Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

中文翻译：

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一种物联网网络能源效率的元启发式优化方法

最近，物联网 (IoT) 被用于智慧城市、农业、天气预报、智能电网、废物管理等多个领域。尽管物联网在多个应用中具有巨大潜力，但仍有一些需要改进的领域。在当前的工作中，我们专注于最大限度地减少物联网网络中传感器的能耗，这将导致网络寿命的增加。在这项工作中，为了优化能源消耗，在物联网网络中选择了最合适的簇头（CH）。所提出的工作利用了混合元启发式算法，即带有模拟退火 (SA) 的鲸鱼优化算法 (WOA)。为了选择物联网网络集群中的最佳 CH，使用了几个性能指标，例如活动节点的数量、负载、温度、剩余能量、成本函数。然后将所提出的方法与几种最先进的优化算法进行比较，如人工蜂群算法、遗传算法、自适应重力搜索算法、WOA。结果证明了所提出的混合方法优于现有方法。