Leader election of dynamic wireless intelligent control machine in sensor network distributed processing

Abstract

Strategies to streamline energy resources in wireless sensor networks (WSN) are still a hot topic. Many researchers have proposed various models of the WSN protocol so that the base station (BS)/sink can receive data collected from each sensor node. The energy in the WSN, which has a long life span, will convey a lot of data to the sink until the sensor nodes die. This paper has proposed modeling the WSN protocol by utilizing the classification and clustering of sensor nodes using the chimpanzee leader election optimization (CLEO) method. The CLEO algorithm is a meta-heuristic algorithm inspired by a social model that applies the pattern of leader selection to the chimpanzee community. The optimization method has the advantage of getting a fast fitness value which is one of the critical factors in utilizing meta-heuristic models in the WSN field. The closest sensor nodes at a certain distance to the sink are marked as classification, while other sensor nodes are marked as clusters. The clustering of sensor nodes utilizes a cluster function using the CLEO algorithm. This paper also carried out the selection of group leaders to obtain an energy efficiency model from WSN-CLEO. The results of the proposed method, WSN-CLEO, have been compared to other protocol models.