Abstract
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 & mobile charger. Here, the collecting & 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.
Highlights
wireless sensor network (WSN) is a well-organized environment consisting of a large number of microactive nodes spread dynamically across the monitoring area through wireless broadcasting
This paper provides a smooth logic-based approach that is suggested for on-demand charging in a dense R-WSN [20]
This paper suggests a primary study to have an overview of the usage of an energy-restricted mobile charger (MC) to charge nonrandom mobile sensors [12]
Summary
WSN is a well-organized environment consisting of a large number of microactive nodes spread dynamically across the monitoring area through wireless broadcasting. It suggests a charging scheme called predictingbased scheduling algorithm (PSA) based on the above study, which includes three algorithms to solve the current research challenges. (1) The issues of energizing nodes with nonrandom mobility have been resolved and are coordinated (2) It first offers a new LSTM methodology for evaluating the precise positions of sensor nodes, which uses the prior trajectory to forecast the forthcoming positions of each sensor node (3) In order to maximize the network’s efficiency, it offers a node identification method for selecting the optimal node as the destination node based on the forecasting findings and the energy level of each node. (4) in order to fulfil the demands of wireless charging, it implements a Kalman filter-based tracking technique that directs the MC to monitor mobile target nodes during energy conversion
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