Paper Info
Title
Balancing Tradeoffs for Energy-Efficient Routing in MANETs Based on Reinforcement Learning
Abstract
This paper proposes an energy-efficient path selection algorithm which aims at balancing the contrasting objectives of maximizing network lifetime and minimizing energy consumption routing in mobile ad hoc networks (MANETs). The method is based on a reinforcement learning technique called the on-policy Monte Carlo (ONMC) method. Simulation results under high mobility environments reveal that variants of the proposed method can achieve the lowest long-term cost, which is a function that depicts the optimal tradeoff balance in the long run, when compared with existing tradeoff balancing schemes such as variants of the conditional max-min battery capacity routing (CMMBR) [2] and the best minimum combined-cost routing algorithm [4].
Year
DOI
Venue
2008
10.1109/VETECS.2008.523
VTC Spring
Keywords
Field
DocType
Monte Carlo methods,ad hoc networks,learning (artificial intelligence),minimisation,mobile radio,telecommunication computing,telecommunication network reliability,telecommunication network routing,MANET,energy consumption routing minimization,energy-efficient path selection algorithm,energy-efficient routing,mobile ad hoc network,network lifetime maximization,on-policy Monte Carlo method,reinforcement learning technique
Link-state routing protocol,Dynamic Source Routing,Policy-based routing,Static routing,Computer science,Computer network,Destination-Sequenced Distance Vector routing,Wireless Routing Protocol,Optimized Link State Routing Protocol,Wireless ad hoc network,Distributed computing
Conference
ISSN
Citations 
PageRank 
1550-2252
4
0.43
References 
Authors
6
2
Name
Order
Citations
PageRank
Wibhada Naruephiphat1151.79
Wipawee Usaha2102.89