Abstract
The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.
Highlights
The Internet of Thing (IoT) has developed rapidly in recent years, in which the wireless sensor network is becoming popular with low energy consumption, multifunction and large-scale deployment by sensing, collecting, processing, and transmitting the sensory data through cooperation between nodes [1, 2]
5 Method This section describes the features learning process of prediction model based on the two-directional long short-term memory (LSTM) neural network which is named as multi-node multifeature (MNMF) prediction model in this paper
7 Conclusion The sensory data in the wireless sensor network is collected by multiple sensors of different nodes, which shows the relative variation of several environmental factors in different regions
Summary
The Internet of Thing (IoT) has developed rapidly in recent years, in which the wireless sensor network is becoming popular with low energy consumption, multifunction and large-scale deployment by sensing, collecting, processing, and transmitting the sensory data through cooperation between nodes [1, 2]. The number of data transmission between common nodes and sink nodes will increase significantly together with network size explosion, which possibly leads to data congestion, and high loss rate of sensory data and low signalnoise ratio [3,4,5]. Using data prediction methods to reduce unnecessary data transmission is an effective way to improve the quality of data collection and increase the network lifetime. The current methods usually use the periodicity and redundancy to predict the specific sensory data based on historical data, which often results in low prediction stability and biased predictions [6,7,8,9,10,11]. Data correlation among the sensory data is helpful to recover the lost data. The temporal correlation can be observed in case that the physical
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