A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

By Xiong Luo, Dandan Zhang, Laurence Tianruo Yang, Ji Liu, Xiaohui Chang, Huansheng Ning. 2016

103 citations recorded by the source corpus.

Wireless sensor networks (WSNs) as one of the key technologies for delivering sensor-related data drive the progress of cyber-physical systems (CPSs) in bridging the gap between the cyber world and the physical world. It is thus desirable to explore how to utilize intelligence properly by developing the effective scheme in WSN to support data sensing and fusion of CPS. This paper intends to serve this purpose by proposing a prediction-based data sensing and fusion scheme to reduce the data transmission and maintain the required coverage level of sensors in WSN while guaranteeing the data confidentiality. The proposed scheme is called GM-KRLS, which is featured through the use of grey model (GM), kernel recursive least squares (KRLS), and Blowfish algorithm (BA). During the data sensing and fusion process, GM is responsible for initially predicting the data of next period with a small number of data items, while KRLS is used to make the initial predicted value approximate its true value with high accuracy. The KRLS as an improved kernel machine learning algorithm can adaptively adjust the coefficients with every input, while making the predicted value more close to actual value. And BA is used for data encoding and decoding during the transmission process due to its successful applications across a wide range of domains. Then, the proposed secure data sensing and fusion scheme GM-KRLS can provide high prediction accuracy, low communication, good scalability, and confidentiality. In order to verify the effectiveness and reasonableness of our proposed approach, we conduct simulations on actual data sets that are collected from sensors in the Intel Berkeley research lab. The simulation results have shown that the proposed scheme can significantly reduce redundant transmissions with high prediction accuracy. A novel data sensing and fusion scheme GM-KRLS is proposed in WSNs for the CPSs.GM-KRLS develops a prediction mechanism to reduce redundant transmissions in WSN.GM-KRLS improves the prediction accuracy with a kernel machine learning algorithm.Blowfish algorithm is employed to guarantee the confidentiality in our scheme.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “As an emerging technology, it is expected to connect all items (such as, physical devices [2, 3], vehicles [4], home appliances [5-7], etc.”
A survey of data fusion in smart city applications (2019, background): “Kreibich et al. [47] and Luo et al. [48] have proposed approaches to improve communication between WSN focusing on the communication mechanism between nodes.”