Adaptive multi-flow opportunistic routing using learning automata

Opportunistic routing is a promising routing paradigm that achieves high throughput by utilizing the broadcast nature of wireless media. It is especially useful for wireless mesh networks due to their static topology. In the current opportunistic routing protocols, it is assumed that all nodes have enough incentive and resource to help the source regardless of their load and presence of other network flows. In addition, the effect of each active flow on other flows and network status is reflected latter by means of a link quality metric (e.g. ETX) which is updated periodically. The coarse-grained behavior of the metric is not in harmony with network flows dynamics. Therefore, some flows may undergo performance degradation between two consecutive periodic updates of the metric. Our proposed approach which is called Dynamic Cooperative Routing (DCR) modifies MORE and equips it with an adaptive decision making mechanism. We use learning automata to accommodate network dynamics when building an opportunistic path for a flow. The learning automata are activated whenever the source transmits a new data batch for the flow. We have shown through simulation that DCR outperforms MORE when two or more flows are active simultaneously and in the presence of background unicast traffic.