An improved Aquila optimization with fuzzy model based energy efficient cluster routing protocol for wireless sensor networks

Abstract

Due to the latest advances in microelectronics, wireless sensor networking (WSN) has been introduced in many applications. The flow of event data in WSN applications requires timely and reliable distribution so that immediate response and appropriate action can be taken. However, the limited power supply to the sensor terminal causes a transmission between the delay on the way to the base station and the power consumption. Clustering techniques are essential in developing the WSN routing algorithm that improves network operating time and power efficiency. However, due to the unbalanced power consumption between the terminals, the WSN is the optimal, energy efficient routing. In this work, we propose an energy efficient cluster based routing protocol (EEC-HO) for WSN using hybrid optimization algorithm. We introduce an improved Aquila optimization with fuzzy (IAO-Fuzzy) model for optimal and efficient cluster formation and cluster head (CH) computation. The main objective of proposed IAO-Fuzzy model used to compute the trust degree of each node, the highest trust node is considered as CH. After that, the hybrid beetle search induced decision making (BSDM) algorithm for optimal path selection to transfer data transfer between two nodes. Finally, the simulation results of proposed EEC-HO routing protocol is compared with the existing routing protocols. For the impact of sensor node density case, we observed that the effectiveness of proposed EEC-HO routing protocol is 45.887%, 30.666%, 56.666%, 17.629% and 41.666% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and dead nodes respectively. For the impact of simulation rounds case, we observed that the effectiveness of proposed EEC-HO routing protocol is 21.216%, 35.417%, 41.667%, 18.568% and 40.000% efficient than existing protocols in terms of energy consumption, network lifetime, average hop count, throughput and number of dead nodes respectively.

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

“…The improved versions of AO can handle a large range of difficult real-world optimization problems better than the standard AO. The strategies used in AO are hybridization with NIOAs [22,23], oppositional-based learning [24], chaotic sequence [25], Levy flight-based strategy [26], Gauss map and crisscross operator [27], Niche Thought with Dispersed Chaotic Swarm [28], random learning mechanism and Nelder-Mead Simplex Search [29], wavelet mutation [30], Weighted Adaptive Searching Technique [31], Binay AO [32], and multi-objective AO [33].…”

A Comprehensive Survey on Aquila Optimizer

“…The study findings demonstrated that the presented techniques perform better than SA, GA, ACO, and GA-PSO. A cluster-based energy-efficient routing protocol for WSNs was proposed by Ramamoorthy et al [ 74 ], using an optimization technique. A modified AO with fuzzy based on the levy flight strategy was developed for optimum and effective cluster formation and cluster head computation.…”