可充电概率传感网络中的充电小车休息时间最大化方法

doi:10.16180/j.cnki.issn1007-7820.2022.06.003

Abstract

Abstract:

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%.

Key words: wireless rechargeable sensor network, probabilistic monitoring model, multi-nodes charging model, mobile charger, charging schedule, the rest time of mobile charger, anchor point, path planning

CLC Number:

TP393

RAN Xianyuan,WANG Ran. Maximizing the Rest Time of Mobile Charger in Rechargeable Probabilistic Sensor Networks[J].Electronic Science and Technology, 2022, 35(6): 13-20.

Figures/Tables 9

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Table 1.

Experimental parameters"

参数	数值
MC的输出功率P_out	5 J·s^-1
MC的移动能耗P_mov	30 J·s^-1
MC的电池容量E_mc	10 000 J
传感器节点初始能量E_max	50 J
传感器节点能耗率e_s	0.2 J·s^-1
MC移动速度v	2 m·s^-1
MC最大充电半:D	2.7 m
目标的监测概率ε	0.80
充电阈值η	50%
能量传递效率函数μ(D_i)	-0.098 5× $D i 2$ -0.037 7×D_i+1

Table 1.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

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

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