NDLI: Latency-aware reinforced routing for opportunistic networks

Content Provider	IET Digital Library
Author	Sharma, Deepak Kumar Gupta, Sarthak Malik, Shubham Kumar, Rohit
Abstract	In opportunistic networks, the path connecting two nodes is not continuous at any time instant. In such an environment, routing is an extremely taxing word owing to the ever-changing nature of the network and random connections between nodes. Routing in such networks is done by a store carry forward mechanism, in which local information is used to make opportunistic routing decisions. In this study, the authors present a novel dynamic and intelligent self-learning routing protocol that is an improvement of the history-based routing protocol for opportunistic (HiBOp) networks. The proposed method presents a novel solution for the estimation of average latency between any two nodes, which is used along with reinforcement learning to dynamically learn the nodes' interactions. Simulation results on a real mobility trace (INFOCOM 2006) show that latency-aware reinforced routing for opportunistic network applied to HiBOp outperforms the original HiBOp protocol by 14.4% in terms of delivery probability, 15% in terms of average latency and 34.7% in terms of overhead ratio.
Starting Page	2981
Ending Page	2989
Page Count	9
ISSN	17518628
Volume Number	14
e-ISSN	17518636
Issue Number	Issue 17, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/14/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2020.0149
Journal	IET Communications
Publisher Date	2020-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Average Latency Estimation Communication Computing Communication Network Design, Planning And Routing Delay Dynamic Self-learning Routing Protocol History-based Routing Intelligent Self-learning Routing Protocol Knowledge Engineering Technique Latency-aware Reinforced Routing Learning in AI Mobile Computing Mobile Radio System Node Opportunistic Network Opportunistic Routing Decisions Probability Protocol Random Connections Reinforcement Learning Routing Protocol Statistics Telecommunication Computing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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