Abstract
The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes.
Highlights
Wireless sensor networks (WSNs) are being widely used in environmental monitoring [1], forest fire warning [2], and medical care [3], with the vigorous development of wireless communication technology, sensor technology, and microelectronic technology
Weighted average method [22], radical basis function (RBF) neural network [23,24], and the evolutionary neural network [25,26] are the available methods for the prediction of dynamic energy consumption rate
In an actual WSN, the energy consumption of some nodes acting as the main cluster head changes more, which might lead to large deviation between the predicted as the main cluster head changes more, which might lead to large deviation between the predicted value and the actual value while using this method
Summary
Wireless sensor networks (WSNs) are being widely used in environmental monitoring [1], forest fire warning [2], and medical care [3], with the vigorous development of wireless communication technology, sensor technology, and microelectronic technology. The problem of node energy holes is obvious when many targets must be monitored in the network and the number of nodes requesting charging increases These studies rarely considered estimating the dynamic energy consumption rate to determine the optimal charging request threshold and the charging node. An online charging scheme based on a radical basis function (RBF) for the energy hole avoidance of WRSN is proposed here to reduce the energy hole rate [16] and the waiting time of charging-needed nodes. We used the energy consumption data predicted by RBF to theoretically analyze and estimate the threshold value of charging request to reduce the waiting time of the charging-needed nodes and considerably improve the fairness of node charging response starting from the three constraints of node energy consumption, network residual energy limit, and MC average service time.
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