Editorial for special issue on Big Data and knowledge extraction for Cyber Physical Systems 2016
The cyber physical system (CPS) has been coming into our view and will be applied in our daily life and business process management. The emerging CPS must be robust and responsive for its implementation in coordinated, distributed, and connected ways. It is expected that future CPS will far exceed today's systems on a variety of characteristics, for example, capability, adaptability, resiliency, safety, security, and usability. Big Data are large, complex, or rapidly generated data sets that cannot be processed by traditional technologies. Every day, the entities comprising decision makers, managers, engineers, scientists, and citizens are faced with a multitude of constantly flowing data streams coming from different sources in different formats. Making sense of these volumes of Big Data requires cutting edge tools that can analyze and extract useful knowledge from vast and diverse data streams. The wonderful living of humans and high efficiency of business rely mostly on how to use the Big Data intelligently and correctly and how to retrieve useful knowledge from the massive data then, it would be possible to seamlessly integrate the virtual world and the physical world. How to integrate and analyze the data? How to retrieve knowledge from Big Data? How to share knowledge among smart things? How to ensure security and protect privacy? These are some of the questions in the long list of challenges that are needed to be addressed in future CPS. This Special Issue solicits high quality contributions with consolidated and thoroughly evaluated research in the area of Big Data and knowledge extraction for CPS that are worthy of archival publication in the journal. It is intended to (1) provide a summary of research that advances knowledge acquisition and utilization from Big Data for CPS and (2) serve as a comprehensive collection of the current state of the art technologies within the context. Due to the great effort made in publicity, we received 37 submissions from both academia and industry in the relevant fields. Following a strict review process, we accepted 22 papers for this Special Issue. Each of the papers was peer reviewed by at least two experts in the field. In the following, we provide a brief introduction to each paper. The paper 'Parallel Irregular Fusion Estimation Based on Nonlinear Filter for Indoor RFID Tracking System, authored by Xue Bo Jin, Chao Dou, Ting li Su, Xiao fen Lian, and Yan shi, proposes a real time indoor RFID tracking system which employs an irregular estimation strategy with parallel structure, where the dynamic model update and state fusion estimation are synchronously processed. Based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) two nonlinear estimation methods are designed. The tracking performance is evaluated, and the simulation results show that the UKF method achieves lower covariance in indoor RFID tracking, while the EKF one has less calculation time. To efficiently track multi level packages, the paper 'Multi level Package Identification Scheme Based on RFID Code and Related Package Message' by Yanghua Gao, Zhihua Zhang, Huanwen Wang, and Hailiang Lu proposes an RFID code scheme. With the investigation of the relation among multi level packages, the subordinate relation of different level RFID codes can be established by recording and storing the inclusion relation between codes in database. Furthermore, to verify the feasibility of the proposed code scheme, it is applied to the fast moving consumer goods management which involves three level packages. In the paper 'On Energy balanced Backpressure Routing Mechanisms for Stochastic Energy Harvesting Wireless Sensor Networks' by Zheng Liu, Xinyu Yang, Peng Zhao, and Wei Yu, the unpredictablility of the harvestable energy is considered, and the stochastic Lyapunov optimization framework is employed to jointly manage energy and make routing decisions. The purpose is to mitigate the energy imbalance problem. Two distributed online policies are developed, which are as follows: (1) Energy balanced Backpressure Routing Algorithm (EBRA) for lossless networks and (2) Enhanced Energy balanced Backpressure Routing Algorithm (EEBRA) for time varying wireless
Int. J. Distributed Sens. Networks