科研之友
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摘要
A complex and vast amount of data will be collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data will create a bottleneck at data centers for processing and retrievals of collected data, and will require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. In this paper, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV applications using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University: the Holocron cluster and the Palmetto cluster. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which prove the efficacy of the system for CV related data distribution and management tasks.
- 出版日期2018-3
