Abstract—In recent years, the Internet of Things (IoT) has made rapid progress in various applications such as battlefield management, agriculture, health care monitoring, and so on, due to its self-organizing nature and flexibility. The IoT nodes are deployed in the target area for the purpose of sensing. However, the IoT nodes are operated on tiny batteries, and it is impossible to replace or recharge the nodes in critical environments like underground coal mines. Therefore, the preservation of energy and maximization of the network lifetime of the IoT nodes became major research topics for researchers. Clustering the IoT nodes has proved to be a prominent way of preserving energy and maximizing the network’s lifetime. Since clustering IoT nodes is an NP-hard optimization problem, we clustered IoT nodes using a nature-inspired optimization technique. Therefore, in this paper, we present an energy-efficient clustering algorithm using the Frilled-Lizard optimization technique for preserving energy and improving the network lifetime of the IoT nodes. The proposed algorithm has been tested with various network scenarios, and the simulation results revealed that the proposed algorithm shows superior performance in terms of network lifetime, residual energy, and number of alive nodes compared to existing algorithms. The results demonstrate that the proposed method extends network lifetime, the number of alive nodes, and energy efficiency by 39.6% over existing algorithms, highlighting its effectiveness in enhancing overall network performance.
 
Keywords—Internet of Things (IoT), frilled-lizard optimization, cluster-head selection, meta-heuristic algorithm, network maximization


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