Self-stabilization
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
An energy-efficient querying framework in sensor networks for detecting node similarities
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
IEEE Transactions on Parallel and Distributed Systems
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
ACM Transactions on Sensor Networks (TOSN)
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Constructing efficient rotating backbones in wireless sensor networks using graph coloring
Computer Communications
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Wireless sensor networks are often densely deployed for environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. Thus using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation. This reduces the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80% compared to raw data collection.