Low Latency Communication of Large Data Objects by Subscriber-centric Selective Data Transfer

Autonomous cyber-physical systems depend on high-resolution sensors that generate multi-gigabyte-per-second data streams for accurate environmental perception and system safety. However, existing publish-subscribe middleware frameworks, such as ROS 2 with DDS, are optimized for small data objects and face significant latency and overhead challenges when handling large sensor data between distributed application nodes. Recognizing the need for more efficient data management, we propose a new companion middleware that prioritizes application-specific data relevance, enabling selective communication of critical information while reducing the burden on communication resources and maintaining interoperability with state-of-the-art publish-subscribe middleware. This companion middleware enables timely and effective sharing of sensor data by focusing on regions of interest relevant to specific tasks, such as traffic light detection in driving scenarios. Experimental evaluations of our open-source implementation of this companion middleware on a Linux platform demonstrate that our protocol integrates efficiently with ROS 2, significantly enhancing data management and communication efficiency.