Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine. (15th August 2020)

• Record Type: Journal Article
• Authors: Mohapatra, Santoshinee; Khilar, Pabitra Mohan
• Is Part Of: Computational intelligence. Volume 36:Number 3(2020)
• Page Start: 1374
• Page End: 1393
• Publication Date: 2020-08-15
• Subjects: artificial immune system -- fault diagnosis -- negative selection algorithm -- support vector machine -- wireless sensor networks; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12380
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.