A Hybrid Model for Data Prediction in Real-World Wireless Sensor Networks

作者：Xu, Xiaobin; Zhang, Guangwei^*

来源：IEEE Communications Letters, 2021, 25(5): 1712-1715.

DOI：10.1109/LCOMM.2017.2706258

• Training
• Data models
• Predictive models
• Wireless sensor networks
• Delays
• Prediction algorithms
• Computational modeling
• energy efficiency
• data prediction
• transmission suppression
• linear model

Data prediction is proposed in wireless sensor networks (WSNs) to extend system lifetime by avoiding transmissions of redundant messages. Existing prediction-based approaches can be classified into two types. One focuses on historical data reconstruction and proposes backward models, which incur uncontrollable delay. The other focuses on the future data prediction and proposes forward models, which require additional transmissions. This letter proposes a hybrid model with the capabilities of both historical data reconstruction and future data prediction to avoid additional transmission and control delay. Two algorithms are proposed to implement this model in real-world WSNs. One is a stagewise algorithm for sensor nodes to build optimal models. The other is for the sink to reconstruct and predict sensed values. Two WSN applications are simulated based on three real data sets to evaluate the performances of the hybrid model. Simulation results demonstrate that the proposed approach has high performance in terms of energy efficiency with controllable delay.

• 出版日期2021-5
• 单位北京邮电大学; 北京工业大学