Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Lower Bounds for Approximation Algorithms for the Steiner Tree Problem
WG '01 Proceedings of the 27th International Workshop on Graph-Theoretic Concepts in Computer Science
An environmental energy harvesting framework for sensor networks
Proceedings of the 2003 international symposium on Low power electronics and design
Hole and antihole detection in graphs
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Heuristic algorithms for real-time data aggregation in wireless sensor networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Adaptive Localized QoS-Constrained Data Aggregation and Processing in Distributed Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Dynamic data fusion for future sensor networks
ACM Transactions on Sensor Networks (TOSN)
Secure hierarchical in-network aggregation in sensor networks
Proceedings of the 13th ACM conference on Computer and communications security
Sparse data aggregation in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Structure-Free Data Aggregation in Sensor Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Parallel and Distributed Systems
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Middleware to support sensor network applications
IEEE Network: The Magazine of Global Internetworking
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In-network data aggregation is widely recognized as an acceptable means to reduce the amount of transmitted data without adversely affecting the quality of the results. To date, most aggregation protocols assume that data from localized regions is correlated, thus they tend to identify aggregation points within these regions. Our work, instead, targets systems where the data sources are largely independent, and over time, the sink requests different combinations of data sources. The combinations are essentially aggregation functions. This problem is significantly different from the localized one because the functions are initially known only by the sink, and the data sources to be combined may be located in any part of the network, not necessarily near one another. This paper describes MVSink , a protocol that lowers the network cost by incrementally pushing the aggregation function as close to the sources as possible, aggregating early the raw data. Our results show between 20% and 30% savings over a simplistic approach in large networks, and demonstrate that a data request needs to be active only for a reasonably short period of time to overcome the cost of identifying the aggregation tree.