Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
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 Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Light-weight Online Predictive Data Aggregation for Wireless Sensor Networks
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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Data aggregation is a current hot research area in sensor netwworks. Aiming at the time series data in sensor networks, we present GMSVM (Grey Model Support Vector Machines), a novel prediction model data aggregation of sensor networks. In this model, grey model (GM) prediction theory is introduced into support vector machines (SVM). And the RBF kernel function is improved by Riemannian geometry analysis and the experimental data series, which can raise the arithmetic speed. The model is validated with fuel pressure data of injector. The results show that the model can execute dynamic multistep prediction, and it has high precision prediction and flexibility. Thus, it can observably reduce the number of transmissions in sensor networks and save energy. Besides, it also has better performance in latency and computation. Comparing with other prediction algorithms, GMSVM is more effective for senor networks, so it has a good foreground to improve the prediction performance of data aggregation.