Selecting an efficient algorithm for Adhoc network routing using multi-criteria choice in algorithms based on reinforcement learning and network traffic

The primaryaim of an ad-hoc network routing protocol is accurate and efficient route creation between node pairs so that messages may be delivered promptly. Route creation need to be done with reduced overhead and bandwidth. This paper presents a scheme to reduce bandwidth and power by the hibernation of nodes for a limited time. The effect of our proposal is then studied by simulation under various conditions and the analysis of the simulation results is done to comprehend the working of our protocol in various areas and how it fares in an application specific scenario.

Download Full-text

Deep Reinforcement Learning Enabled Network Routing Optimization Approach with an Enhanced DDPG Algorithm

Asia Communications and Photonics Conference/International Conference on Information Photonics and Optical Communications 2020 (ACP/IPOC) ◽

10.1364/acpc.2020.m4a.206 ◽

2020 ◽

Author(s):

Lingyu Meng ◽

Wen Yang ◽

Bingli Guo ◽

Shanguo Huang

Keyword(s):

Reinforcement Learning ◽

Network Routing ◽

Optimization Approach ◽

Routing Optimization

Download Full-text

A Distributed Reinforcement Learning Scheme for Network Routing

Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications ◽

10.4324/9780203773826-9 ◽

2013 ◽

pp. 55-61

Keyword(s):

Reinforcement Learning ◽

Network Routing ◽

Learning Scheme ◽

Distributed Reinforcement

Download Full-text

Deep Reinforcement Learning based Load Balancing Policy for balancing network traffic in datacenter environment

2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT) ◽

10.1109/icgciot.2018.8752969 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ashwini R. Doke ◽

K. Sangeeta

Keyword(s):

Reinforcement Learning ◽

Load Balancing ◽

Network Traffic

Download Full-text

Prioritized Sweeping Reinforcement Learning Based Routing for MANETs

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v5.i2.pp383-390 ◽

2017 ◽

Vol 5 (2) ◽

pp. 383 ◽

Cited By ~ 1

Author(s):

Rahul M Desai ◽

B P Patil

Keyword(s):

Reinforcement Learning ◽

Shortest Path ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Network Routing ◽

Delivery Ratio ◽

Learning Method ◽

Shortest Path Routing ◽

Mobile Ad Hoc ◽

Prioritized Sweeping

<p class="Default">In this paper, prioritized sweeping confidence based dual reinforcement learning based adaptive network routing is investigated. Shortest Path routing is always not suitable for any wireless mobile network as in high traffic conditions, shortest path will always select the shortest path which is in terms of number of hops, between source and destination thus generating more congestion. In prioritized sweeping reinforcement learning method, optimization is carried out over confidence based dual reinforcement routing on mobile ad hoc network and path is selected based on the actual traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on 50 Nodes Mobile ad hoc networks with random mobility. Various performance parameters such as Interval and number of nodes are used for judging the network. Packet delivery ratio, dropping ratio and delay shows optimum results using the prioritized sweeping reinforcement learning method.</p>

Download Full-text