PGO-DLLA: Parallel Grid Optimization by the Daddy Long-Legs Algorithm for Preventing Black Hole Attacks in MANETs (Informatica, 2015)
Mobile ad hoc networks (MANETs) are wireless networks that are considered a good alternative to theother 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 ofsecure routing and network security have been working on acceptable solutions to prevent black holeattacks in MANETs. Unfortunately, most of the proposals are not attainable or have performancedifficulties. One of the most ambitious goals in the research is to find a way to prevent black hole attackswithout decreasing network throughput or increasing routing overhead. Swarm intelligence is aresearch 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 andimproving routing security with swarm intelligence. In this paper, we propose a parallel grid algorithmfor MANETs that optimizes both routing discovery and security in an Ad Hoc On-Demand DistanceVector (AODV). The new technique, called Parallel Grid Optimization by the Daddy Long-LegsAlgorithm (PGO-DLLA), simulates the behavior of the biological spiders known as daddy long-legsspiders. Experiments were conducted on an NS2 simulator to demonstrate the efficiency and robustnessof the proposed algorithm. The results indicate better performance than the AntNet algorithm withrespect to all metrics except throughput, for which AntNet is the better algorithm. In addition, the resultsshow that PGO-DLLA outperforms the standard AODV algorithm in simulations of both a peacefulenvironment and a hostile environment represented by a black hole.