Optimal Wireless Charging Inclusive of Intellectual Routing Based on SARSA Learning in Renewable Wireless Sensor Networks

The next generation's sensor nodes will be more intelligent, energy conservative, and perpetual lifetime in the set-up of wireless sensor networks (WSNs). These sensors nodes are facing the overwhelming challenge of energy consumption which gradually decreases the lifetime of overall network. The wireless power transfer (WPT) is one of the most emerging technologies of energy harvesting that deploys at the heart of sensor nodes for efficient lifetime solution. A wireless portable charging device (WPCD) is drifting inside the WSN to recharge all the nodes which are questing for the eternal life. In this paper, we aspire to optimize a multi-objective function for charging trail of WPCD, and self-learning algorithm for data routing jointly. We formulated that the objective functions can optimize the fair energy consumption as well as maximize the routing efficiency of WPCD. The fundamental challenge of the problem is, to integrate the novel path for WPCD by applying the Nodal A∗ algorithm. We proposed a novel method of sensor node's training for intellectual data transmission by using of clustering and reinforcement learning (SARSA) defined as clustering SARSA (C-SARSA) along with an optimal solution of objective functions. The whole mechanism outperforms in terms of trade-off between energy consumption and stability (fair energy consumption among all nodes) of the WSN; moreover, it prolongs the lifetime of the WSN. The simulated results demonstrate that our proposed method did better than compared literature in terms of energy consumption, stability, and lifetime of the WSN.