نوع مقاله : مقاله پژوهشی (کاربردی)
نویسندگان
1 گروه مهندسی برق آموزشکده فنی اسلام آباد غرب ، دانشگاه فنی و حرفه ای کرمانشاه - ایران
2 دانشجوی کارشناسی، گروه مهندسی برق، دانشگاه فنی و حرفه‌ای، تهران، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Internet of Things network consists of many devices with vary characteristics and constraints. One of the major properties of IoT that it has heterogenous nodes and communication protocols. Hence, it is required routing method that decrease the energy consumption of network. In this article proposed cluster head selection method with using of improved grey wolf optimization. The goal of proposed algorithm is improving load balancing and decreasing power consumption. In this method process of cluster head selection use many metrics to improve load balancing and energy consumption. This method has more convergence than traditional grey wolf optimization. Proposed method implemented by MATLAB 2019a and run many simulations to compare with three other metaheuristic algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), improved artificial bee colony (IABC), and glowworm swarm optimization (GSO). The result of simulations shows that the proposed method improved end to end delay, convergence, energy consumption parameters. It can be concluded that load balancing on IoT network improved.
کلیدواژه‌ها [English]
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