Self-maintenance model for Wireless Sensor Networks. (August 2018)

• Record Type: Journal Article
• Authors: Elsayed, Walaa; Elhoseny, Mohamed; Sabbeh, Sahar; Riad, Alaa
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 799
• Page End: 812
• Publication Date: 2018-08
• Subjects: Wireless Sensor Network -- Cluster head -- Sensor node -- Self-detect -- Self-diagnosis -- Fault recovery; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.022
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Wireless Sensor Networks have wide variety of applications and their nodes are prone to failure due to a hardware failure or malicious attacks. The self-healing mechanism is used for fault detection, diagnosis and healing. However, implementing the self-healing procedures at the cluster head affects the network performance. In this paper, we present a distributed self-healing approach for both node and cluster head levels. At node level, battery, sensor and receiver faults can be diagnosed while, at cluster head level, transmitter and mal-functional nodes can be detected and recovered. Compared to the state-of-the art methods, our model tolerates up to 67.3% of different hardware faults at node level. Moreover, it realized a detection accuracy of sensor circuit fault tolerate up to 76.9%, 52% of battery fault and 71.96% of receiver faults. At head class level, 75.7% of transmitter fault and 60% of microcontroller circuit fault are realized.