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#Multi-hop Routing Scheme in Decentralized Wireless Networks ODD Protocol
This file describes the agent based model for investigating the energy efficiency of multi-hop routing in a decentralized wireless sensor network.
The model was designed to investigate the energy efficiency of multihop routing protocol for decentralized wireless sensor networks.
The main question was how was the energy efficiency being achieved in networks employing multihop routing?
What parameters gave the optimal energy savings compared to direct transmission?
How is the network lifetime effected by changes of these parameters in the same network?
##Entities, State Variables and Scales
The model has one two entities.
Wireless sensor nodes and Data packets.
The nodes are spread randomly in a 150x150 landscape.
They have the following variables.Energy, Geographical Location, Channel State Estimation.
Data packets have only one variable.
Their size which is constant.
The length of one time step is equivalent to one round of data transmission between a random source and destination node.
##Process Overview and Scheduling
There are three main processes in this model.
The first process involves of two random nodes initializing communication with each other by sending out RTS(Request to Send) and CTS(Clear to Send) packets with maximum transmission energy.
The second process involves the source node investigating all the nodes connected with good link quality with it and selecting the best node in terms of geographical location as the next hop relay.
The last process consists of the source node transmitting to that chosen relay node with corresponding transmission energy.
The main principle that is addressed in this model is the energy savings that is possible for multi-hop routing for peer to peer wireless communication.
The energy that is needed to transmit to a distant node is far more than the energy needed to transmit to a nearby node.
A simple decode and forward communication scheme is followed by this protocol.
The agents (wireless sensor nodes) decides which node to relay to according to their link quality and geographical location.
Thus adaptive behavior is seen among the agents during runtime of the operation.
Optimal network parameters can be calculated by investigating the model in a decentralized network environment.
Stochasticity is employed in this model to model different nodes trying to communicate with each other.
The network is initialized when the model starts.
The nodes are deployed randomly to simulate a decentralized sensor network over a vast area.
The sensor nodes are also initialized in this process.
Initial energy of the nodes and their geographical locations are learnt by the agents.
All the agents establish a good wireless link with the nodes with whom it has good link quality.
The environment remains constant during the simulation.
Therefore, there are no input data for the environment model.
There is one submodel in this model.
When a relay node has no other nodes with good link quality and optimal geographical location to relay to, it chooses the second best node and relays the data to it.
Rafi Mahabbat Bin Belal