Proposing a New Framework for Optimizing Energy Consumption in Sensor Nodes Used in the Internet of Things

This paper presents a comparative analysis of evolutionary algorithms, including genetic algorithms, particle swarm optimization, cuckoo algorithm, and the Harris Hawk Optimization algorithm, for optimizing vehicle routing in smart cities. The study evaluates the performance of these algorithms in minimizing costs and maximizing efficiency in the context of providing services to requesters. Results indicate the effectiveness of the Harris Hawk Optimization algorithm compared to other approaches, suggesting its suitability for real-world applications in smart city environments. Future directions for research in this area are also discussed.Keywords: Evolutionary algorithms, Vehicle routing, Smart cities, Optimization, Comparative analysis1.IntroductionSmart transportation has emerged as an indispensable necessity and solution in today's traffic-congested cities. Urbanization presents significant challenges, including traffic congestion, pollution, and inefficiencies in transportation systems. In response to these challenges, smart cities have been developed, leveraging Information and Communication Technology (ICT) to promote sustainable development approaches[1].Afundamental aspect of smart cities is the implementation of intelligent transportation systems, which optimize vehicle routing to minimize congestion, reduce travel time, and enhance overall efficiency. Evolutionary algorithms have gained prominence as effective tools for addressing complex optimization problems in smart transportation systems[2].This research focuses on improving vehicle routing in smart cities using evolutionary algorithms, with a particular emphasis on the Hybrid Harris's Hawks Optimization (HHO) algorithm[3]. The HHO algorithm is a gradient-independent optimization technique inspired by the cooperative behavior and agile pursuit of Harris's Hawks in nature, known as the "surprise pounce."