PDDS: Scalable Sensor Data Distribution for Cyber-Physical Systems Using GPGPUs

Abstract

In many cyber-physical systems (CPS), one of the primary challenges is how to disseminate many sensor streams to collaborating entities in a timely, reliable, and scalable manner. However, in CPS, communication and related computation is often unstable and constitutes a serial bottleneck when multiple sensor streams need to be distributed. In this paper, we propose a novel publish-subscribe middleware architecture, called parallel data distribution service (PDDS), to address this problem. To bypass this serial bottleneck effectively, PDDS exploits state-space models for sensor streams both at a publisher and its subscribers. These models are used to estimate true states of monitored physical processes without actual communication. This approach not only reduces the communication overhead significantly, but also provides the tolerance against potential delays and instability in communication. Further, with the model-based methods, many sensor streams can be accessed in parallel since sensor streams can bypass a serial bottleneck, such as network stacks. At the middleware layer, PDDS processes the model-related computation, such as the state estimation, in parallel using modern embedded general-purpose computing on graphics processing units, enabling highly scalable and energy-efficient processing of massive sensor streams. The proposed approach is implemented in a data distribution service middleware using CUDA. Our evaluation results show that the proposed approach can achieve significant energy savings and much higher scalability compared to baseline approaches.