The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
TiNA: a scheme for temporal coherency-aware in-network aggregation
Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
The design and evaluation of a query processing architecture for sensor networks
The design and evaluation of a query processing architecture for sensor networks
IEEE Communications Magazine
Computer Networks: The International Journal of Computer and Telecommunications Networking
A survey of communication/networking in Smart Grids
Future Generation Computer Systems
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Nodes in wireless sensor networks have very limited storage capacity, computing ability and battery power. Node failure and communication link disconnection occur frequently, which means weak services of the network layer. Sensed data is inaccurate which often has errors. Focusing on inaccuracy of the observed data and power limitation of sensors, this paper proposes a sampling frequency control algorithm and a data compression algorithm. Based on features of the sensed data, these two algorithms are combined together. First, it adjusts the sampling frequency on sensed data dynamically. When the sampling frequency cannot be controlled, data compression algorithm is adopted to reduce the amount of transmitted data to save energy of sensors. Based on the compressed data, we also propose an approximate query processing algorithm, which reduces query processing time dramatically. Experiments and analysis show that the proposed algorithms can decrease sampling times reduce the amount of transmitted data, save energy of sensors and improve the query efficiency.