Title
Reliable Hybrid Multicast Routing in Mobile Ad Hoc Networks: Reinforcement Learning-based Approach.
Abstract
Mobile Ad Hoc Networks (MANETs) are types of wireless network consist of mobile nodes which are communicating with each other via wireless radio links without any underlying physical infrastructure. Due to infrastructure-less, node mobility, and unreliable radio links, reliable routing is the most challenging issue in the MANET. In this paper, we proposed a novel heuristic approach called Reinforcement Learning (RL) based Reliable Hybrid Multicast Routing (RL-RHMRP) which possesses the ability to learn the network context and makes routing decisions in the selection of neighbor nodes and route establishment process. RL-RHMRP works based on reliable intermediate node forwarding mechanism and follows Q-Learning (QL) method (one of the RL technique) with On-policy and Model-based features. Reliable Decisive Factor (RDF) is computed based on measured power level, received signal strength, mobility, and link stability of a node. Our scheme chooses the best path from the mesh of paths for data transmission by considering the computed sum of RDF value for both proactive and reactive MANETs region. Simulation evaluation has been done in NS-2 for various performance parameters like Packet Delivery Ratio, Jitter, End-to-end delay, and Overheads in comparison to the zone-based routing protocols such as ZRP (Zone Routing Protocol) and MZRP (Multicast Zone Routing Protocol). It is observed from the result and discussion section that RL-RHMRP outperforms than ZRP and MZRP.
Year
Venue
Keywords
2019
AD HOC & SENSOR WIRELESS NETWORKS
MANETs,hybrid routing,reliability decisive factor,link stability,zone radius,q-learning
Field
DocType
Volume
Mobile ad hoc network,Computer science,Computer network,Multicast,Distributed computing,Reinforcement learning
Journal
45
Issue
ISSN
Citations 
3-4
1551-9899
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Gyanappa A. Walikar100.34
Rajashekhar C. Biradar2899.60