Abstract
This paper intends to reduce the communication cost, while ensuring data prediction accuracy and data transmission efficiency in wireless sensor networks (WSNs). A data fusion scheme using clustering and prediction algorithms is proposed. Initially, nodes are clustered by using historical data, and then they are linked based on the actual geographical distance. Next, during the data fusion process, the base station and sensor nodes both use the online recurrent extreme learning machine (OR-ELM) to predict the future sensing data, which can guarantee that the data sequence in the base station and sensor nodes are synchronous. If the prediction fails, data will be transmitted to other nodes in the link and forwarded. Finally, experimental results reveal that the proposed data fusion scheme not only can effectively predict the sensor data, but also can reduce spatial and temporal redundant transmissions with low computational cost.
Hongyu Li, M.S., Chongqing University of Posts and Telecommunications. His current main research interest includes: wireless sensor networks and extreme learning machine.
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Yu, X., Li, H., Gan, C., Zhang, Z. (2019). A Data Fusion Scheme for Wireless Sensor Networks Using Clustering and Prediction. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_43
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DOI: https://doi.org/10.1007/978-981-13-6264-4_43
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