History-sensitive based approach to optimizing top-k queries in sensor networks

Sensor networks generate a large amount of data during monitoring process. These data must be sparingly exacted to conserve energy. There are two methods to obtain data: push and pull. When the sensory data satisfied a preset condition, they are pushed towards the base station. The pull method is to actively query the sensor networks for any interesting sensory data. The problem is how to plan the query and save the energy. When a query has been executed, there are some hints that can be kept to optimize the subsequent query processing. Energy consumption can be reduced by not contacting nodes whose values either can be predicted or are unlikely to be used. In this paper, we propose a history-sensitive based method to optimize top-k query processing in sensor networks. The top-k query looks for and utilizes the historical data in each sensor node. Subsequent top-k queries are guided by these historical data, therefore, to improve the entire query process. Simulation results show that the number of query hops can be reduced and the delays in response are improved.