The development of wireless charging technology has made research in rechargeable wireless sensing networks (WRSNs) a hot topic. Charge scheduling, which involves allocating mobile charger(s) to replenish energy to the sensors of a network so that the network can remain active for a longer time, is one of the most essential issues in this field. Most previous works addressed the charge-scheduling problem by using proactive or on-demand methods, each with its own advantages and issues. In this work, we propose a novel bottleneck prediction and removal mechanism (BPR) that seamlessly integrates the two completely different scheduling strategies. The BPR predicts proactively the potential charging needs that may overwhelm the on-demanding scheduling and eliminate those incidents beforehand. This allows the on-demand scheduling, which usually relies on time and/or distance factors in determining charging order, to concentrate on tackling the problem considering only the distance factor. A simple distance-based near job first (NJF) algorithm is thus sufficient for charge scheduling when time-related factors are considered prior to the determination of the charging order. Use of NJF algorithm also allow us derive a shorter path length in charge scheduling, which results in a tighter charging threshold. A lower charging threshold corresponds to fewer charging requests and less pressure on charge scheduling. In general, the new scheduling strategy relies on the on-demand feature to gather the charging demands of the network, while proactively includes additional sensor nodes that could potentially cause subsequent issues in the scheduling plan. The on-demand and proactive natures of the proposed strategy ensure that it can adapt to the dynamic charging needs of sensors, while at the same time taking precautionary measures to avoid possible burst requests in the future. Extensive simulations show that the proposed strategy can achieve a better network lifetime than other state-of-art scheduling methods.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)