A framework for enabling energy efficient semantic views in wireless sensor networks for data intensive applications

Sensor networks have been envisioned to be a promising technique for data intensive applications such as disaster management and emergency response and are being designed and deployed for these applications [1]. The effectiveness of sensor networks in providing information is determined by human's capacity to recognize and comprehend information from the raw data collected, and act accordingly. Finding relevant information from the large amount of data, however, becomes a challenging problem because user interests continues to grow as the number and variety of sensors increase and users expect to receive only the data they select to view. Transmitting users irrelevant data during data processing not only overloads users with unneeded data but also incurs unnecessary communication overhead. Furthermore, the user interests may be correlated when a large number of users seek information from sensor networks. As a result, a lot of redundant data transmission can be incurred during processing in resource-constrained sensor networks. Data aggregation, though effective in reducing data transmission for aggregated queries, doesn't take the correlation among user interests into consideration during processing. Therefore, additional techniques need to be proposed to provide efficient information delivery for correlated user interests in resource-constrained sensor networks.;To bridge the gap between data collected by sensors and the information interests of users, the concept of "semantic view" is proposed in this thesis. The semantic view is a powerful abstraction which allows the fusion of multi-sensor and multi-source data into a virtual data gathering and analysis infrastructure commensurate with the interest of an end user. The main challenge is to enable semantic views in an energy efficient manner in resource constrained sensor networks. To that end, a framework which consists of five protocols and algorithms, "Query Aware Sensing", "Probabilistic Query Dissemination", "Correlated Multi-query Processing", "Location Discovery using Out-of-Range information with multi-lateration", and "End-to-end pairwise key establishment" is presented. The ultimate goal is to develop an energy efficient and secure framework towards enabling semantic views in sensor networks for data intensive applications.