PGO-DLLA: Parallel Grid Optimization by the Daddy Long-Legs Algorithm for Preventing Black Hole Attacks in MANETs

Abstract

Mobile ad hoc networks (MANETs) are wireless networks that are considered a good alternative to the other types of networks during the hardest times such as wars or natural environment disasters. MANETs have the capability of working without any need for base stations or infrastructures. However, MANETs are subject to severe attacks, such as the black hole attack. Many researchers in the field of secure routing and network security have been working on acceptable solutions to prevent black hole attacks in MANETs. Unfortunately, most of the proposals are not attainable or have performance difficulties. One of the most ambitious goals in the research is to find a way to prevent black hole attacks without decreasing network throughput or increasing routing overhead. Swarm intelligence is a research area for information models that studies the collective behavior of insects or animal swarms. Some algorithms have been proposed to address black hole attacks through new protocols and improving routing security with swarm intelligence. In this paper, we propose a parallel grid algorithm for MANETs that optimizes both routing discovery and security in an Ad Hoc On-Demand Distance Vector (AODV). The new technique, called Parallel Grid Optimization by the Daddy Long-Legs Algorithm (PGO-DLLA), simulates the behavior of the biological spiders known as daddy long-legs spiders. Experiments were conducted on an NS2 simulator to demonstrate the efficiency and robustness of the proposed algorithm. The results indicate better performance than the AntNet algorithm with respect to all metrics except throughput, for which AntNet is the better algorithm. In addition, the results show that PGO-DLLA outperforms the standard AODV algorithm in simulations of both a peaceful environment and a hostile environment represented by a black hole.