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

Abstract

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%.

Fuzzy-Logic Based IDS for Detecting Jamming Attacks in Wireless Mesh IoT Networks

“…[16]. From the outcomes, it was recognised that the PSR and PDR were difficult to decide about jamming and its types [17]. • Average number of required transmissions per packet (ATX): Heo et al [18] define the ATX metric as a total number of transmissions divided by the number of successfully received unique packets.…”

“…However, it is hard to measure the BER by a sensor node since it needs to collect a tremendous amount of data. Moreover, this method cannot classify different kinds of jamming attacks [17]. • Packets Dropped per Terminal (PDPT): Misra et al…”

Rapid jamming detection approach based on fuzzy in WSN

“…The jamming detection technique (JDT) 22 and a framework 23 determine the occurrence of jamming by comparing measured PDR and RSSI with their threshold value. In Vijayakumar et al, 24 fuzzy logic is applied for enhancing the detection accuracy. The proposed system applies the adaptive neuro fuzzy logic, 25 statistical approach, 26 and multi criteria decision-making system to identify the jamming.…”

Energy‐efficient design of 3D UWSNs leveraging compressive sensing and principal component analysis: A communication techniques perspective

“…Due to the availability of limited supply of power in the sensor node, clustering technique is generally incorporated into the wireless sensor network . The basic idea behind the clustering is to divide the entire field of interest into small nonoverlapping segments called as clusters.…”

“…9,10 Due to the availability of limited supply of power in the sensor node, clustering technique is generally incorporated into the wireless sensor network. 11,12 The basic idea behind the clustering is to divide the entire field of interest into small nonoverlapping segments called as clusters. After formation of the clusters, in each cluster, there is one cluster head (CH) that receives the data from their cluster members from its own cluster, and finally, CHs aggregate the received information into a single lump of data and transfer this to the BS.…”