Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (8): 2348-2352.

### Fault diagnosis algorithm of WSN based on precondition of neighbor nodes

MA Mengying1,2, ZENG Yali1,2, WEI Tiantian1,2,3, CHEN Zhide1,2

1. 1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou Fujian 350007, China;
2. Fujian Provincial Key Laboratory of Network Security and Cryptology(Fujian Normal University), Fuzhou Fujian 350007, China;
3. Key Laboratory of Network Assessment Technology of CAS(Institute of Information Engineering), Chinese Academy of Sciences, Beijing 100093, China
• Received:2018-01-15 Revised:2018-03-10 Online:2018-08-10 Published:2018-08-11
• Supported by:
This work is partially supported by the Natural Science Foundation of Fujian Province (2016J0101).

### 基于邻居节点预状态的无线传感器网络故障诊断算法

1. 1. 福建师范大学 数学与信息学院, 福州 350007;
2. 福建省网络安全与密码技术重点实验室(福建师范大学), 福州 350007;
3. 中国科学院信息工程研究所 中国科学院网络测评技术重点实验室, 北京 100093
• 通讯作者: 陈志德
• 作者简介:马梦莹(1994-),女,河南息县人,硕士研究生,主要研究方向:网络与信息安全;曾雅丽(1990-),女,福建漳州人,博士研究生,主要研究方向:网络与信息安全;魏甜甜(1992-),女,河南平顶山人,硕士研究生,主要研究方向:网络与信息安全;陈志德(1976-),男,福建泉州人,教授,博士,主要研究方向:网络安全与密码学、分布式计算。
• 基金资助:
福建省自然科学基金资助项目（2016J0101）。

Abstract: To address the problem of low detection accuracy when the fault node rate was higher than 50% in Wireless Sensor Network (WSN), a wireless sensor fault diagnosis algorithm based on the precondition of neighbor nodes and neighbor node data was proposed. Firstly, the historical data of nodes were used to pre-calculate the states of sensor nodes initially. Then the final state of each node was judged by taking advantage of similarity of nodes and pre-states of neighbor nodes. Finally, the fault node information was sent to the base station by mobile sensors through the optimal path, which effectively reduced the number of communications. A WSN was simulated in an area of 100 m*100 m. The experimental results show that compared with the traditional Distributed Fault Detection (DFD) algorithm, the diagnosis accuracy of the proposed algorithm is improved by 9.84 percentage points. Moreover, the proposed algorithm even achieves more than 95% fault diagnosis accuracy when the node failure rate is as high as 50% in the network. In practical application, the proposed algorithm improves the fault diagnosis accuracy, reduces the energy consumption effectively, and prolongs the network lifetime as well.

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