Timely analysis of real-time sensor data streams is essential to key applications in the Internet of Things (IoT), such as smart health, transportation, and energy. Although advanced stream processing engines (SPEs), such as Apache Storm, Flink, and Spark Streaming, provide powerful stream processing frameworks in a cloud, sending sensor data to the SPE for analysis over the wide area network may incur many deadline misses and create bottlenecks in the core Internet. A viable alternative is real-time sensor data stream processing in edge devices; however, it is challenging to support timing constraints using limited resources available in such devices. Real-time scheduling theory is not directly applicable, since it is agnostic to data semantics and usually based on worst-case assumptions for predictability that would be too pessimistic and resource inefficient in edge devices. The problem is becoming increasingly serious as the number of IoT devices and data volume increases rapidly. The proposed work aims to bridge the widening gap by investigating cost-efficient approaches for soft real-time stream processing at the edge. This project explores novel approaches to scheduling, sensor stream processing, and load sharing to significantly decrease deadline misses and communicational as well as computational resource consumptions, while enhancing the reliability of real-time stream processing.
The research is expected to provide an enabling technology for important IoT applications with great societal impacts, such as those in healthcare, transportation, and energy that produce immense real-time sensor data streams, by substantially improving the timeliness and reliability of real-time stream processing with less resource consumptions compared to state-of-the-art SPEs. The investigator will use select research results to continue education and outreach efforts that include broadly disseminating publications and code that will be produced by this project, developing new courses and teaching materials on real-time stream processing, recruiting underrepresented groups of students to work on the project, and encouraging the younger generation to study computer science and pursue careers in industry and academia.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
