作者：宋三华

作者单位：黄淮学院信息工程学院, 河南 驻马店 463000

关键词：无线传感网络;二值事件;融合中心;恶意节点;检测率

摘要：

在无线传感网络中二值事件的分布式检测系统中，局部的传感节点统计过程容易遭受恶意节点的安全袭击，而检测二值事件的准确性依赖于传感节点的可靠性。为此，提出基于可靠度量的恶意节点的检测算法RDICS识别恶意节点，并减少这些恶意节点的融合权值，进而削弱它们对最终决策的贡献。在设置融合权值时，先计算基于所有传感节点的融合中心决策与单个传感节点决策的不一致性，然后再计算包含除此传感节点的决策外的所有传感节点的融合决策与此传感节点决策的不一致性。再依据这两个参数，估计传感节点的可靠性，进而设置融合权值。最后，实验数据表明，RDICS算法的检测率优于现存的RBDA算法。

Reliability metric-based detection algorithm of compromised sensor nodes in wireless sensor networks

SONG Sanhua

College of Information Engineering, HuangHuai University, Zhumadian 463000, China

Abstract: In the distributed detection of a binary event in wireless sensor networks (WSNs), the statistical process of local sensor nodes is vulnerable to security attacks of compromised sensor nodes. The detection precision of a binary event strongly depends on the reliability of these sensor nodes in the network. Therefore, the reliability metric-based detection algorithm to identify compromised sensor nodes (RDICS) is proposed to identify the compromised sensor nodes and decreases their fusion weights so as to reduce their contributions towards the final decision. In computing the fusion weights, firstly the inconsistency between the fusion center's decision where all the sensor nodes contributions are considered and the single node decision should be calculated, and then the inconsistency between the fusion center's decision where the single node contributions is excluded and the single node decision should be calculated. Finally, according to these two parameters, the reliability of the sensor nodes is estimated, and the fusion weights are set. Experiment results show that the proposed RDICS algorithm outperforms the existing RBDA algorithm.

Keywords: wireless sensor networks; binary event; fusion center; compromised sensor nodes; detection ratio

2018, 44(7): 148-152  收稿日期: 2017-09-19;收到修改稿日期: 2017-11-10

基金项目: 河南省科技厅基金项目（162102110119）

作者简介: 宋三华(1981-),男,河南正阳县人,讲师,硕士,研究方向为网络及Android嵌入式系统应用。

参考文献

[1] DAI W, SHEN Y, WIN M Z.Distributed power allocation for cooperative wireless network localization[J]. IEEE J. Sel. Areas Commun., 2015, 33(1):28-40.
[2] 蔡绍滨, 韩启龙, 高振国, 等. 基于云模型的无线传感器网络恶意节点识别技术的研究[J]. 电子学报, 2012, 40(11):2232-238.
[3] 陈东海, 李长庚. 基于簇头功能分化的无线传感器网络成簇算法[J]. 传感技术学报, 2015, 28(2):244-248.
[4] LI H J, LI K Q, QU W Y. Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks[J]. Future Genration Computer Systems, 2014, 37(7):108-116.
[5] ESTRIN D, GIROD L, POTTIE G. Instrumenting the world with wireless sensor networks[C]//In Pro. IEEE Int. Conf. Aoust., 2015.
[6] SONG O W, PHOEBUS C, MICHAEL M. Instrumenting wireless sensor networks for real time surveillance[C]//In Pro. of the International Conference on Robotics and Automation, 2015.
[7] ZHANG L, DING G, WU Q. Byzantine attack and defense in cognitive radio networks:a survey[J]. IEEE Communications Surveys and Tutorials, 2015, 17(3):1342-1363.
[8] VEMPATY A, TONG L, VARSHNEY P. Distributed inference with byzantine data:state-of -the art review on data falsification attacks[J].IEEE Signal Processing Magazine, 2013, 30(5):65-75.
[9] GENTZ R, WU S X, WAI H T. Data injection attacks in randomized gossiping[C]//In IEEE Transactions on Signal and Information Processing over Networks, 2016.
[10] RAWAT A S, ANAND P, CHEN H. Collaborative spectrum sensing in the presence of byzantine attacks in cognitive radio networks[J]. IEEE Trans. Signal Process, 2014, 59(2):774-786.
[11] CHEN H, JIN X, XIE L. Reputation-based collaborative spectrum sensing algorithm in cognitive radio networks[C]//Pro. PIRMC, 2013.
[12] URKOWITZ H. Energy detection of unknown deterministic signals[C]//Proc. IEEE, 2016.
[13] NURELLARI E, LERNON D M, GHOGHO M. Distributed binary event detection under data-falsification and energy-bandwidth limitation[J]. IEEE Sensors Journal, 2016(16):6298-6309.