QELAR
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Contents |
Overview
QELAR is a Q-learning-based energy-efficent and lifetime-aware routing protocol. It is designed to address various issues related to underwater acoustic sensor networks (UW-ASNs). By learning the environment and evaluating an action-value function (Q-value), which gives the expected reward of taking an action in a given state, the distributed learning agent is able to make a decision automatically.
We find that Q-learning is very suitable in UW-ASNs in the following ways:
- Low Overhead. Nodes only keep the routing information of their direct neighbor nodes which is a small subset of the network. The routing information is updated by one-hope broadcasts rather than flooding.
- Dynamic Network Topology. Topology changes happen frequently in the harsh underwater environment. Without GPS available underwater, QELAR learns from the network environment and allows a fast adaptation to the current network topology.
- Load Balance. QELAR takes node energy into consideration in Q-learning, so that alternative paths can be chosen to use network nodes in a fair manner, in order to avoid 'hot spots' in the network.
- General Framework. Q-learning is a framework that can be easily extended. We can easily integrate other factors such as end-to-end delay and node density for extension and can balance all the factors according to our need by tuning the parameters in the reward function.
Design
Related Publications
- T. Hu and Y. Fei, “QELAR: A machine-learning-based adaptive routing protocol for energy efficient and lifetime-extended underwater sensor networks,” IEEE Trans. on Mobile Computing, vol. 9, no. 6, June 2010.
- T. Hu and Y. Fei, “QELAR - A Q-learning-based energy-efficient and lifetime-aware routing protocol for underwater sensor networks,” in IEEE Int. Performance Computing & Communications Conf., Dec. 2008.
Simulation Tools
Downloads
Installation Guide
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