Progressive Content-Sensitive Data Retrieval in Sensor Networks

Problem statement: For a sensor network comprising autonomous and self-organizing data sources, efficient similarity-based search for semantic-rich resources (such as video data) has been considered as a challenging task due to the lack of infrastructures and the multiple limitations (such as band-width, storage and energy). While the past research discussed much on routing protocols for sensor networks, few works have been reported on effective data retrieval with respect to optimized data search cost and fairness across various environment setups. This study presented the design of progressive content prediction approaches to facilitate efficient similarity-based search in sensor networks. Approach: The study proposed fully dynamic, hierarchy-free and non-flooding approaches. Association rules and Bayesian probabilities were generated to indicate the content distribution in the sensor network. The proposed algorithms generated the interest node set for a node based on its query history and the association rules and Bayesian rule. Because in most cases the data content of a node was semantically related with its interest of queries, the sensor network was therefore partitioned into small groups of common interest nodes and most of the queries can be resolved within these groups. Consequently, blind search approach based on flooding could be replaced by the heuristic-based uni-casting or multicasting schemes, which drastically reduced the system cost of storage space, network bandwidth and computation power. Results: We verified the performance with experimental analysis. The simulation result showed that both Bayesian scheme and association scheme require much less message complexity than flooding, which drastically reduced the consumption of system resources. Conclusion: Content distribution knowledge could be used to improve the system performance of content-based data retrieval in sensor networks.