Fuzzy logic–based jamming detection algorithm for cluster‐based wireless sensor network

• Published in: International journal of communication systems 2018-07, Vol.31 (10), p.n/a
• Main Authors: Vijayakumar, K. P., Ganeshkumar, P., Anandaraj, M., Selvaraj, K., Sivakumar, P.
• Format: Article
• Language: English
• Subjects: Algorithms; cluster; Clusters; Computer simulation; fuzzy; Fuzzy logic; Jamming; PDR; Remote sensors; RSSI; Signal strength; Wireless communications; Wireless sensor networks
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.3567

Summary In this paper, a fuzzy logic–based jamming detection algorithm (FLJDA) is proposed to detect the presence of jamming in downstream data communication for cluster‐based wireless sensor networks. The proposed FLJDA keeps an eye on the existing nodes and new node to determine their behavior by applying fuzzy logic on measured jamming detection metrics. To monitor the behavior of the nodes, the FLJDA computes the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The major features of this paper are the following: (1) The jamming detection algorithm is specifically implemented for downstream data communication, (2) cluster head estimates jamming detection metrics for detecting the jamming unlike the existing algorithms where individual nodes explicitly collect the jamming detection metrics, and (3) the proposed algorithm optimizes the jamming detection metrics on the basis of fuzzy logic unlike the existing approaches, which uses merely jamming detection threshold alone for jamming detection. The simulation results of the proposed system provide the true detection ratio as high as 99.89%. The objective of this paper is to detect the presence of jamming in downstream data communication for cluster‐based wireless sensor network. The fuzzy logic–based jamming detection algorithm uses the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The packet delivery ratio and received signal strength indicator of every member in the cluster is measured and assessed by the cluster head (CH), and the CH determines whether the cluster members are jammed or not. The fuzzy logic–based jamming detection algorithm improves the decision making from node centric to CH centric and from crisp centric to fuzzy centric.