基于信任模型的WSNs安全数据融合算法
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摘要
为了抵御无线传感器网络内部的恶意攻击行为和故障节点的误操作行为对数据融合结果的影响,提出一种基于信任模型的多层不均匀分簇无线传感器网络安全数据融合算法.该算法基于多层不均匀的分簇网络拓扑实现安全数据融合能够有效均衡网络中节点的能耗.通过节点间的通信行为和数据相关性建立信任评估模型,并引入动态的信任整合机制和更新机制,实现簇内和簇间的信任评估,选择可信融合节点并将可信节点所收集的数据进行基于信任值加权的数据融合.仿真实验表明,该算法能够实现精确的信任评估,有效识别内部恶意攻击节点,得到的数据融合结果具有较高的精确度,实现了安全的数据融合.
To resist the influence of the malicious attacks and the malfunctions of fault nodes in wireless sensor networks( WSNs) on data aggregation,this paper proposes an algorithm of trust-based secure data aggregation for WSNs. The algorithm is based on multi-layer non-uniform clustering network topology to achieve secure data aggregation,which can effectively balance the network energy consumption. The trust evaluation model is established based on the communication behavior and data correlation among the nodes. The dynamic trust integration mechanism and update mechanism are introduced to realize the trust evaluation intra-cluster and inter-cluster. Based on the trust value,a trusted aggregation node is chosen in the cluster to complete data fusion of trusted nodes. Simulation results show that the algorithm can achieve accurate and effective trust evaluation,identify internal malicious nodes,and obtain the data aggregation results with high accuracy.
To resist the influence of the malicious attacks and the malfunctions of fault nodes in wireless sensor networks( WSNs) on data aggregation,this paper proposes an algorithm of trust-based secure data aggregation for WSNs. The algorithm is based on multi-layer non-uniform clustering network topology to achieve secure data aggregation,which can effectively balance the network energy consumption. The trust evaluation model is established based on the communication behavior and data correlation among the nodes. The dynamic trust integration mechanism and update mechanism are introduced to realize the trust evaluation intra-cluster and inter-cluster. Based on the trust value,a trusted aggregation node is chosen in the cluster to complete data fusion of trusted nodes. Simulation results show that the algorithm can achieve accurate and effective trust evaluation,identify internal malicious nodes,and obtain the data aggregation results with high accuracy.
引文
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[8] Ozdemir S. Functional reputation based reliable data aggregation and transmission for wireless sensor networks[J]. Computer Communications,2008,31(17):3941-3953.
[9] Liu C X,Liu Y,Zhang Z J. Improved reliable trust-based and energy-efficient data aggregation for wireless sensor networks[J]. International Journal of Distributed Sensor Networks,2013,9(5):1-11.
[10] Liu Y,Liu C X,Zeng Q A. Improved trust management based on the strength of ties for secure data aggregation in wireless sensor networks[J]. Telecommunication Systems,2016, 62(2):319-325.
[11] Ye Z W,Wen T,Liu Z Y,et al. A security fault-tolerant routing for multi-layer non-uniform clustered WSNs[J].EURASIP Journal on Wireless Communications&Networking,2016,2016(192):1-12.
[12]叶正旺,温涛,刘振宇,等.基于节点行为动态变化的WSNs信任模型[J].控制与决策,2017,32(4):715-720.(Ye Zheng-wang,Wen Tao,Liu Zhen-yu,et al. Trust model based on dynamic change of node behavior for WSNs[J].Control and Decision,2017,32(4):715-720.)
[13] Mi S,Han H,Chen C,et al. A secure scheme for distributed consensus estimation against data falsification in heterogeneous wireless sensor networks[J]. Sensors, 2016,16(2):1-17.
[14] Li X Y,Gui X L. Cognitive model of dynamic trust forecasting[J]. Journal of Software,2010,21(1):163-176.
[15] Fullér R,Majlender P. An analytic approach for obtaining maximal entropy OWA operator weights[J]. Fuzzy Sets and Systems, 2001,124(1):53-57.
[2] Akkaya K,Demirbas M,Aygun R S. The impact of data aggregation on the performance of wireless sensor networks[J]. Wireless Communications&Mobile Computing,2008,8(2):171-193.
[3] Rajagopalan R,Varshney P K. Data-aggregation techniques in sensor networks:a survey[J]. IEEE Communications Surveys&Tutorials,2006,8(4):48-63.
[4] Ozdemir S,Xiao Y. Secure data aggregation in wireless sensor networks:a comprehensive overview[J]. Computer Networks,2009, 53(12):2022-2037.
[5] Momani M,Challa S. Survey of trust models in different network domains[J]. International Journal of Ad Hoc,Sensor and Ubiquitous Computing, 2010,1(3):1-19.
[6]张仕斌,方杰,宋家麒.一种面向WSNs的可信数据融合算法研究[J].小型微型计算机系统,2014,35(10):2347-2352.(Zhang Shi-bin,Fang Jie,Song Jia-qi. Study on an algorithm of trusted data fusion oriented on WSNs[J]. Journal of Chinese Computer Systems, 2014, 35(10):2347-2352.)
[7]杨黎斌,慕德俊,蔡晓妍.无线传感器网络入侵检测研究[J].计算机应用研究,2008, 25(11):3204-3208.(Yang Li-bin,Mu De-jun,Cai Xiao-yan. Study on intrusion detection for wireless sensor network[J]. Application Research of Computers,2008,25(11):3204-3208.)
[8] Ozdemir S. Functional reputation based reliable data aggregation and transmission for wireless sensor networks[J]. Computer Communications,2008,31(17):3941-3953.
[9] Liu C X,Liu Y,Zhang Z J. Improved reliable trust-based and energy-efficient data aggregation for wireless sensor networks[J]. International Journal of Distributed Sensor Networks,2013,9(5):1-11.
[10] Liu Y,Liu C X,Zeng Q A. Improved trust management based on the strength of ties for secure data aggregation in wireless sensor networks[J]. Telecommunication Systems,2016, 62(2):319-325.
[11] Ye Z W,Wen T,Liu Z Y,et al. A security fault-tolerant routing for multi-layer non-uniform clustered WSNs[J].EURASIP Journal on Wireless Communications&Networking,2016,2016(192):1-12.
[12]叶正旺,温涛,刘振宇,等.基于节点行为动态变化的WSNs信任模型[J].控制与决策,2017,32(4):715-720.(Ye Zheng-wang,Wen Tao,Liu Zhen-yu,et al. Trust model based on dynamic change of node behavior for WSNs[J].Control and Decision,2017,32(4):715-720.)
[13] Mi S,Han H,Chen C,et al. A secure scheme for distributed consensus estimation against data falsification in heterogeneous wireless sensor networks[J]. Sensors, 2016,16(2):1-17.
[14] Li X Y,Gui X L. Cognitive model of dynamic trust forecasting[J]. Journal of Software,2010,21(1):163-176.
[15] Fullér R,Majlender P. An analytic approach for obtaining maximal entropy OWA operator weights[J]. Fuzzy Sets and Systems, 2001,124(1):53-57.
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