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.
In this paper, the implementation of Mobile Error Probability algorithm has been implemented to improve the output efficiency of the sensor nodes in the wireless network environment. According the WSNs is concerned, it’s very important to focus on residual energy of each node. The Mobile Error Probability algorithm support to this very strongly, it ultimately calculates the residual energy corresponding to the consumed energy by the node at each level of the beaconing. This is only applicable for the mobile node depending upon the distance between the nodes. If the distance exceeds the set limit, then only the node sends the beaconing signal else it will be in idle state. Besides, the above concept, the method of RWS is also been implemented to estimate the routing path which can be used for the transmission in minimum stipulated of time. Scheduling algorithm has been used for proper cycling of the node in various modes such as active, idle, sleep and dead conditions. All the above algorithms are been implemented in EQSR, ED as well as proposed model using NS-II simulation through results are also been examined.
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