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Digital transformation and AI @Edge

  1. Digital transformation and AI @Edge Cindy Xing
  2. Bio - Co-chair K8s IOT/Edge workgroup - Project lead and code maintainer for CNCF sandbox project KubeEdge - Member of K8s community - Principal Software Engineer at Microsoft working on Azure IOT Edge - Years of working experiences in distributed system - Passionate on enabling customers and innovations through Edge Computing
  3. Industrial 4.0 - A new era of industrial revolution - Advanced manufacturing techniques - IOT and AI - Inter-connect everything - Digital transformation and automation - Data everywhere and data is the King
  4. What does a manufacturer care about DESIGN Provide the new generation of products with improved processes and design INBOUND LOGISTICS OPERATION Apply the best practices to daily operations of producing goods SERVICE Collect feedback and reiterate on the next-gen product AKA, Supply Chain. Provide parts/ingredients needed for production OUTBOUND LOGISTICS Deliver goods in the most efficient way to customer
  5. Step by Step to Digital Transformation “Pay-as-you-go”, Outcome-based Products Products that never break “New business models” Services 7 Digital Feedback Loops, Method Calls How can an autonomous response be achieved? “Self-optimizing” Adaptability 6 Long-Term Storage, AI Models, Machine Learning What will happen? “Being prepared” Predictions 5 Time- Series/Historical Data, Hierarchical Data Modeling Why is it happening? “Understanding” Transparency 4 Telemetry Dashboards What is happening? “Seeing” Visibility 3 Edge Gateways How to Connect? “Plugging in” Connectivity 2 PLCs/IPCs What Data? “Defining Tags” Computerization 1 Value Time
  6. Data Powers Manufacturing Innovation Introducing 21st century style product analysis from IT products into manufacturing industry. Create “Digital Twin” of the physical product including all its features, parameters, interactions, and lifecycle. Bridging the gap of all manufacturing operations and achieving informed products and processes requires building stronger collaboration, breaking silos, and accessing level-appropriate data freely. Innovation Redesign processes to move other primary activities into production Data provide visibility and insight on all aspects of products, processes, and people Data-informed company: build a data-informed culture Digitize and digitalize OEE (Overall Equipment Effectiveness), which is the gold standard to understand, measure, and improve Availability, Performance, and Quality; And also achieve Prescriptive Maintenance etc.
  7. Industrial 4.0 and Edge Computing • Data and intelligence close to customers • Short network latency & quick response • Higher security and compliance requirement • Larger scalability and highly de-centralized • Heterogeneous and diversified hardware/network settings • Cloud and Edge integration • New market, technologies and opportunities • Solution for OT/IT together
  8. AI @ Edge • Rich and large amount of raw data • Highly responsive and closer to real-time • Enhanced security features to be added • Highly de-centralized • Closer integration of Cloud and Edge (inferenced AI) • Superior customer experience
  9. New Technologies • Application • Execution format: container • Execution environment: docker, lxc, containerd, cri-o, kata, etc. • Orchestrator: Kubernetes, mesos, cloudfoundry, openshift, etc. • Communication Protocols and Standards: OPC UA, MQTT, AMQP, Modbus • Ways to collect and process data • IOT (Sensor, smart devices, etc.) • Run inferenced AI model @edge • Edge computing architecture and projects: Edgex, Akraino, KubeEdge, K3S, etc.
  10. Rich data modeling preserves source context Vendors can extend the data model of each product (Companion Specification) Maps to industrial protocols, e.g. BACNet | PLCopen | MTConnect | … Vendor, Platform and OS Independent Open Source on GitHub Discoverable Services Oriented Architecture (SOA) independent of the transport method Owned by a Non-Profit (OPC Foundation) 50M installed base and exponential growth Secure Design from group-up Based on open security standards Authentication | Encryption Evolves as security technologies evolve Vendors/Users can choose level of security Easily acceptable by IT departments Data ModellingInteroperability Security The Industrial Interoperability Standard
  11. Opensource Edge Projects
  12. Challenges • Huge amount of data highly geographically distributed • Diversified network and communication protocols • Real time data processing and data analytics • Higher security and compliance risks • Resource constraint and network connection bandwidth/reliability • Infrastructure for OT and IT • Tooling for application developers and data scientists
  13. Hackers breach IoT devices to launch an attack that takes down the internet for a day IoT attacks on the rise Hackers infiltrate critical safety systems in nuclear, oil and gas plants, halting operations Attackers gain access to casino database through fish tank’s connected filter device Mirai Data Breach Triton
  14. SOURCE: "IDC Marketscape: Worldwide Industrial IoT Platforms in Manufacturing 2019 Vendor Assessment" by Stacy Crook and Reid Paquin, June 2019, IDC #US45116819 and IDC #US45116919 IDC Marketscape on Manufacturing and Energy