Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 37, Issue 11, Pages 2634-2647Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2019.2933968
Keywords
Underwater wireless sensor networks; multi-path routing; reinforcement learning; in-field experiments
Funding
- EC EASME ArcheoSub project Autonomous underwater Robotic and sensing systems for Cultural Heritage discovery Conservation and in situ valorization
- Sapienza's IoT4Offshore
- MIUR Dipartimenti di eccellenza 2018-2022 of the Department of Computer Science of Sapienza University
- NSF [CNS 1726512]
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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.
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