Research Area: Mobile Ad Hoc Networks
Mobile Ad-hoc Networks (MANETs) consist of mobile nodes that usually have limited energy resources. MANET routing protocols should consider the dynamics and energy constraints of the network, and this makes them an optimization problem. Various optimization-based MANET routing protocols have been proposed in literature and each of them consider different metrics and try to cope with specific problems. In this paper, a novel heterogeneous MANET routing protocol called learning Automata and Genetic based Ad hoc On-Demand Distance Vector (AGEN-AODV) is proposed, in which routes are rated based on energy, stability, traffic, and hop-count criteria. The Genetic Algorithm (GA) in conjunction with Learning Automata (LA) is used to select the optimal route. The LA runs concurrent to the GA and initializes, adjusts and optimizes its coefficients based on the network feedback, preventing the GA from divergence or sub-optimal convergence. Compared with related works, the throughput, packet delivery ratio (PDR), delay, network lifetime, and energy consumption are improved by at least 4%, 8%, 8%, 13%, and 30% respectively.
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Author(s) Name: Mohammad Nabati, Mohsen Maadani & Mohammad Ali Pourmina
Journal name: Mobile Networks and Applications
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Publisher name: Springer
DOI: 10.1007/s11036-021-01821-6
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