QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks
Abstract
Underwater sensor network (UWSN) has emerged in recent years as a promising networking technique for various aquatic applications. Due to specific characteristics of UWSNs, such as high latency, low bandwidth, and high energy consumption, it is challenging to build networking protocols for UWSNs. In this paper, we focus on addressing the routing issue in UWSNs. We propose an adaptive, energy-efficient, and lifetime-aware routing protocol based on reinforcement learning, QELAR. Our protocol assumes generic MAC protocols and aims at prolonging the lifetime of networks by making residual energy of sensor nodes more evenly distributed. The residual energy of each node as well as the energy distribution among a group of nodes is factored in throughout the routing process to calculate the reward function, which aids in selecting the adequate forwarders for packets. We have performed extensive simulations of the proposed protocol on the Aqua-sim platform and compared with one existing routing protocol (VBF) in terms of packet delivery rate, energy efficiency, latency, and lifetime. The results show that QELAR yields 20 percent longer lifetime on average than VBF.
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In QELAR, rewards account for both the packet transmission energy and the neighbor node's residual energy.
“In QELAR, rewards account for both the packet transmission energy (incurred for forwarding the packet to the neighbor node) and the neighbor node's residual energy.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
Hu and Fei propose QELAR, a model-based variant of the Q-routing algorithm, to provide faster convergence, route cost reduction, and energy preservation in underwater WSNs.
“Hu and Fei [197] propose QELAR, a model-based variant of the Q-routing algorithm, to provide faster convergence, route cost reduction, and energy preservation in underwater WSNs.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
QELAR is 25% more energy efficient than VBF.
“Results show that QELAR is 25% more energy efficient than VBF.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
Taking into account the residual energy helps achieve a balanced energy distribution among nodes by avoiding highly utilized routes (hotspots).
“Taking into account the residual energy helps achieve a balanced energy distribution among nodes by avoiding highly utilized routes (hotspots).”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
The lifetime of the network is 25%∼30% higher with QELAR in the presence of failures and network partition compared with VBF with comparable transmission range.
“The lifetime of the network is 25% ∼ 30% higher with QELAR in the presence of failures and network partition compared with VBF with comparable transmission range, which makes QELAR more robust to faults.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
QELAR is more robust to faults than VBF.
“which makes QELAR more robust to faults”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
Both QELAR and VBF show comparable routing efficiency and delivery rates.
“Whereas, both show comparable routing efficiency and delivery rates.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
Utility functions such as QoS provisioning, load balancing and network lifetime maximization require more information to be disseminated at the cost of increased complexity and communication overhead.
“utility functions such as QoS provisioning, load balancing and network lifetime maximization, as in Q-PR [24], QELAR [197,277], require more information to be disseminated at the cost of an increased complexity and communication overhead.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [197]
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- Authors
- Hu, T., & Fei, Y.
- Affiliations
- University of Connecticut (US)
- ORCID
- Yunsi Fei
- Venue
- IEEE Transactions on Mobile Computing
- Published
- 2010-06
- Volume
- 9
- Issue
- 6
- Pages
- 796-809
- ISSN
- 1536-1233 (ISSN-L)
- OpenAlex
- W2130509337
- Cited by
- 405
- References
- 26
- Citations by year
- 2026: 25 · 2025: 23 · 2024: 37 · 2023: 38 · 2022: 37 · 2021: 44 · 2020: 40 · 2019: 39 · 2018: 32 · 2017: 20 · 2016: 17 · 2015: 15 · 2014: 13 · 2013: 6 · 2012: 10
- FWCI
- 17.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Mobile Computing (publishedVersion)
- Funders
- National Science Foundation
- University of Connecticut
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Keywords
- underwater wireless sensor networks, adaptive routing protocol, energy efficiency, network lifetime, reinforcement learning, reward function, packet delivery ratio, latency
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