A Kernel Machine-Based Secure Data Sensing and Fusion Scheme in Wireless Sensor Networks for the Cyber-Physical Systems
Xiong Luo, Dandan Zhang, Laurence Tianruo Yang, Ji yin Liu, Xiaohui Chang, Huansheng Ning · 2019
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 effective WSN schemes to support data sensing and fusion of CPS. This chapter intends to serve this purpose by proposing a prediction-based data sensing and fusion scheme to reduce data transmission and maintain the required coverage level of sensors in WSN while guaranteeing data confidentiality. The proposed scheme is called GM-KRLS, which features 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 the 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 closer to the 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 conducted simulations on actual data sets 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.