The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors.
Tag: Cyber-Physical Systems
M2-S Smart-ARM: Smart Streaming Telemetry for Agile and Resilient Management of Wireless and Mobile Software Defined Networking
Traditional application-driven (i.e., fault, SLA, or DoS attack detection) “pull” model (SNMP) based network management is expensive and slow for network problem detection, isolation, and root cause analysis. Especially, the recent federation of novel softwareisation and virtualization architectures, as well as Internet of Things (IoT) technologies, require better management over the heterogeneous systems and services. The project adopts “push” based open source forwarding (streaming) network management technologies by using P4 (Programming Protocol-Independent Packet Processors) Inband Network Telemetry (INT).
M3-T Mobile Edge Point Cloud Services for Auto Driving
Provide Realtime live 3D map service from mobile edge using distributed sensing and low latency point cloud aggregation and multicasting. We will use high efficiency scalable point cloud source coding. We will provide real time point cloud LiveMaps aggregated through mobile edge computing. We propose a joint source-channel coding for V2V and V2I communication.