QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks

By Tiansi Hu, Yunsi Fei. IEEE Transactions on Mobile Computing, 2010

381 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/019e77139621e5ed542a9664551a4a04895fd8bd

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In Computer Networks and Communications

In Electrical and Electronic Engineering

In Ocean Engineering

Citations in the corpus

Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges (2019, methodology): “Multipath routing like DBR [42], MRP [46], and EEDBR [43] performs far better compared to single path routing protocols like QELAR [64] and H2-DAB [65].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “QELAR is evaluated and compared against the vectorbased forwarding protocol (VBF) through simulations with 250 mobile sensor nodes uniformly deployed in a 3D space.”
Application of reinforcement learning to routing in distributed wireless networks: a review (2013, background): “Hu and Fei (2010) use a system model to estimate the converged Q-values of all actions so that it may not be necessary to update Q-values only after taking the corresponding actions.”
Underwater Optical Wireless Communications, Networking, and Localization: A Survey (2018, background): “A machine learning based routing protocol was proposed in [246] which is energy efficient and improves the lifetime of the network.”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “ing protocol that distributes traffic evenly among all sensors was suggested in [68], which proposed an adaptive, energyefficient, and lifetime-aware routing scheme, called QELAR,”
6G and Beyond: The Future of Wireless Communications Systems (2020, methodology): “In routing protocol design for wireless sensor networks, researchers have successfully utilized reinforcement learning methods to achieve a more energy-efficient routing scheme for underwater sensor networks [83].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “Using MRL, an agent can compute the state transition probability, which is used to select a next-hop node for packet transmission in routing [164].”