Networking infrastructure and data management for large-scale cyber-physical systems
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A cyber-physical system (CPS) is a system featuring a tight combination of, and coordination between, the system’s computational and physical elements. A large-scale CPS usually consists of several subsystems which are formed by networked sensors and actuators, and deployed in different locations. These subsystems interact with the physical world and execute specific monitoring and control functions. How to organize the sensors and actuators inside each subsystem and interconnect these physically separated subsystems together to achieve secure, reliable and real-time communication is a big challenge. In this thesis, we first present a TDMA-based low-power and secure real-time wireless protocol. This protocol can serve as an ideal communication infrastructure for CPS subsystems which require flexible topology control, secure and reliable communication and adjustable real-time service support. We then describe the network management techniques designed for ensuring the reliable routing and real-time services inside the subsystems and data management techniques for maintaining the quality of the sampled data from the physical world. To evaluate these proposed techniques, we built a prototype system and deployed it in different environments for performance measurement. We also present a light-weighted and scalable solution for interconnecting heterogeneous CPS subsystems together through a slim IP adaptation layer and a constrained application protocol layer. This approach makes the underlying connectivity technologies transparent to the application developers thus enables rapid application development and efficient migration among different CPS platforms. At the end of this thesis, we present a semi-autonomous robotic system called cyberphysical avatar. The cyberphysical avatar is built based on our proposed network infrastructure and data management techniques. By integrating recent advance in body-compliant control in robotics, and neuroevolution in machine learning, the cyberphysical avatar can adjust to an unstructured environment and perform physical tasks subject to critical timing constraints while under human supervision.