On maximum clique problems in very large graphs
External memory algorithms
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
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Proceedings of the 3rd international conference on Embedded networked sensor systems
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ACM Transactions on Sensor Networks (TOSN)
Modeling spatially correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
IEEE Transactions on Parallel and Distributed Systems
Time synchronization methods for wireless sensor networks: A survey
Programming and Computing Software
A survey on clustering algorithms for wireless sensor networks
Computer Communications
ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Query Processing in Sensor Networks
IEEE Pervasive Computing
Isolines: efficient spatio-temporal data aggregation in sensor networks
Wireless Communications & Mobile Computing - Distributed Systems of Sensors and Actuators
Prediction-based energy map for wireless sensor networks
Ad Hoc Networks
EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations
Journal of Systems and Software
Predictive modeling-based data collection in wireless sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Generic Information Transport for Wireless Sensor Networks
SUTC '10 Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
Reordering for Better Compressibility: Efficient Spatial Sampling in Wireless Sensor Networks
SUTC '10 Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
IEEE Transactions on Parallel and Distributed Systems
Model-Aided data collecting for wireless sensor networks
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
IEEE Journal on Selected Areas in Communications
Hi-index | 0.00 |
Wireless Sensor Networks (WSN) are often deployed to sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data volumes. Sensor nodes are battery powered and sending the requested large amount of data rapidly depletes their energy. Fortunately, the environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific measurement and forensics tolerate high latencies for sensor data collection. Accordingly, we develop a fully distributed adaptive technique for spatial and temporal in-network data compression with accuracy guarantees. We exploit the spatio-temporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to further minimize the amount of data to be transported to the sink. Using real data, we demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data. In our simulations, we achieved data compression of up to 95% on the raw data requiring around 5% of the original data to be transported to the sink.