QELAR
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We find that Q-learning is very suitable in UW-ASNs in the following ways: | We find that Q-learning is very suitable in UW-ASNs in the following ways: | ||
| - | *'''Low Overhead.''' | + | *'''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. | ||
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| + | * '''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 == | == Design == | ||
Revision as of 13:55, 23 May 2011
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
Simulation Tools
Downloads
Installation Guide
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