Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks |
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| Affiliation: | 1. Department of Information Technology, India;2. Department of Instrumentation and Control Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded-431606 (MS), India;3. Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded-431606 (MS), India |
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| Abstract: | In this paper, a dynamic fuzzy energy state based AODV (DFES-AODV) routing protocol for Mobile Ad-hoc NETworks (MANETs) is presented. In DFES-AODV route discovery phase, each node uses a Mamdani fuzzy logic system (FLS) to decide its Route REQuests (RREQs) forwarding probability. The FLS inputs are residual battery level and energy drain rate of mobile node. Unlike previous related-works, membership function of residual energy input is made dynamic. Also, a zero-order Takagi Sugeno FLS with the same inputs is used as a means of generalization for state-space in SARSA-AODV a reinforcement learning based energy-aware routing protocol. The simulation study confirms that using a dynamic fuzzy system ensures more energy efficiency in comparison to its static counterpart. Moreover, DFES-AODV exhibits similar performance to SARSA-AODV and its fuzzy extension FSARSA-AODV. Therefore, the use of dynamic fuzzy logic for adaptive routing in MANETs is recommended. |
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| Keywords: | MANETs Routing protocol Fuzzy logic system Dynamic membership function Reinforcement learning |
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