Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Efficient Data Harvesting for Tracing Phenomena in Sensor Networks
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Localized coverage boundary detection for wireless sensor networks
QShine '06 Proceedings of the 3rd international conference on Quality of service in heterogeneous wired/wireless networks
A survey on clustering algorithms for wireless sensor networks
Computer Communications
Coverage problems in wireless sensor networks: designs and analysis
International Journal of Sensor Networks
Energy-Efficient Tracking of Continuous Objects in Wireless Sensor Networks
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Observing Walking Behavior of Humans Using Distributed Phenomenon Detection and Tracking Mechanisms
SAINT '08 Proceedings of the 2008 International Symposium on Applications and the Internet
Tracking dynamic boundary fronts using range sensors
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Roadmap query for sensor network assisted navigation in dynamic environments
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Secure and Fault-Tolerant Event Boundary Detection in Wireless Sensor Networks
IEEE Transactions on Wireless Communications
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Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient. In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes. We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.