FAOACA-SND: Fuzzy selfish node detection employing arithmetic optimization algorithm and cell automata in DTN

Abstract

In recent years, the number of smart devices has grown exponentially. However, the security and privacy of these devices are often neglected, which can lead to serious consequences. In this paper, we propose a method for detecting selfish nodes in DTN. Our method is based on analyzing the behavior of nodes in the network and identifying those that are not following the protocol. Cluster read selection using the hybrid optimized algorithm and cellular automata is used as the first phase of selfish node detection. The second phase uses fuzzy logic to categorize the nodes as selfish or cooperative. Fuzzy logic receives four network parameters from the simulation in MATLAB in a dynamic environment. The four network parameters are packet drop, average delay, residual energy, and nodes' reputations. The simulation results depict that the proposed method is a very accurate way to find selfish nodes. In the proposed mechanism, the proportion of false positives has decreased by 61.53 % compared to the existing mechanism.