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In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short-term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load-adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high-capacity smartphone chargers for sensor nodes for charging. Nearly all R-WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting-based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node &amp; mobile charger. Here, the collecting &amp; processing of data are performed through the big data collection in WSNs. The R-WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

Energy-Efficient Enhancement for the Prediction-Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs