumption and time requirements remain a barrier to the development of efficient IoT-based transportation systems. These challenges can only be met by analyzing energy harvesting methodologies, low-power devices, and efficient communication protocols. In response to these problems, this research presents a new strategy to minimize energy usage and travel. Because the problem of building a sustainable IoT-based urban transportation network occurs in varied contexts, this research proposes a new approach to reduce energy consumption and travel time by employing a well-known K-Nearest Neighbor and Crow Search Algorithm (CSA-KNN). Due to the simple search method and the slow convergence of the CSA, this paper combines it with the KNN. CSA adapts the life-of-crows metaheuristic algorithm to design ecologically sound IoT-based urban transportation networks, while KNN is a data mining technique predominantly used for categorization, demonstrating significant efficacy in classifying data according to the k-value. KNN is known for its effectiveness in local search, while CSA is designed for global optimization. Integrating both approaches facilitates a more thorough investigation of the solution space, balancing between exploiting promising regions and exploring diverse areas. The proposed method was implemented using MATLAB based on the evening rush hour dataset in Santander. According to the findings, the recommended method is more efficient than the existing techniques. It can improve the travel time and the energy metric by 5.02% and 2.48%, respectively, compared to other state-of-the-art algorithms.