2011-05-24T15:22:29Z

Tiansi: /* Packet Format */

== 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-hop 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 ==
[[Image: rl.jpg|thumb|300px]]
=== 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 <math>a</math> in a state <math>s</math> as <math>Q(s,a)</math>, and the direct reward of taking such an action as <math>r(s,a)</math>. The optimal <math>Q(s,a)</math> can be approximated by the following iteration:
<math>
Q(s,a)\leftarrow(1-\alpha)Q(s,a)+\alpha\left[r(s,a)+\gamma\max_aQ(s',a)\right],
</math>

where <math>s'</math> is the next state, <math>\alpha</math> and <math>\gamma</math> 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 <math>-g</math>.
*'''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 <math>n</math> is defined as
<math>
c(n)=\frac{E_{res}(n)}{E_{init}(n)}
</math>
*'''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 <math>n</math> that holds a packet and all its direct neighbors in the transmission range. It is defined as
<math>
d(n)=\frac{2}{\pi}\arctan(E_{res}(n)-\overline{E}(n)).
</math>
where <math>\overline{E}(n)</math> is the average residual energy in Node <math>n</math>'s group.

In summary, the reward that Node <math>n</math> forwards a packet to Node <math>m</math> is
<math>
r(n,a,m)=-g-\alpha_1\left[c(n)+c(m)\right]+\alpha_2\left[d(n)+d(m)\right],
</math>
where <math>\alpha_1</math> and <math>\alpha_2</math> are the constant weights that can be adjusted.

=== Packet Format ===
[[Image: pf.jpg|thumb|300px]]

The packet header includes three parts: wo packet-related fields, a tuple related to the previous forwarder and the routing decision made by the previous forwarder.

The two packet-related field are
*'''Packet ID''', the unique ID of the packet, and
*'''Destination Address''', the address where the packet should be ultimately send to.

Before forwarding the packet, a node should put its own information in the tuple:

*'''V Value''', the current V value,
*'''Residual Energy''', the current residual energy,
*'''Average Energy''', the average residual energy in its local group, and
*'''Previous-hop Node''', the ID or address of the previous hop.

The last field '''Next Forwarder''' indicates that the ID of the node eligible to be the next forwarder.

== 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 ==
The simulation tool is originally developed on [http://www.isi.edu/nsnam/ns/ NS2], and is currently being moved to [http://www.omnetpp.org OMNet++].

=== Downloads ===
The simulation tool of QELAR is built based on
*[http://www.omnetpp.org OMNet++ 4.1]: [http://laurel.engr.uconn.edu/~tiansi/tools/omnetpp-4.1-src.tgz omnetpp-4.1-src.tgz]
*[http://mixim.sourceforge.net/ MiXiM 2.0]: [http://laurel.engr.uconn.edu/~tiansi/tools/MiXiM-2.0.tar.gz MiXiM-2.0.tar.gz]

The QELAR tool is currently being moving to the platform of OMNet++. It is coming soon.

=== Installation Guide ===
Will be provided upon release.

=== User Guide ===
Will be provided upon release.

File:Pf.jpg

2011-05-24T15:18:09Z

Tiansi:

QELAR

2011-05-24T15:17:56Z

Tiansi: /* Packet Format */

== 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-hop 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 ==
[[Image: rl.jpg|thumb|300px]]
=== 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 <math>a</math> in a state <math>s</math> as <math>Q(s,a)</math>, and the direct reward of taking such an action as <math>r(s,a)</math>. The optimal <math>Q(s,a)</math> can be approximated by the following iteration:
<math>
Q(s,a)\leftarrow(1-\alpha)Q(s,a)+\alpha\left[r(s,a)+\gamma\max_aQ(s',a)\right],
</math>

where <math>s'</math> is the next state, <math>\alpha</math> and <math>\gamma</math> 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 <math>-g</math>.
*'''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 <math>n</math> is defined as
<math>
c(n)=\frac{E_{res}(n)}{E_{init}(n)}
</math>
*'''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 <math>n</math> that holds a packet and all its direct neighbors in the transmission range. It is defined as
<math>
d(n)=\frac{2}{\pi}\arctan(E_{res}(n)-\overline{E}(n)).
</math>
where <math>\overline{E}(n)</math> is the average residual energy in Node <math>n</math>'s group.

In summary, the reward that Node <math>n</math> forwards a packet to Node <math>m</math> is
<math>
r(n,a,m)=-g-\alpha_1\left[c(n)+c(m)\right]+\alpha_2\left[d(n)+d(m)\right],
</math>
where <math>\alpha_1</math> and <math>\alpha_2</math> are the constant weights that can be adjusted.

=== Packet Format ===
[[Image: pf.jpg|thumb|300px]]

== 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 ==
The simulation tool is originally developed on [http://www.isi.edu/nsnam/ns/ NS2], and is currently being moved to [http://www.omnetpp.org OMNet++].

=== Downloads ===
The simulation tool of QELAR is built based on
*[http://www.omnetpp.org OMNet++ 4.1]: [http://laurel.engr.uconn.edu/~tiansi/tools/omnetpp-4.1-src.tgz omnetpp-4.1-src.tgz]
*[http://mixim.sourceforge.net/ MiXiM 2.0]: [http://laurel.engr.uconn.edu/~tiansi/tools/MiXiM-2.0.tar.gz MiXiM-2.0.tar.gz]

The QELAR tool is currently being moving to the platform of OMNet++. It is coming soon.

=== Installation Guide ===
Will be provided upon release.

=== User Guide ===
Will be provided upon release.

QELAR

2011-05-24T15:09:31Z

Tiansi: /* Design */

== 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-hop 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 ==
[[Image: rl.jpg|thumb|300px]]
=== 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 <math>a</math> in a state <math>s</math> as <math>Q(s,a)</math>, and the direct reward of taking such an action as <math>r(s,a)</math>. The optimal <math>Q(s,a)</math> can be approximated by the following iteration:
<math>
Q(s,a)\leftarrow(1-\alpha)Q(s,a)+\alpha\left[r(s,a)+\gamma\max_aQ(s',a)\right],
</math>

where <math>s'</math> is the next state, <math>\alpha</math> and <math>\gamma</math> 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 <math>-g</math>.
*'''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 <math>n</math> is defined as
<math>
c(n)=\frac{E_{res}(n)}{E_{init}(n)}
</math>
*'''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 <math>n</math> that holds a packet and all its direct neighbors in the transmission range. It is defined as
<math>
d(n)=\frac{2}{\pi}\arctan(E_{res}(n)-\overline{E}(n)).
</math>
where <math>\overline{E}(n)</math> is the average residual energy in Node <math>n</math>'s group.

In summary, the reward that Node <math>n</math> forwards a packet to Node <math>m</math> is
<math>
r(n,a,m)=-g-\alpha_1\left[c(n)+c(m)\right]+\alpha_2\left[d(n)+d(m)\right],
</math>
where <math>\alpha_1</math> and <math>\alpha_2</math> are the constant weights that can be adjusted.

=== Packet Format ===

== 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 ==
The simulation tool is originally developed on [http://www.isi.edu/nsnam/ns/ NS2], and is currently being moved to [http://www.omnetpp.org OMNet++].

=== Downloads ===
The simulation tool of QELAR is built based on
*[http://www.omnetpp.org OMNet++ 4.1]: [http://laurel.engr.uconn.edu/~tiansi/tools/omnetpp-4.1-src.tgz omnetpp-4.1-src.tgz]
*[http://mixim.sourceforge.net/ MiXiM 2.0]: [http://laurel.engr.uconn.edu/~tiansi/tools/MiXiM-2.0.tar.gz MiXiM-2.0.tar.gz]

The QELAR tool is currently being moving to the platform of OMNet++. It is coming soon.

=== Installation Guide ===
Will be provided upon release.

=== User Guide ===
Will be provided upon release.