Paper Info
Title
CARMA: Channel-Aware Reinforcement Learning-Based Multi-Path Adaptive Routing for Underwater Wireless Sensor Networks
Abstract
Routing solutions for multi-hop underwater wireless sensor networks suffer significant performance degradation as they fail to adapt to the overwhelming dynamics of underwater environments. To respond to this challenge, we propose a new data forwarding scheme where relay selection swiftly adapts to the varying conditions of the underwater channel. Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. We compare the performance of CARMA with that of three other routing solutions, namely, CARP, QELAR and EFlood, through SUNSET-based simulations and experiments at sea. Our results show that CARMA obtains a packet delivery ratio that is up to 40% higher than that of all other protocols. CARMA also delivers packets significantly faster than CARP, QELAR and EFlood, while keeping network energy consumption at bay.
Year
DOI
Venue
2019
10.1109/JSAC.2019.2933968
IEEE Journal on Selected Areas in Communications
Keywords
Field
DocType
Routing,Relays,Wireless sensor networks,Energy consumption,Routing protocols,Reinforcement learning
Computer science,Network packet,Communication channel,Computer network,Real-time computing,Energy consumption,Wireless sensor network,Relay,Reinforcement learning,Routing protocol,Underwater
Journal
Volume
Issue
ISSN
37
11
0733-8716
Citations 
PageRank 
References 
1
0.35
0
Authors
6
Name
Order
Citations
PageRank
Valerio Di Valerio1867.82
Francesco Lo Presti2107378.83
C. Petrioli31713157.55
Luigi Picari431.87
Daniele Spaccini5646.15
Stefano Basagni656452.16