Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey

Underwater wireless sensor networks (UWSNs) have emerged as a promising networking technology owing to their various underwater applications. Many applications require sensed data to be routed to a centralized location. However, the routing of sensor networks in underwater environments presents seve...

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Autores principales:	Rehenuma Tasnim Rodoshi, Yujae Song, Wooyeol Choi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Underwater wireless sensor network routing protocol reinforcement learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/35e644aa5e2d4dc59dc5a885a5aa3ee0
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id	oai:doaj.org-article:35e644aa5e2d4dc59dc5a885a5aa3ee0
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spelling	oai:doaj.org-article:35e644aa5e2d4dc59dc5a885a5aa3ee02021-11-25T00:00:33ZReinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey2169-353610.1109/ACCESS.2021.3128516https://doaj.org/article/35e644aa5e2d4dc59dc5a885a5aa3ee02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615209/https://doaj.org/toc/2169-3536Underwater wireless sensor networks (UWSNs) have emerged as a promising networking technology owing to their various underwater applications. Many applications require sensed data to be routed to a centralized location. However, the routing of sensor networks in underwater environments presents several challenges in terms of underwater infrastructure, including high energy consumption, narrow bandwidths, and longer propagation delays than other sensor networks. Efficient routing protocols play a vital role in this regard. Recently, reinforcement learning (RL)-based routing algorithms have been investigated by different researchers seeking to exploit the learning procedure via trial-and-error methods of RL. RL algorithms are capable of operating in underwater environments without prior knowledge of the infrastructure. This paper discusses all routing protocols proposed for RL-based UWSNs. The advantages, disadvantages, and suitable application areas are also mentioned. The protocols are compared in terms of the key ideas, RL designs, optimization criteria, and performance-evaluation techniques. Moreover, research challenges and outstanding research issues are also highlighted, to indicate future research directions.Rehenuma Tasnim RodoshiYujae SongWooyeol ChoiIEEEarticleUnderwater wireless sensor networkrouting protocolreinforcement learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154578-154599 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Underwater wireless sensor network routing protocol reinforcement learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Underwater wireless sensor network routing protocol reinforcement learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Rehenuma Tasnim Rodoshi Yujae Song Wooyeol Choi Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey
description	Underwater wireless sensor networks (UWSNs) have emerged as a promising networking technology owing to their various underwater applications. Many applications require sensed data to be routed to a centralized location. However, the routing of sensor networks in underwater environments presents several challenges in terms of underwater infrastructure, including high energy consumption, narrow bandwidths, and longer propagation delays than other sensor networks. Efficient routing protocols play a vital role in this regard. Recently, reinforcement learning (RL)-based routing algorithms have been investigated by different researchers seeking to exploit the learning procedure via trial-and-error methods of RL. RL algorithms are capable of operating in underwater environments without prior knowledge of the infrastructure. This paper discusses all routing protocols proposed for RL-based UWSNs. The advantages, disadvantages, and suitable application areas are also mentioned. The protocols are compared in terms of the key ideas, RL designs, optimization criteria, and performance-evaluation techniques. Moreover, research challenges and outstanding research issues are also highlighted, to indicate future research directions.
format	article
author	Rehenuma Tasnim Rodoshi Yujae Song Wooyeol Choi
author_facet	Rehenuma Tasnim Rodoshi Yujae Song Wooyeol Choi
author_sort	Rehenuma Tasnim Rodoshi
title	Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey
title_short	Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey
title_full	Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey
title_fullStr	Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey
title_full_unstemmed	Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey
title_sort	reinforcement learning-based routing protocol for underwater wireless sensor networks: a comparative survey
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/35e644aa5e2d4dc59dc5a885a5aa3ee0
work_keys_str_mv	AT rehenumatasnimrodoshi reinforcementlearningbasedroutingprotocolforunderwaterwirelesssensornetworksacomparativesurvey AT yujaesong reinforcementlearningbasedroutingprotocolforunderwaterwirelesssensornetworksacomparativesurvey AT wooyeolchoi reinforcementlearningbasedroutingprotocolforunderwaterwirelesssensornetworksacomparativesurvey
_version_	1718414687943524352

Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks: A Comparative Survey

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