论文部分内容阅读
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 redundancy 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. 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 by. 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.