A Probabilistic Query Routing Scheme for Wireless Sensor Networks

The use of wireless sensor networks for information discovery and monitoring of continuous physical fields has emerged as a novel and efficient solution. To this end, a query message is routed through the network to fetch data from sensor nodes and report it back to a sink node. As several applications only require a limited subset of the available data in the network, this query could be ideally routed to fetch only relevant data. In this way, much energy due to message exchange among nodes could be saved. In this paper, we consider the application of computational intelligence on nodes to implement a parallel adaptive simulated annealing (PASA) mechanism able to direct queries to relevant nodes. Besides, a reinforcement learning algorithm is proposed to adapt progressively the query process to the characteristics of the network, limiting the routing space to areas with useful data. Finally, the relevant data collection mechanism is also discussed to illustrate the complete process. We show by extensive simulations that the routing cost can be reduced by approximately 60% over flooding with an error less than 5%.