Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks

• To ensure good network performance, a routing protocol for MANETs must change its routing policy online to account for changes in network conditions and to deal with routing information imprecision.
• The main focus of this paper is on the use of fuzzy logic and reinforcement learning.
• A dynamic membership function is defined to enhance the adaptivity of legacy fuzzy logic systems.
• A fuzzy extension of a reinforcement learning based routing protocol for MANETs is presented.
• Dynamic fuzzy logic is more appropriate than reinforcement learning for adaptive energy aware routing in MANETs.

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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