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
IEEE Transactions on Parallel and Distributed Systems
ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
PRESTO: feedback-driven data management in sensor networks
IEEE/ACM Transactions on Networking (TON)
EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations
Journal of Systems and Software
An unequal cluster-based routing protocol in wireless sensor networks
Wireless Networks
IEEE Transactions on Parallel and Distributed Systems
Distributed spatial clustering in sensor networks
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Research on the energy hole problem based on unequal cluster-radius for wireless sensor networks
Computer Communications
Energy-aware adaptive cooperative FEC protocol in MIMO channel for wireless sensor networks
Journal of Electrical and Computer Engineering
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In wireless sensor network, sensor readings generated by nearby nodes are redundant and highly correlated, both in space and time domains. Since transmitting redundant and highly correlated data incurs a huge waste of energy and bandwidth, spatial and temporal correlation should be exploited in order to reduce redundant data transmission. In this paper, we propose an energy efficient data gathering protocol that uses a prediction-based filtering EEDGPF mechanism to solve the problem of redundant data transmissions. Our data gathering protocol organises a WSN into clusters, using data similarity that exists in readings of sensor nodes and cluster heads and uses a GARCH 1, 1 model-based non-linear predictor to exploit the temporal correlation of sensor readings. Experimental results over real dataset show that our protocol significantly outperforms linear predictor AR3-based protocol proposed in Jiang et al. 2011, in terms of number of data packets delivered, number of successful predictions and average energy consumption.