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Survey comparison of Monkey-Based Optimization techniques in LEACH protocol WSNs

Wireless sensor network (WSN) deployments have increased at an exponential rate in recent years, and this trend is expected to continue. Wireless sensor networks have been focusing on enhancing their performance in terms of energy efficiency, communication efficacy, and system throughput due to their rapid development. Numerous unique processes have been developed by adapting natural organisms’ social behaviors, such as bats, birds, ants, fish, and honeybees. These systems, known as nature-inspired systems or swarm intelligence, are capable of developing optimization techniques, managing large-scale networks, and circumventing resource limits. By structuring the social foraging behavior of spider monkeys, spider monkey optimization (SMO) is a recent addition to the family of swarm intelligence algorithms. Additionally, a bio-inspired clustering technique based on the social behavior of Rhesus Macaque (RM) monkeys has been discussed with the goal of extending network longevity. The purpose of this article is to investigate the mechanism of SMO and RM in the context of WSNs by developing a mathematical model of the behavior patterns to which the cluster-based SMO and RM approach is applied. Additionally, our suggested methods, which are based on the behavioral structure of the monkeys, intend to improve the energy efficiency and network quality of Low Energy Adaptive Clustering Hierarchy (LEACH) protocols.