A two-tier data dissemination model for large-scale wireless sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
A survey of middleware for sensor networks: state-of-the-art and future directions
Proceedings of the international workshop on Middleware for sensor networks
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
SNQL: a query language for sensor network databases
TELE-INFO'08 Proceedings of the 7th WSEAS International Conference on Telecommunications and Informatics
Event-Based Location Dependent Data Services in Mobile WSNs
RTCSA '09 Proceedings of the 2009 15th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
AFIN '10 Proceedings of the 2010 Second International Conference on Advances in Future Internet
Programming wireless sensor networks: Fundamental concepts and state of the art
ACM Computing Surveys (CSUR)
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
IEEE Communications Magazine
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In the sensor database management, a query is disseminated from the base-station to the sensor nodes in a sensor network to collect various types of ambient data requested by applications. Sensor database systems typically support event-driven queries to be executed when some events are detected at some sensor nodes. In this paper, we propose a location-aware, event-driven query processing scheme using an in-network query processing algorithm as an extension to our previous work on Sensor Network Query Language (SNQL). The proposed scheme provides a couple of location-aware expressions for selective dissemination and in-network propagation of a query. We also propose a spatial metadata management scheme by using Quadtree. Our evaluation shows that energy consumption of our event-driven query processing is reduced by 45% and response time is faster by up to 50% compared to the existing systems.