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

被引：38
作者：
Chettibi, Saloua [1 ]
Chikhi, Salim [1 ]
机构：
[1] Univ Constantine 2, SCAL Team, MISC Lab, Constantine, Algeria
来源：
APPLIED SOFT COMPUTING | 2016年 / 38卷
关键词：
MANETs; Routing protocol; Fuzzy logic system; Dynamic membership function; Reinforcement learning; KRILL HERD ALGORITHM; PROTOCOLS;
D O I：
10.1016/j.asoc.2015.09.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：321 / 328
页数：8
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