可充电概率传感网络中的充电小车休息时间最大化方法

在保证无线可充电传感网中目标点持久覆盖的情况下,基于概率监测模型和多节点充电模型,对最大化传感网络中充电小车休息时间比例问题进行研究。文中通过放宽节点不可死亡的限制,提出基于单位子簇的充电选择算法和基于全局的重聚类启发式算法。将目标点周围冗余的传感器构建成目标簇并根据贪心思想划分子簇。充电小车以子簇为单位服务覆盖过程中的充电请求,通过调整子簇内距离要求并对全局请求节点重聚类来选择锚点进行充电,从而降低子簇的混合增益,减少锚点数量。仿真实验表明,与单节点充电模型相比,新算法可将充电小车的休息时间提升10%~15%。

Maximizing the Rest Time of Mobile Charger in Rechargeable Probabilistic Sensor Networks

Under the condition of ensuring the permanent coverage of the target point in the wireless rechargeable sensor network, based on the probabilistic monitoring model and the multi-node charging model, the problem of maximizing the rest time ratio of the mobile charger in the rechargeable sensor network is studied. By relaxing the restriction on the immortality of sensors, the charging selection algorithm based on unit sub-cluster and entire-based re-clustering heuristic algorithm are proposed. The redundant sensors around the target point are constructed into target clusters and sub-clusters are divided according to the greedy idea. By adjusting the distance requirements within the sub-clusters and re-clustering the global request nodes, the anchor points are selected for charging, thereby reducing the hybrid gain of the sub-clusters and reducing the number of anchor points. Simulation experiments show that compared with the single-node charging model, the new algorithm improves the rest time of the mobile charger by 10%~15%.