Evolutionary Algorithms for Enhancing Mobile Ad Hoc Network Security

Abstract

AbstractThe exponential growth of IoT has increased the demand for faster communication and higher computational capacity for the processing and sharing of data. Fog computing along with the 5G technologies is expected to be the solution for fulfilling this requirement. The improvements of 5G technologies come with a few challenges including security and privacy control. Therefore, emerging 6G technologies that direct fog computing toward intelligent edge computing are considered as the future applications of modern fog computing. Wireless ad hoc networks play a vital role in implementing these applications by providing the necessary connectivity to multiple IoT devices. Wireless ad hoc network technologies have improved in quality over the recent decades due to the low cost and minimal hardware requirements. One of the widely extended types of wireless ad hoc networks is mobile ad hoc networks that consist of wireless and autonomous mobile nodes with a decentralized architecture. Therefore, these have dynamic network topologies where the network experiences typical drawbacks and security issues such as unreliable communication; limited transmission range; interference attacks, including eavesdropping; blackhole attack; wormhole attack; flooding; etc. Hence, there is a need for secure and reliable communication mechanisms for mobile ad hoc networks. This chapter explains how evolutionary methods available in computational intelligence can be applied to overcome the issues of mobile ad hoc networks in terms of security and reliable communications. Evolutionary algorithms consist of heuristic and population-based collective learning approaches that are self-adaptive and robust. Those can be used for approximating solutions for all types of problems that cannot be solved numerically in an efficient manner. When analyzing the available biological processes, similarities can be found in mobile ad hoc networks and biological systems, such as self-organizing, failure recovery, generating stability, collaborations, resource minimization, etc. These can be applied for designing the network and optimizing it to have reliable communication links. Evolutionary algorithms are commonly utilized and inspired to be used with the biological systems where they act as an optimization method to find the solution. Hence, the use of evolutionary algorithms that apply metaheuristics to address the available security problems of mobile ad hoc networks along with IoT (Internet of Things) services is explained in depth in the chapter.KeywordsFog computing5G6GEdge computingMobile ad hoc networkSecurityUnreliable communicationEvolutionary algorithmsOptimization methodsGenetic algorithmsIoT (Internet of Things)