Wireless sensor networks consist of a large number of sensor nodes having limited power. The most important feature of these networks is the presence of dynamic topology which will lead to the mobility of the nodes. This mobility requires a routing capable of adapting to these changes. Despite the power restriction in these networks, the purpose in routing algorithm is not to find the shortest route; rather it is the power of each node which constitutes one of the most important issues. In this article, we have used soft computing techniques for routing in the network. The results obtained show that the combined method based on soft computing as compared to previous methods some what improves the minimalization of used power.
Key words: Fuzzy logic, multi agent system, Q learning, reinforcement learning, wireless sensor network.
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