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

Qi Yang,Jingfeng Qian,Qiang Ye,Xuan Zhang

doi:10.1177/1550147718774467

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.

Highlights

Wireless sensor network (WSN) is a special distributed wireless network that can support pervasive and robust monitoring of various physical conditions such as temperature, sound, humidity, and so on
We propose a novel method to detect the abnormal node in distributed time synchronization algorithm
We study the time synchronization algorithm under an assumption that the topology of the cognitive WSN is a totally connected graph

Summary

Introduction

Wireless sensor network (WSN) is a special distributed wireless network that can support pervasive and robust monitoring of various physical conditions such as temperature, sound, humidity, and so on. We propose a novel method to detect the abnormal node in distributed time synchronization algorithm. All the nodes except the abnormal nods in the network adjust their local time references according to the distributed synchronization algorithm.

Results

Conclusion