Principal component analysis based data collection for sustainable internet of things enabled Cyber–Physical Systems

The Internet of Things (IoT) enabled Cyber–Physical System (CPS) is a promising technology applying in smart home, industrial manufacturing, intelligent transportation, etc. The IoT enabled CPS consists of two main components, i.e., IoT devices and cybers, which interact with each other. The IoT devices collect sensory data from physical environments and transmit them to the cybers, and the cybers make decisions to respond to the collected data and issue commands to control the IoT devices. It is generally known that energy is an important but limited resource in IoT devices. Data compression is an efficient way to reduce the energy consumption of data collection in sustainable IoT enabled CPSs, especially the Principal Component Analysis (PCA) based data compression. The trade-off between data compression ratio and data reconstruction error is one of the biggest challenges for PCA based data compression. In this paper, we investigate PCA based data compression to maximize the compression ratio with bounded reconstruction error for data collection in IoT enabled CPSs. Firstly, a similarity based clustering algorithm is proposed to cluster IoT devices in an IoT enabled CPS. Then, a PCA based data compression algorithm is proposed to compress the collected data to the greatest extent in each cluster with a bounded reconstruction error. Extensive simulations are conducted to verify the efficiency and effectiveness of the proposed algorithms.