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An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

Author

Listed:
  • Qi Yang
  • Xuan Zhang
  • Jingfeng Qian
  • Qiang Ye

Abstract

In wireless sensor networks, time synchronization is an important issue for all nodes to have a unified time. The wireless sensor network nodes should cooperatively adjust their local time according to certain distributed synchronization algorithms to achieve global time synchronization. Conventionally, it is assumed that all nodes in the network are cooperative and well-functioned in the synchronization process. However, in cognitive radio wireless sensor networks, the global time synchronization process among secondary users is prone to fail because the communication process for exchanging synchronization reference may be frequently interrupted by the primary users. The anomaly nodes that failed to synchronize will significantly affect the global convergence performance of the synchronization algorithm. This article proposes an anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks. The proposed method adopts the statistical linear correlation analysis approach to detect anomaly nodes through the historical time synchronization information stored in local nodes. Simulation results show that the proposed method can effectively improve the robustness of the synchronization algorithm in distributed cognitive radio sensor networks.

Suggested Citation

  • Qi Yang & Xuan Zhang & Jingfeng Qian & Qiang Ye, 2018. "An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:5:p:1550147718774467
    DOI: 10.1177/1550147718774467
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    References listed on IDEAS

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    1. Hongli Dong & Zidong Wang & Steven X. Ding & Huijun Gao, 2014. "A Survey on Distributed Filtering and Fault Detection for Sensor Networks," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, January.
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