Strengthening barrier-coverage of static sensor network with mobile sensor nodes

被引:22
|
作者
Xu, Biaofei [1 ]
Zhu, Yuqing [2 ]
Kim, Donghyun [3 ]
Li, Deying [1 ]
Jiang, Huaipan [4 ]
Tokuta, Alade O. [3 ]
机构
[1] Renmin Univ China, Sch Informat, Beijing, Peoples R China
[2] Calif State Univ Los Angeles, Dept Comp Sci, Los Angeles, CA 90032 USA
[3] N Carolina Cent Univ, Dept Math & Phys, Durham, NC 27707 USA
[4] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Peoples R China
基金
美国国家科学基金会;
关键词
Wireless sensor network; Barrier coverage; Mobile sensor node; Grey forecasting model GM(1,1);
D O I
10.1007/s11276-015-0946-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A wireless sensor network (WSN) provides a barrier-coverage over an area of interest if no intruder can enter the area without being detected by the WSN. Recently, barrier-coverage model has received lots of attentions. In reality, sensor nodes are subject to fail to detect objects within its sensing range due to many reasons, and thus such a barrier of sensors may have temporal loopholes. In case of the WSN for border surveillance applications, it is reasonable to assume that the intruders are smart enough to identify such loopholes of the barrier to penetrate. Once a loophole is found, the other intruders have a good chance to use it continuously until the known path turns out to be insecure due to the increased security. In this paper, we investigate the potential of mobile sensor nodes such as unmanned aerial vehicles and human patrols to fortify the barrier-coverage quality of a WSN of cheap and static sensor nodes. For this purpose, we first use a single variable first-order grey model, GM(1,1), based on the intruder detection history from the sensor nodes to determine which parts of the barrier is more vulnerable. Then, we relocate the available mobile sensor nodes to the identified vulnerable parts of the barrier in a timely manner, and prove this relocation strategy is optimal. Throughout the simulations, we evaluate the effectiveness of our algorithm.
引用
收藏
页码:1 / 10
页数:10
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