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B P Patil
Professor, E & TC Department, Army Institute of Technology, Pune, India

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Dual Reinforcement Q Routing for Ad Hoc Networks Rahul M Desai; B P Patil
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i3.pp786-794

Abstract

Ad Hoc Networks are infrastructure less network in which nodes are connected by Multi-hop wireless links. Each node is acting as a router as it supports distributed routing. Routing challenges occurs as there are frequent path breaks due to the mobility. Various application domains include military applications, emergency search and rescue operations and collaborative computing. The existing protocols used are divided into proactive and on demand routing protocols. The various new routing algorithms are also designed to optimize the performance of a network in terms of various performance parameters. Dual reinforcement routing is learning based approach used for routing. This paper describes the implementation, mathematical evaluation and judging the performance of a network and analyze it to find the performance of a network.
Learning Based Route Management in Mobile Ad-Hoc Networks Rahul M Desai; B P Patil; Davinder Pal Sharma
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i3.pp718-723

Abstract

Ad hoc networks are mobile wireless networks where each node is acting as a router. The existing routing protocols such as Destination sequences distance vector, Optimized list state routing protocols, Ad hoc on demand routing protocol, dynamic source routing are optimized versions of distance vector or link state routing protocols.  Reinforcement Learning is new method evolved recently which is learning from interaction with an environment. Q Learning which is based on Reinforcement learning that learns from the delayed reinforcements and becomes more popular in areas of networking. Q Learning is applied  to the routing algorithms where the routing tables in the distance vector algorithms are replaced by the estimation tables called as Q values. These Q values are based on the link delay. In this paper, various optimization techniques over Q routing are described in detail with their algorithms.
Co-Authors Davinder Pal Sharma Rahul M Desai