WVSN入侵检测全视角弱栅栏β-QoM增强构建算法

doi:10.3778/j.issn.1673-9418.2208006

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (12): 2765-2774.DOI: 10.3778/j.issn.1673-9418.2208006

WVSN入侵检测全视角弱栅栏β-QoM增强构建算法

郭新明¹^,⁺(), 蔡军伟²

1.咸阳师范学院计算机学院，陕西咸阳 712000
2.宁波工程学院理学院，浙江宁波 315211

收稿日期:2022-07-19 修回日期:2022-09-21 出版日期:2022-12-01 发布日期:2022-12-16
通讯作者: +E-mail: guoxinming118@126.com
作者简介:郭新明（1969—），男，陕西蓝田人，硕士，副教授，硕士生导师，CCF专业会员，主要研究方向为物联网技术、网络空间安全。
蔡军伟（1969—），男，浙江宁波人，硕士，副教授，主要研究方向为数据处理、最优化。
基金资助:
国家自然科学基金面上项目(61973249);陕西省重点研发计划项目(2020NY-175);陕西省教育厅服务地方项目(19JC041);咸阳师范学院“学术带头人”资助项目(XSYXSDT202124)

WVSN Intrusion Detection Full-View Weak Barrier β-QoM Enhanced Construction Algorithm

GUO Xinming¹^,⁺(), CAI Junwei²

1. School of Computer, Xianyang Normal University, Xianyang, Shaanxi 712000, China
2. School of Science, Ningbo University of Technology, Ningbo, Zhejiang 315211, China

Received:2022-07-19 Revised:2022-09-21 Online:2022-12-01 Published:2022-12-16
About author:GUO Xinming, born in 1979, M.S., associate professor, M.S. supervisor, professional member of CCF. His research interests include Internet of things technology and cyberspace security.
CAI Junwei, born in 1969, M.S., associate professor. His research interests include data processing and optimization.
Supported by:
National Natural Science Foundation of China(61973249);Key Research and Development Program of Shaanxi Province(2020NY-175);Education Department of Shaanxi Province Serves Local Projects(19JC041);“Academic Leader” of Xianyang Normal University(XSYXSDT202124)

摘要/Abstract

摘要：

针对无线视觉传感器网络（WVSN）捕获直线轨迹入侵者图像精准度不足的问题，提出了一种入侵检测全视角弱栅栏β-QoM增强构建算法CPFWBβEC。将随机均匀部署的WVSN最优全视角弱栅栏β-QoM增强构建转化成集合覆盖问题，并从理论上证明其为NP-hard的。在此基础上，启发式算法CPFWBβEC被提出。算法CPFWBβEC主要基于传感器覆盖面积优先的贪心思想，从而在WVSN网络上实现了入侵检测全视角弱栅栏的β-QoM增强构建。仿真实验结果表明，该算法栅栏构建平均成功率分别比算法W-GraProj和D-eTriB提高了约0.116和0.340，且生成栅栏的平均节点数分别比算法W-GraProj和D-eTriB减少了约35.5%和56.1%。另外，随着β值的增大，全视角弱栅栏的构建节点数也随之增加。同时，算法CPFWBβEC时间复杂度为O(n_cgn)，因此适用于节点部署密集且实时性较高的环境中。

关键词: 无线视觉传感器网络（WVSN）, 全视角, 弱栅栏, β图像宽度（β-QoM）

Abstract:

Aiming at the problem of insufficient accuracy of the intruder image captured by wireless visual sensor network (WVSN), the intruder moving along a straight trajectory, a full-view weak barrier β-QoM enhancement algorithm CPFWBβEC for intrusion detection is proposed in this paper. The optimal full-view weak barrier β-QoM enhancement construction in WVSN with nodes randomly and uniformly deployed is transformed into a set cover problem, and it is theoretically proven to be a NP-hard problem. Consequently, a heuristic algorithm CPFWBβEC is proposed. CPFWBβEC is mainly based on the greedy idea of sensor coverage area priority, so as to realize the β-QoM enhanced construction of intrusion detection full-view weak barrier in WVSN. The simulation results show that the average success rate of the barrier construction of the proposed algorithm is about 0.116 and 0.340 higher than that of W-GraProj and D-eTriB respectively. The average number of nodes to generate the barrier is reduced approximately by 35.5% and 56.1% compared with W-GraProj and D-eTriB respectively. In addition, with the increase of the value of β, the number of construction nodes of the weak barrier at full-view also rises up. At the same time, the time complexity of the algorithm CPFWBβEC is O(n_cgn), which means it is suitable for environments with dense node deployment and high real-time requirement.

Key words: wireless visual sensor networks (WVSN), full-view, weak barriers, β quality of monitoring (β-QoM)

中图分类号:

TP393

郭新明, 蔡军伟. WVSN入侵检测全视角弱栅栏β-QoM增强构建算法[J]. 计算机科学与探索, 2022, 16(12): 2765-2774.

GUO Xinming, CAI Junwei. WVSN Intrusion Detection Full-View Weak Barrier β-QoM Enhanced Construction Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(12): 2765-2774.

图/表 16

图1 WVSN中直线入侵行为检测示意图

Fig.1 Linear intrusion behavior detection in WVSN

图2 无线视觉传感器感知模型

Fig.2 Sensing model of wireless visual sensor

图3 视觉传感器全视角覆盖示意图

Fig.3 Full-view coverage diagram of visual sensor

图4 监测目标朝向取值范围示意图

Fig.4 Value range of monitoring target orientation

图5 视觉传感器β-QoM有效捕获区

Fig.5 β-QoM effective capture area of visual sensor

图6 穿越路径上的全视角弱栅栏β-QoM增强覆盖

Fig.6 Full-view weak barrier β-QoM enhanced coverage on crossing path

图7 WVSN全视角弱栅栏β-QoM增强覆盖示意图

Fig.7 Full-view weak barrier β-QoM enhanced coverage of WVSN

图8 WVSN覆盖区域网格划分示意图

Fig.8 Grid division of WVSN coverage area

图9 视觉传感器覆盖网格集

Fig.9 Coverage grid set of visual sensor

图10 视觉传感器的有效覆盖网格

Fig.10 Effective coverage grids of visual sensor

表1 仿真实验平台的参数设置

Table 1 Parameter setting of simulation platform

参数名称	值
带状监测区域（L×W）/m²	140×60
r/m	15~20
γ	π/3
θ	π/2
部署节点数	100~1 500

图11 三种算法栅栏构建成功率的比较

Fig.11 Comparison of barrier construction success rate for three algorithms

图12 三种算法的栅栏构建平均节点数比较

Fig.12 Comparison of average node number of barrier construction for three algorithms

表2 算法性能对比

Table 2 Comparison of algorithm performance

算法名称	栅栏类型	β-QoM	首次成功构建栅栏时的平均部署节点数	100%构建栅栏的临界点平均部署节点数
CPFWBβEC	全视角、弱	有	145	380
W-GraProj	全视角、弱	无	240	550
D-eTriB	全视角、强	有	460	890

图13 不同β值下CPFWBβEC算法弱栅栏构建节点数

Fig.13 Node number of weak barrier constructed by CPFWBβEC with different β

图14 不同β值下CPFWBβEC算法弱栅栏构建成功率

Fig.14 Success rate of weak barrier constructed by CPFWBβEC with different β

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WVSN入侵检测全视角弱栅栏β-QoM增强构建算法

WVSN Intrusion Detection Full-View Weak Barrier β-QoM Enhanced Construction Algorithm

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