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This paper describes how a system that performs Live Learning can be modified to use radar rather than visible light. The pipelined and iterative machine learning (ML) workflow of this system can operate at low network bandwidths for selective transmission of rare unlabeled events embedded in high-bandwidth real-time sensor data. While radar offers greater range and can overcome the severe signal attenuation experienced by visible light under conditions such as rain or fog, it poses a number of challenges for ML. This paper describes how these challenges can be overcome for Live Learning, and identifies some future directions for research. 

We introduce survival-critical machine learning (SCML), in which a robot encounters dynamically evolving threats that it recognizes via machine learning (ML), and then neutralizes. We model survivability in SCML, and show the value of the recently developed approach of Live Learning. This edge-based ML technique embodies an iterative human-in-the-loop workflow that concurrently enlarges the training set, trains the next model in a sequence of “best-so-far” models, and performs inferencing for both threat detection and pseudo-labeling. We present experimental results using datasets from the domains of drone surveillance, planetary exploration, and underwater sensing to quantify the effectiveness of Live Learning as a mechanism for SCML. 

The convergence of 5G wireless networks and edge computing enables new edge-native applications that are simultaneously bandwidth-hungry, latency-sensitive, and compute-intensive. Examples include deeply immersive augmented reality, wearable cognitive assistance, privacy-preserving video analytics, edge-triggered serendipity, and autonomous swarms of featherweight drones. Such edge-native applications require network-aware and load-aware orchestration of resources across the cloud (Tier-1), cloudlets (Tier-2), and device (Tier-3). This paper describes the architecture of Sinfonia, an open-source system for such cross-tier orchestration. Key attributes of Sinfonia include: support for multiple vendor-specific Tier-1 roots of orchestration, providing end-to-end runtime control that spans technical and non-technical criteria; use of third-party Kubernetes clusters as cloudlets, with unified treatment of telco-managed, hyperconverged, and just-in-time variants of cloudlets; masking of orchestration complexity from applications, thus lowering the barrier to creation of new edge-native applications. We describe an initial release of Sinfonia ( https://github.com/cmusatyalab/sinfonia ), and share our thoughts on evolving it in the future.