QELAR
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In summary, the reward that Node <math>n</math> forwards a packet to Node <math>m</math> is | In summary, the reward that Node <math>n</math> forwards a packet to Node <math>m</math> is | ||
<math> | <math> | ||
| - | r(n,a,m)=-g-\alpha_1[c(n)+c(m)]+\alpha_2[d(n)+d(m)], | + | r(n,a,m)=-g-\alpha_1\left[c(n)+c(m)\right]+\alpha_2\left[d(n)+d(m)\right], |
</math> | </math> | ||
where <math>\alpha_1</math> and <math>\alpha_2</math> are the constant weights that can be adjusted. | where <math>\alpha_1</math> and <math>\alpha_2</math> are the constant weights that can be adjusted. | ||
Revision as of 15:06, 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
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:
![Q(s,a)\leftarrow(1-\alpha)Q(s,a)+\alpha\left[r(s,a)+\gamma\max_aQ(s',a)\right],](/nueess/images/math/b/c/b/bcbff2bebe87680f3251621f179ac9e2.png)
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. Energy distribution should also be considered to ensure that the energy is distributed evenly among a group of sensor nodes, which includes the node n that holds a packet and all its direct neighbors in the transmission range. It is defined as
In summary, the reward that Node n forwards a packet to Node m is
![r(n,a,m)=-g-\alpha_1\left[c(n)+c(m)\right]+\alpha_2\left[d(n)+d(m)\right],](/nueess/images/math/f/5/6/f567ebc5bea4476b23decb4b96028ab2.png)
where α1 and α2 are the constant weights that can be adjusted.
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.
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