ISSN 1000-1239 CN 11-1777/TP

• 网络技术 •

### 无线传感器网络节点位置验证框架

1. 1（杭州安恒信息技术股份有限公司 杭州 310051）；2（浙江师范大学网络应用安全研究中心 浙江金华 321004) (Crain.miao@dbappsecurity.com.cn)
• 出版日期: 2019-06-01
• 基金资助:
国家自然科学基金项目(61502431，61379023)；浙江省计算机科学与技术重中之重学科(浙江师范大学)基金项目(ZC323014074)；浙江省科技厅公益性技术应用研究计划基金项目(2015C33060)

### Node Location Verification Framework for WSN

Miao Chunyu1, Chen Lina2, Wu Jianjun2, Zhou Jiaqing2， Feng Xuhang1

1. 1（Hangzhou Anheng Information Technology Co. LTD, Hangzhou 310051）；2（Research Center of Network Application Security, Zhejiang Normal University, Jinhua, Zhejiang 321004)
• Online: 2019-06-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61502431, 61379023), the Opening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University (ZC323014074), and the Zhejiang Provincial Science Technology Department Public Welfare Technology Application Research Project (2015C33060).

Abstract: Localization is one of the pivot technologies in wireless sensor networks. The traditional node localization schemes consider that the locations of anchors are reliable, which makes these schemes are invalid in some scenarios with unreliable anchors such as drifted anchors, fake anchors and malicious anchors. Aiming at solving this problem mentioned above, a distributed and lightweight node location verification framework (NLVF) is proposed. NLVF offers location verification service as an underlying technic for the traditional localization algorithms, including range-based localization algorithm and the range-free localization algorithm. NLVF can filter out these unreliable anchors by which the application area of traditional localization algorithms is enlarged. UNDA (unreliable node detection algorithm) is the key algorithm of NLVF. It constructs location reputation model based on mutual distance observation between neighbors in WSN. UNDA algorithm improves the localization reliability by filtering out these anchors with inferior location reputations. Extensive experiments are conducted to evaluate the performance of UNDA. Results show that NLVF is adapted to both of range-based and range-free localization schemes. It works better in the presence of three kinds of unreliable anchors. So, it yields general applicability. In addition, UNDA relatively has high accuracy, and the average success rate of detection is more than 95%, so NLVF yields significant practicability.