Multivariate fuzzy logic‐based ad hoc on‐demand distance vector protocol under the impact of node mobility in wireless ad hoc networks

Abstract

SummaryEfficient routing of generated packets through the network with minimal overhead in path discovery and subsequent route maintenance is the fundamental objective required in the modern wireless network operation. Recently, the application of autonomic computational learning techniques for design and optimization of routing protocols in ad hoc networks is substantially gaining the research interest. The commonly deployed soft computing methodology of fuzzy inference system is capable of handling uncertain and imprecise networking information related to the frequently changing states of generic mobile technologies. In this paper, we propose a novel fuzzy logic‐based ad hoc on‐demand distance vector (FL‐AODV) routing protocol employing the multivariate cross‐layer design architecture to optimize the multiple performance parameters in wireless ad hoc networks. This fuzzy optimization framework essentially applies the header length from data link and physical layers, route timeout from network layer, and node mobility speed from application layer as inputs to the fuzzification interface. Besides, bit rate for application layer and communication range parameter for data link layer are scrutinized as the fuzzy outputs derived from the defuzzifier. The designed adaptive routing protocol is extensively assessed through simulation experimental analysis under the varying effects of node mobility conditions. Various network performance attributes including the reception cache hit, packet delivery ratio, packet errors, ping loss rate, mean throughput, and delay are computed and analyzed for comprehensive comparison between the presented fuzzy‐based FL‐AODV and classical AODV routing mechanisms. Finally, we compare our fuzzy routing model with previous algorithms to demonstrate its efficacy in terms of key performance metrics of throughput and delay.