文献类型：期刊文献

中文题名：Predictive model-aided filtering scheme of data-collection in WSN

英文题名：Predictive model-aided filtering scheme of data-collection in WSN

作者：Huang, Ru[1]; Zhang, Zai-Chen[2]; Xu, Guang-Hui[3]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] State Key Laboratory of Mobile Communications, Southeast University, Nanjing 210096, China; [3] Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China

年份：2011

卷号：18

期号：2

起止页码：17

中文期刊名：The Journal of China Universities of Posts and Telecommunications

外文期刊名：Journal of China Universities of Posts and Telecommunications

收录：EI(收录号：20111913962198);Scopus;CSCD:【CSCD2011_2012】；

基金：supported by the National Natural Science Foundation of China (60802005);the Science Foundation for the Excellent Youth Scholars at East China University of Science and Technology (YH0157127);the Undergraduate Innovational Experimentation Program in ECUST (X1033)

语种：英文

中文关键词：WSN;data-collection;filtering mechanism;energy-saving

外文关键词：Adaptive filters - Adaptive filtering - Data acquisition - Energy efficiency - Energy utilization - Middleware - Quality of service - Trees (mathematics)

摘要：The paper proposes a prediction-mode-based filtering mechanism（PMF） to solve the problems of transmission energy wasting caused by time-redundant data in wireless sensor networks（WSN）,according to the characteristic of spatio-temporal correlations on sampling series in data-collection.Prior works have suggested several approaches to decrease energy cost during data transmission process via data aggregation tree structure.Distinguish from those methods in above researches,our proposed scheme mainly focus on reducing the temporal redundant degree in event-source to achieve energy-saving effect via self-adaptive filtering structure.The framework of PMF for energy-efficient collection is composed of prediction module for mining the change law of time domain,self-learning module for updating model,and driving module for controlling data filtering operation.Combined with the design of error driving rule and threshold distributing rule,which is the middleware in the above filtering mechanism,the quantity of transmission load in networks can be greatly inhibited on the premise of quality of service（QoS） assurance and energy consumption can be reduced consequently.Finally,the experimental results show that the performance of PMF can significantly outperform some classical data-collection algorithms on energy-saving effect and self-adaptability.
The paper proposes a prediction-mode-based filtering mechanism (PMF) to solve the problems of transmission energy wasting caused by time-redundant data in wireless sensor networks (WSN), according to the characteristic of spatio-temporal correlations on sampling series in data-collection. Prior works have suggested several approaches to decrease energy cost during data transmission process via data aggregation tree structure. Distinguish from those methods in above researches, our proposed scheme mainly focus on reducing the temporal redundant degree in event-source to achieve energy-saving effect via self-adaptive filtering structure. The framework of PMF for energy-efficient collection is composed of prediction module for mining the change law of time domain, self-learning module for updating model, and driving module for controlling data filtering operation. Combined with the design of error driving rule and threshold distributing rule, which is the middleware in the above filtering mechanism, the quantity of transmission load in networks can be greatly inhibited on the premise of quality of service (QoS) assurance and energy consumption can be reduced consequently. Finally, the experimental results show that the performance of PMF can significantly outperform some classical data-collection algorithms on energy-saving effect and self-adaptability. ? 2011 The Journal of China Universities of Posts and Telecommunications.