Probability model-based on-demand query processing for wireless sensor networks database

Wireless sensor networks query processing is useful for sensing systems, such as environmental monitoring. Innetworks query processing is attracted by researchers to develop user-friendly sensing systems. Due to the sensors considered as a limited resource and battery powered devices, more robust features are recommended to limit the communication access to the sensor nodes for maximizing the sensor lifetime. For this reason, a new architecture that combines a probability modelling techniques with dynamic programming query processing to optimize the query communication cost is proposed. In this paper, a dynamic technique to enhance the query engine for the interactive sensing system interface is developed. The probability technique is responsible for reducing communication cost for each query executed outside the wireless sensor networks. The query optimizer generates the lower cost based plan. The experiment is tested on an in-house developed interface with a testbed platform Sensinode cc2430, operates with Contiki operating system. The results show a massive saving of energy in the wireless sensor network according to the architecture specified.