Model-matching driven energy-saving filtering mechanism in gathering-oriented WSN
Ru Huang, Zaichen Zhang, Jie Zhu · Journal of Circuits and Systems · 2010
The paper addresses a model-matching filtering mechanism (MMF) for driving energy-saving periodic data-gathering in wireless sensor networks (WSN) via mining the statistical-characteristic of flow with imcomplete information at source. Distinguish from traditional lossless-fusion technologies adopted in the process of homogeneous-data transmission on the aspects of action-phases and fault-tolerability in gathering mechanism, our novel schemes focus on designing heterogeneous-data gathering mechanism at traffic source to further reduce total energy cost and transmission delay by using loss-fusion technology and the application-oriented trait of WSN. The whole operation processes of MMF are composed by two main stages. In the basic data-gathering stage, semi-supervised learning method is adopted to estimate parameters of mixed GMM, which is applied to describe the statistical distribution characteristics of data block. Furthermore, model-matching driven filtering operation on traffic could be executed by adaptively controlling the frequency of communication operations and filtering the redundant loads in adaptive filtering stage. Simulation results show that MMF can achieve energy-saving and robust data-gathering on the premise of QoS requirement by extracting redundancy-attributes on heterogeneous flow data and driving corresponding model-matching filtering operation.