Open AccessLatency‐aware reinforced routing for opportunistic networks
Author(s) -
Sharma Deepak Kumar,
Gupta Sarthak,
Malik Shubham,
Kumar Rohit
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2020.0149
Subject(s) - computer science , computer network , latency (audio) , routing protocol , dynamic source routing , distributed computing , zone routing protocol , static routing , policy based routing , geographic routing , link state routing protocol , path vector protocol , routing (electronic design automation) , telecommunications
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.