Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

Abstract

Sensor networks have helped wireless communication systems. Over the last decade, researchers have focused on energy efficiency in wireless sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors have limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using the witness-based data fusion technique, the evaluated network lifetime, energy consumption, communication overhead, end-to-end delay, and data delivery ratio. Energyefficient data fusion optimizes energy utilization using squirrel search optimization and a recurrent neural network. The method allows the system to recognize a sensor with excessive energy dissipation and relocate data fusion to a more energy-efficient node. The proposed model was compared against artificial neural network-particle swarm optimization (ANN-PSO), cuckoo optimization algorithm-back propagation neural network (COABPNN), Elman neural network-whale optimization algorithm (ENN-WOA), and extreme learning machine-particle swarm optimization (ELM-PSO). The model achieved 94.50% network lifetime, 26.63% communication overhead, 93.85% data delivery ratio, 10.50 ms end-to-end delay, and 282 J energy usage.

Load frequency control of interconnected power system using cuckoo search algorithm

“…Evolutionary algorithm (EA) techniques offer an effective approach to address the LFC problem by efficiently handling nonlinear objective functions. Among this techniques, cuckoo search algorithm (CSA) [6]- [11], fractional order proportional integral derivative (PID) controller based on gases brownian motion optimization (GBMO) [12], [13], hybrid grey wolf optimization and CSA [14], [15], novel hybrid local unimodal sampling (LUS) and teaching learning based optimization (TLBO) based fuzzy-PID controller [16], CSA and particle swarm optimization (PSO) [17], artificial bee colony (ABC) algorithm [18], PID controller coordinated with redox flow batteries (RFBs) [19], hybrid bacteria foraging optimization algorithm and particle swarm optimization [20], observer-based sliding mode control [21], grey wolf optimizer algorithm [22], firefly algorithm [23], quasi-oppositional grey wolf optimization algorithm [24], squirrel search optimization and recurrent neural network [25], fuzzy-based PID droop controller [26], archimedes optimization algorithm [27], CSAbased for tunning both PI and fractional order proportional integral derivative (FOPID) controllers [28], modified fletcher-reeves method [29], PSO and CSA [30], artificial CSA [31], cuckoo search (CS) and neural network [32], have gained popularity in the design of LFC controllers.…”

Energy aware reliable routing model for sensor network enabled internet of things environment

“…In IoT-enabled WSNs, sensor nodes collect data about the surrounding environment and relay that information to the IoT edge gateway server or base station (BS) based on predetermined events. Since the devices that collect data are so small and have relatively little energy and processing capabilities, the data has to initially travel utilizing a series of intermediary nodes before reaching the IoT edge gateway server [3], [4]. This causes several problems, including energy inefficiency, delays, and additional communication costs [5], [6].…”