Capacity of Opportunistic Routing and Routing Mechanism of Correlated Source Over a Network
Date Issued
2011
Date
2011
Author(s)
Tseng, Fan-Min
Abstract
In wireless network, the broadcast feature makes the transmission link unreliable in certain network, like cognitive radio network or wireless mesh network. The opportunistic routing is therefore been proposed to solve the problem. In opportunistic routing, the end-to-end transmission path is not pre-determined. Every node would be potential forwarder. In this routing mechanism, we conclude that the capacity is bound in the situation that whether the forwarders to destination have received an un-transmitted packet so that it can transmit to destination. To solve this problem we use the concept of network coding that we find the min-cut of network from source to those forwarders. Our analytical result shows that the capacity is related to the total transmission links within T time slots, and the capacity will also increase with the number of transmission paths.
The second part of our work does the multi-sources, single destination network while the sources are correlated, which is most likely the wireless sensor network. We use the overhearing information to improve traffic efficiency, i.e., every node can do data compression by the transmitting information message that it overhears from its neighboring nodes. We let each sensor choose its forwarder, and also do efficient transmission by the overhearing message of its possible forwarders. Our goal is to minimize the total transmitting information message from every source nodes and relay nodes. To obtain a distributed routing algorithm, we use the Gibbs sampler which theoretically can approach to global optimal by local information. We compare our algorithm with greedy approach, and the simulation result shows that our algorithm improves the total transmitting information message significantly. We also find that the improvement is related to how correlated of sources.
Subjects
opportunistic routing
network coding
correlated source
Gibbs sampler
Type
thesis
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