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Wireless Rechargeable Sensor Networks (WRSN) tackles the energy replenishment problem of sensor nodes. This kind of network uses mobile wireless charger (MWC) to charge sensor nodes periodically by its demand. The main issue in WRSN is to reducing the charging time and cost in improving energy efficiency of sensor nodes. In this case, it is required to analyze MWC charging factors as Traveling Speed, Number of Sensors to Charge, Charging Power, Traveling Path, and Charging Time. Hence, in this paper we propose synthesized energy efficient approach (SynE2A) for promising network lifetime and minimum energy consumption in WRSNs. To predict the optimum traveling path and reduce the issues of MWC, we presented Chessboard based sensor network with the following operations: (1). Unequal Cluster Formation, (2). Adaptive Duty Cycling, (3). Inter-Cluster Routing and (4). Charging Path Schedule. Firstly, unequal clusters are formed in terms of 1-hop, 2-hop, 3-hop and 4-hop clusters. Compound Entropy Determination (ComED) method is presented to elect optimum cluster head (CH). Secondly, adaptive duty cycling is implemented using Neural Neutrosophic Flexible Duty Cycling (N2-FlexDC) scheme. This is a generative adversarial network (GAN) to assign optimum timeslots for sensors. Thirdly, tri-cohesive multi-path backup routing (Tr-CoMBR) protocol is used for multi-path routing. Adhoc Ondemand Multipath Routing with Dijsktra Algorithm is presented to determine multiple paths from source to the destination and then the Hybrid Markov Model evaluates the paths and finds the best path from set of possible paths. Finally, intelligent move is determined for MWC by Intelligent Q-learning algorithm. It is performed like a Queen in chessboard (move anywhere in a straight line, horizontally, vertically, or diagonally). Next to the move prediction, the charging path is predicted by Weighted Planar Graph in which charging threshold for clusters are defined to replenish energy to sensors. Simulations conducted using NS3.26 network simulator and the performance is analyzed for energy consumption, arriving time of MWC, throughput, delay, network lifetime and PDR. The performance proved that it is outperforms than the well-known methods of WRSN.
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