Swim Swarm: Digital Twins + UAVs + Machine Learning

by Brad Johnson, on Sep 22, 2017 11:21:00 AM

Swim ESPTM is a streaming data analytics engine that applies machine learning in real-time, taking advantage of a distributed learning architecture to gain efficiency, ensure security and high availability, and to permit rapid and reliable dissemination of learned insights. Swim ESP is self-training and operates in real-time, training and learning continually from data streams.

Swim takes advantage of the price & performance benefits of Moore’s Law to deliver an affordable, easy to use, secure, and fault tolerant computational fabric that facilitates real-time decision making that naturally and efficiently aggregates and publishes relevant data to higher-order applications and services:

  • A digital twin of each real-world object learns from contextually relevant data streams, simplifying the learning problem.  
  • By learning on devices, rather than the cloud, Swim enables revolutionary autonomous control decisions that are appropriate for robotics systems, aircraft, drones and other self-controlling vehicles, and other real-time control systems.
  • Swim edge learning is self-training.  Its algorithms continually check their hypotheses against real world data, training and adjusting for over fitting as needed.  This avoids the need for machine learning experts in the field.
  • Learned insights are available in real-time and in context, to permit smart, real-time control decisions.
  • Swim runs on commodity edge hardware: Swim can learn as much on a device that costs $50 at the edge, as a solution costing $1000/month in the cloud.
  • Swim uses learning to self-configure, self-secure and manage, reducing cost and complexity.
  • Swim is secure by design, providing a guaranteed chain of custody for all data, and hardware security with auto-patching.

Learn how SWIM implements Machine Learning at the edge to transform sensor data generated by UAVs from low-value data into low-overhead, high value insights capable of informing fast-timescale decisions by UAVs.

Topics:Machine LearningSWIM SoftwareIndustrial IOTEdge AnalyticsdronesUAV'sDigital TwinSWIM Swarm