With the burgeoning of Internet of Things (IoT) and 5G technologies, we envision future vehicles will serve as a computing platform for a variety of services like advanced driver assistance system (ADAS), remote diagnostics, on-board entertainment, and a variety third-party services, such as public safety. As the amount of data generated by these services on one vehicle increases dramatically and has the potential to reach four terabytes per day, it is a huge challenge to guarantee performance through the limited on-board computing power. Therefore, an efficient data analytics platform, including both hardware and software, is needed to enable future vehicle computing. Many auto vendors are working on their proprietary platform, however, an open platform that provide interfaces for researchers and developers is missing. In this project, we aim to develop such a platform that provides a full stack solution and dynamically senses services status and adjusts services to guarantee service quality and user experience.
We are developing on an open vehicular data analytics platform (OpenVDAP), which consists of four components: heterogeneous vehicle computing unit (VCU), operation system (EdgeOSv), driving data collector and integrator (DDI), and application library (libvdap).
VCU: heterogeneous vehicle computing unit: We intend to manage and utilize the heterogeneous computing hardware resources in a future vehicle, e.g., CPU, GPU and FPGA, as well as computing devices from passengers, to support real-time data processing. We also aim to manage the multiple communication components, e.g., 3G/4G/5G and DSRC, available in the vehicle.
EdgeOSv: edge operating system for vehicle: The core of OpenVDAP is an edge operating system for vehicles, call EdgeOSv, in which each service offers multiple execution pipelines in response to various network and computational constraints. EdgeOSv employs an elastic management model that automatically chooses an optimal pipeline to reduce the lowest end-to-end latency.
DDI: driving data collector and integrator: We build a driving data collector and integrator. This service will collect all data generated by the vehicle, including the data read by OBD reader and other sensors' data, such as dash camera. Moreover, this service will integrate related data from Internet, such as weather, road condition and so on.
libvdap: application library: We provide a library for developers who want to build third-party services on our EdgeOSv. Using libvdap, developers can access all vehicle data by communicating with DDI service.
- External collaborators
- Jie Tang, Shaoshan Liu, Jie Cao, Dawei Sun, Bolin Ding, Zhe Zhang, Jean-Luc Gaudiot and Weisong Shi, pi-Hub: Large-Scale Video Learning, Storage, and Retrieval on Heterogeneous Hardware Platforms, Technical Reprot MIST-TR-2017-012, November 2017.
- Mohit Kumar, Youhuizi Li and Weisong Shi, Energy Consumption in Java: An Early Experience, in Proceedings of the 8th International Green and Sustainable Computing Conference (IGSC), Orlando, FL, Oct 23-25, 2017.
- Shanhe Yi, Zijiang Hao, Qingyang Zhang, Quan Zhang, Weisong Shi and Qun Li, LAVEA: Latency-aware Video Analytics on Edge Computing Platform, in Proceedings of 2nd ACM/IEEE Symposium on Edge Computing (SEC), San Jose, Oct 12-14, 2017.
- Qingyang Zhang, Quan Zhang, Weisong Shi and Hong Zhong, Poster: Enhancing AMBER Alert using Collaborative Edges, in Proceedings of 2nd ACM/IEEE Symposium on Edge Computing (SEC), San Jose, Oct 12-14, 2017.
- Zhenyu Ning, Fengwei Zhang, Weisong Shi and Larry Shi, Position Paper: Challenges Toward Securing Hardware-assisted Execution Environments, in Proceedings of Hardware and Architectural Support for Security and Privacy (HASP), in conjunction with ISCA, Toronto, Canada, June 25, 2017.
- Quan Zhang, Qingyang Zhang, Weisong Shi and Hong Zhong, Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics, Technical Report MIST-TR-2017-002, January 2017.
- Songqing Chen, Tao Zhang and Weisong Shi, Editorial on Fog Computing, IEEE Internet Computing, Vol. 21, No. 2, March/April 2017.
- Weisong Shi, Hui Sun, Jie Cao, Quan Zhang, and Wei Liu, 边缘计算:万物互联时代新型计算模型, 《计算机研究与发展》, Vol. 54, No. 5, pp. 907-924. May 2017. (Chinese)
- Qingyang Zhang, Zhifeng Yu, Weisong Shi and Hong Zhong, Demo Abstract: EVAPS: Edge Video Analysis for Public Safety, in Proceedings of 1st IEEE/ACM Symposium on Edge Computing (SEC), Washington DC, Oct 27-28, 2016.
- Quan Zhang, Xiaohong Zhang, Qingyang Zhang, Weisong Shi and Hong Zhong, Firework: Big Data Sharing and Processing in Collaborative Edge Environment, in Proceedings of 4th IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), Washington DC, Oct. 24-25, 2016.
- Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li and Lanyu Xu, Edge Computing: Vision and Challenges, IEEE Internet of Things Journal, Vol. 3, No. 5, October 2016, pp. 637-646.