Bipolar fuzzy information‐based PROMETHEE‐based outranking scheme for mitigating vampire attack in wireless sensor networks

Abstract

The sensor nodes comprising sensing devices, small processing units, and battery, cooperate in wireless sensor networks (WSNs) for gathering potential factors that aids in achieving significant decision‐making processes in deployed applications. WSNs are vulnerable to security threats due to their open nature, availability characteristics, and lack of energy, resulting in a diversified number of security attacks. Among the security attacks, vampire attack is the serious threat as they drain the energy of nodes, which in turn crumbles the network lifetime and energy stability in WSNs. In this article, Bi‐Polar Information‐Based Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE)‐based outranking scheme is proposed for detecting and isolating vampire attacks to improve throughput with minimized energy consumptions and delay for prolonging lifetime. This proposed BPFI‐POR scheme used bipolar information that facilitates an asymmetrical association between two decision making factors that influences the discrimination and selection of genuine nodes from vampire nodes during the routing process. It aids in characterizing precise and vague information through bipolar fuzzy linguistic terms that has the maximized probability of being represented as trapezoidal bipolar fuzzy numbers. It estimated the cooperation of intermediate sensor nodes' based on trapezoidal bipolar fuzzy numbers for determining their preference or selection using criteria that could be influenced during the routing process. It also used the ranking function for accessing the alternatives through crisp real preferences with entropy weighting information responsible for attributes weight computation. The simulation results of the BPFI‐POR scheme confirmed better throughput, detection time with minimized energy consumptions and latency on par with the competitive algorithms.