@inproceedings{4dfd5d1b7fdd48ec8232997bf3cdb9d4,
title = "Topology-Aware Reinforcement Learning Routing Protocol in Underwater Wireless Sensor Networks",
abstract = "Existing reinforcement learning (RL)-based routing protocols in underwater wireless sensor networks (UWSNs) do not consider the network topology when selecting a next-forwarder for packet forwarding. To eliminate resource waste from the forwarding in a wrong direction, this paper proposes a network topology-aware RL routing protocol for UWSNs. Taking the network topology into account, sensor nodes first find next-forwarder candidates and then select a highest-valued one of them to forward data. The simulation result shows that the proposed scheme outperforms QELAR in terms of latency and total energy consumption.",
keywords = "reinforcement learning, routing, underwater wireless sensor networks",
author = "Kim, \{Hee Won\} and Junho Cho and Cho, \{Ho Shin\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th International Conference on Information and Communication Technology Convergence, ICTC 2019 ; Conference date: 16-10-2019 Through 18-10-2019",
year = "2019",
month = oct,
doi = "10.1109/ICTC46691.2019.8939720",
language = "English",
series = "ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "124--126",
booktitle = "ICTC 2019 - 10th International Conference on ICT Convergence",
address = "United States",
}