Smart Spider Monkey Optimization (SSMO) for Energy‐Based Cluster‐Head Selection Adapted for Biomedical Engineering Applications

Abstract

Using energy efficiency to increase the life and sustainability of wireless sensor networks (WSNs) for biomedical applications is still a challenge. Clustering has boosted energy productivity by allowing cluster heads to be categorized, but its implementation is still a challenge. Existing cluster head selection criteria start with determining acceptable cluster head locations. The cluster heads are picked from the nodes that are most closely connected with these places. This location-based paradigm incorporates challenges such as faster processing, less precise selection, and redundant node selection. The development of the sampling-based smart spider monkey optimization (SSMO) approach is addressed in this paper. If the sample population’s nodes are varied, network nodes are picked from among them. The problems with distributed nodes and cluster heads are no longer a concern. This article shows how to use an SSMO and smart CH selection to increase the lifetime and stability of WSNs. The goal of this study is to look at how cluster heads are chosen using standard SMO and sampling-based SMO for biomed applications. Low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SSMO improved routing protocol measurements are compared to those obtained in homogeneous and heterogeneous settings using equivalent methodologies. In these implementations, SSMO boosts network longevity and stability periods by an estimated 12.22%, 6.92%, 32.652%, and 1.22%.

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

“…Ajay et al 23 proposed a CH selection strategy using smart spider monkey optimization (SSMO) with sampling for selecting the sensor nodes depending on the energy possessed by each of them to extend network lifetime. This SSMO was proposed for handling the problems of energy holes during the selection of CHs and distributed sensor nodes of the network.…”

“…In the phase of steady state, the “intra and inter‐cluster communication” and interaction between the CH and sink occur. This phase is implemented as a reactive strategy which operates on the merits of soft and hard threshold limits utilized by the TSEP protocol 23 . In the initial step of steady state, the data transmission is allowed only when the currently sensed value of sensor nodes is greater than the hard threshold.…”

“…In the prey encircling phase, the position of the prey (solution) is determined by the whale search agent by updating its position with respect to W Best t ð Þ using Equations ( 22) and (23).…”

Hybrid Red Fox and Improved Whale Optimization Algorithm-based Clustering scheme for improving network longevity in Wireless Sensor Networks

“…The number of clusters and mean packet loss of this TSMPSO was identified to be comparatively less that the baseline clustering approaches. Ajay et al [25] proposed a Smart Spider Monkey Optimization -based Energy efficient Clustering Scheme (SSMOECS) for handling the challenges of faster processing in location-based CH selection process. It prevented redundant node selection and less precise selection problems that are inherent during the process of CH selection depending on the strategy of sampling.…”

“…In addition, 'X W ' with dimension 'D * LF' executes WOA, while 'X C ' with dimension 'D * (1 − LF)' is modified by CSO. Moreover, the update formulae of CSO with respect to horizontal and vertical crossover is depicted from Equations (25)(26)(27). : Horizontal Crossover:…”