QELAR

From NUEESS

Contents 1 Overview 2 Design 2.1 Q-learning 2.2 Reward Function 3 Related Publications 4 Simulation Tools 4.1 Downloads 4.2 Installation Guide

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

Q-learning

Q-learning is one type of Reinforcement Learning algorithms, by which a system can learn to achieve a goal in control problems based on its experience. An agent in RL chooses actions according to the current state of a system and the reinforcement it receives from the environment. Most RL algorithms are based on estimating value functions, functions of states (or of state-action pairs), which evaluate how good it is for the agent to be in a given state (or how good it is to perform an action in a given state).

We denote the value of taking an action a in a state s as Q(s,a), and the direct reward of taking such an action as r(s,a). The optimal Q(s,a) can be approximated by the following iteration:

where s' is the next state, α and γ is the learning rate and future discount, respectively.

Reward Function

The reward function in Q-learning determines the behavior of the learning agent. In QELAR, we consider the following three rewards:

• The penalty of forwarding a packet. Forwarding a packet consumes energy, occupies channel bandwidth, and contributes to the delay. Therefore forwarding a packet should always receive a negative reward, which is − g.
• Residual energy. Lower reward should be given if the residual energy of either the sender or the receiver is low. Therefore, forwarding to a packet to a node with low residual energy can be avoided. The reward related to residual energy of Node n is defined as

• Energy distribution.

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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