Lightweight Fault Detection Strategy for Wireless Sensor Networks Based on Trend Correlation

• Main Authors: Xiuwen Fu, Ye Wang, Wenfeng Li, Yongsheng Yang, Octavian Postolache
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Wireless sensor networks; fault detection; trend correlation; self-starting mechanism; fault type; exponential smoothing method
• Online Access: https://ieeexplore.ieee.org/document/9316657/

In this work, we propose a fault detection strategy for wireless sensor networks (WSNs) called Trend Correlation based Fault Detection strategy (TCFD). This strategy can detect the faulty sensor nodes through analyzing the trend correlation and the median value of neighboring nodes. On this basis, aiming to avoid the excessive routing overhead caused by over-frequent fault detection, a fault detection self-starting mechanism is designed based on the cubic exponential smoothing method. Since the detection results in TCFD are determined by historical data at consecutive times and do not rely on the comparison of instantaneous sensed values at a single moment, it can significantly reduce the impact of fault detection time on detection accuracy. The simulation results have indicated that compared with referenced strategies, the proposed TCFD can obtain better fault detection accuracy for four common fault types of sensor nodes; in the case where the real fault rate of the network reaches 0.5, at least 70% of the faulty nodes can be detected by TCFD and the false alarm rate can still be kept below 30%; with the help of fault detection self-starting mechanism, the response time of sensor nodes to faults can be significantly shortened.