Simultaneous wireless information and power transmission-based power transfer and energy prediction for efficient communication with golden Taylor sea lion optimization in wireless sensor network

摘要

Wireless Sensor Network (WSN) is an emerging lower cost and resourcefulsolution, which enables controlled observation of the environment. The highamount of energy is required in wireless networks during the transmission ofdata. Here, Golden Ant Lion Whale Optimization (GALWO) and Golden TaylorSea Lion Optimization (GTSLnO) techniques are presented for cluster head(CH) selection and prediction of neighbor nodes' age. The six stages performedin this work are setup, steady-state, prediction, power transfer, communicationor route discovery, and route maintenance stages. In the setup level, CH selectionis carried out by GALWO, which is the combination of Ant Lion WhaleOptimization (ALWO) with Golden Search Optimization (GSO). Moreover,ALWO is an integration of Ant Lion Optimizer (ALO) with the Whale OptimizationAlgorithm (WOA). In the steady state, the distance, energy, delay,throughput, and trust update are considered as objective functions. In the predictionstage, the Deep Convolutional Neural Network (Deep CNN) is utilizedfor age prediction of neighbor nodes, wherein Deep CNN is tuned byemploying GTSLnO. The GTSLnO is an incorporation of GSO and Taylorseries with Sea Lion Optimization (SLnO). Then, the power transfer stage isdone utilizing simultaneous wireless information and power transmission(SWIPT). Thereafter, the communication/route discovery stage is conductedfor path selection through neighbor node, and lastly, the route maintenancestage is carried out. The GTSLnO–Deep CNN achieved a minimal delay of0.089 s, maximal residual energy, throughput, and trust of 0.500 J,98,843 kbps, and 0.452 for DoS attack.