Industrial IoT Solution Architecture Design From Connectivity to Data

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Leonard Hutchinson
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1 Industrial IoT Solution Architecture Design From Connectivity to Data Cheryl Hsu Program Manager Strategic Engagement & Industrial IoT, Microsoft

2 IoT Enables a Digital Feedback Loop The benefits are profound IoT enables a digital feedback loop that connects PEOPLE Customers Operations CUSTOMERS DATA PRODUCTS Products/Assets INTELLIGENCE Employees Our vision is to help businesses take advantage of the digital feedback loop OPERATIONS

3 Digital Feedback Loop A realtime connection enables new breakthrough levels of insights that in turn drive informed actions INSIGHTS THINGS ACTIONS

Respond and recover quickly With fragmented solutions Maintain technicians onsite to determine and resolve issues Take days or weeks to reroute and reconfigure devices Search for data needed for

4 Respond and recover quickly With fragmented solutions Maintain technicians onsite to determine and resolve issues Take days or weeks to reroute and reconfigure devices Search for data needed for root-cause analysis OR OR With IoT Access devices remotely to diagnose and resolve issues Complete corrections within hours, including rerouting processes and reconfiguring machines Access comprehensive data immediately to perform root-cause analysis 1 2 3

Expand, change and scale easily With fragmented solutions Solve storage on your own using capacity planning, capital purchases and ongoing maintenance + + Connect new devices later after

5 Expand, change and scale easily With fragmented solutions Solve storage on your own using capacity planning, capital purchases and ongoing maintenance + + Connect new devices later after customizations and integration efforts are complete Take weeks or months to modify and extend systems with custom connections With IoT Exploit cloud solutions to scale instantly and pay for only what you need Connect new devices now with little or no configuration required Add to and extend systems faster by building on the extensible architecture 1 2 3

6 Enabling the Digital Feedback Loop used to be challenging Data storage Enterprise integration Device recovery Updating devices Drivers On device analytics Securing data Solution scale Internationalization Transport protocols Device lifecycle Device commercialization HW certification Cloud-to-device commands THINGS CI/CD Disaster recovery INSIGHTS Cold path analytics Hot path analytics < ---- End-to-End Security ---- > Manufacturing scale Industry and government compliance Operations monitoring ACTIONS Business process integration High availability Device updates Provisioning devices Warm path analytics Data ownership Data visualization Cost management

7 Before working on Architecture Design

8 Understand your business needs and challenges Define value proposition Understand organizational impact Assess organizational capability Secure business stakeholder buy-in Input to Architectural Design

9 Getting Started IoT Reference Architectures

IoT Reference Architecture The latest Azure IoT cloud native

factoring for solutions Prescriptive technology recommendations

technology implementation choices Recommendations for scaling

10 IoT Reference Architecture The latest Azure IoT cloud native recommended architecture and latest technology implementation recommendations. Provides: Overview of the IoT space Recommended subsystem factoring for solutions Prescriptive technology recommendations per subsystem Proven production ready architecture Proven technology implementation choices Recommendations for scaling systems Reference architecture implementations such as Remote Monitoring and Connected Factory.

11 Things Insights Actions Every organization has unique skills and experience Every IoT application has unique needs and considerations

12 Azure IoT reference architecture Core Subsystems UI & Reporting Tools Visualize data and learnings IoT IoT Devices IoT Devices Provision and send data from device to cloud Device Management Cloud Gateway (IoT Hub) Stream processing and rules evaluation over data Stream Processing Business Integration Store data Storage Integrate with Business processes

13 All Subsystems Lambda Architecture Fast Path Real Time Processing IoT Edge IoT IoT Devices Devices Devices Device Management Cloud Gateway (IoT Hub) Stream processing and rules evaluation over data Store data Data Transformation Stream Processing Warm Path Store UI & Reporting Tools User Management Visualize data and learnings Business Integration IoT IoT Devices IoT Devices Devices Bulk Device Provisioning Cold Path Store Machine Learning Integrate with Business processes Slow Path Batch Processing

14 All Subsystems and Cross-Cutting needs IoT Edge IoT IoT Devices Devices IoT IoT Devices IoT Devices Bulk Device Provisioning Device Management 1. Devices send telemetry records or events to the cloud gateway. Cloud Gateway 2.1 Stream processing and rules evaluation is done for device telemetry records and events. Data Transformation Security Deployment High Availability and Disaster Recovery 2.1 Device telemetry data is transformed if needed. Store data 2.2 Device data telemetry is stored. Warm Path Store UI & Reporting Tools Stream Processing Cold Path Store User Management 4. Device information is visualized and shown in UI. Visualize data and learnings 3. Business Process integration ( , CRM, etc.) is executed. 4. ML processing is done for data. Machine Learning Business Integration

15 The industry s most agile, comprehensive, and secure portfolio Solutions (PaaS) Azure IoT (PaaS) Partner repeatable solutions Azure IoT Solution Accelerators Solutions (SaaS) Azure IoT Central IoT SaaS Microsoft Dynamics Connected Field Service Technologies (PaaS) Device support IoT Data and Analytics Visualization and Integration Azure IoT Device SDK Azure IoT Hub Azure Stream Analytics Azure HD Insight Microsoft Flow Azure Active Directory Azure IoT certified devices IoT Hub Device Provisioning Service Azure Time Series Insights Azure Data Lake Analytics Azure Logic Apps Microsoft Power BI Security Program for Azure IoT Edge Azure Machine Learning Azure Data Lake Notification Hubs Azure Monitor Windows 10 IoT Azure IoT Edge Cosmos DB Azure Websites

16 SaaS, PaaS, and IaaS Guidance

End-to-end implementation Completely customizable Open-source microservices based architecture Device connectivity and management Azure IoT solution accelerators Dashboards,

17 End-to-end implementation Completely customizable Open-source microservices based architecture Device connectivity and management Azure IoT solution accelerators Dashboards, visualization, and insights Workflow automation and integration Command and control Preconfigured solutions Remote Monitoring Connected Factory Predictive Maintenance Device Simulation

devices and begin tailor to your needs Fine-tuned to specific assets and

18 Accelerate time to value Start quickly for common IoT scenarios Finish with your IoT application Get started in minutes Modify existing rules and alerts Add your devices and begin tailor to your needs Fine-tuned to specific assets and processes Highly visual for your real-time operational data Integrate with back-end systems

19 Microsoft Azure IoT Solution Accelerators

20 Azure IoT Connected Factory

21 Industrial Devices (OPC-UA Servers) OPC UA Integration into Azure IoT Firewall JSON/AMQP OPC Clients, Servers, ERP Portals, OPC Graph Database and OPC UA.NET Standard Stack Presentation & Business Connections UA Binary UA Binary/AMQP IoT Proxy Module Hot Path Analytics Azure Stream Analytics, Azure Storm, Websites, Mobile Services UA Binary OPC UA Client Module JSON/AMQP Microsoft Dynamics Any Azure IoT Gateway SDK Azure IoT Hub Cold Path Analytics & Storage Azure HD Insight, Azure Storage, SQL, DocDB, Dynamics, BizTalk Services, Notification Hubs Other Devices On-Premise: Device Connectivity Cloud: Data Ingestion & Processing, Command & Control Cloud: Presentation

22 Industrial IoT

23 Critical needs in Industrial IoT Interoperability Scalability Precision Programmability Low latency

24 Connecting your factory with IoT

Global PLC & Industrial network market share Global PLC market share as of 2017, by manufacturer Industrial network market shares 2016 according to HMS Siemens Rockwell Mitsubishi Schneider

25 Global PLC & Industrial network market share Global PLC market share as of 2017, by manufacturer Industrial network market shares 2016 according to HMS Siemens Rockwell Mitsubishi Schneider Omron B&R GE ABB Others ail=true&bb=1

26 Connectivity Complexity Machinery Equipment CNC Injection Molding More Connectivity Ethernet capable? Serial standard? Others PLC Brand Driver OPC UA supported?

27 Enterprise & plant transformation Enterprise & Plant Topology Information Systems Enhanced ERP-Level Enterprise Resources Responsive and dynamic crossindustry Value Finance, HR, SCM, SRM, CRM Networks Communication Layer Manufacturing MES-Level Manufacturing Execution Systems Plant Cross-plant Schedules, performance, Products, BOMs, Routings, coordination Quality, & flexibility Performance Communication Layer Control-Level Machine Controllers HMI, Production SCADA, optimization Alarms, Events, & Historians reduced process costs Communication Layer P Device-Level Sensors, Devices Intelligent equipment with Automation Control, Safety, reduced failures & downtime

28 Industrial Devices (OPC-UA Servers) OPC UA Integration into Azure IoT Firewall JSON/AMQP OPC Clients, Servers, ERP Portals, OPC Graph Database and OPC UA.NET Standard Stack Presentation & Business Connections UA Binary UA Binary/AMQP IoT Proxy Module Hot Path Analytics Azure Stream Analytics, Azure Storm, Websites, Mobile Services UA Binary OPC UA Client Module JSON/AMQP Microsoft Dynamics Any Azure IoT Gateway SDK Azure IoT Hub Cold Path Analytics & Storage Azure HD Insight, Azure Storage, SQL, DocDB, Dynamics, BizTalk Services, Notification Hubs Other Devices On-Premise: Device Connectivity Cloud: Data Ingestion & Processing, Command & Control Cloud: Presentation

29 Azure IoT Connected Factory Architecture Telemetry path Time Series Insights OPC UA Server MES Simulation (OPC UA Client) OPC UA Server Command & Control path OPC UA Server Gateway SDK with OPC Proxy & OPC Publisher Modules IoT Hub Web App hosting Solution Dashboard & OPC UA Client Linux VM (with multiple assembly lines) VM

30 Real-Time Analytics Real-time Analytics (aka Stream Analytics) is the phenomenon of processing data as soon as it is generated, to derive very quick analysis/insight for timely action. Real-time Analytics We are trying to get insights from our devices in real-time, etc.

I N T E G R A T I O N W I T H A Z U R E E V E N T H U B & I O T H U B Azure Stream Analytics has built-in,

sources of Streaming Data to Azure Stream Analytics.

(see next slide) Azure Blob Storage is supported as a source of Reference Data.

31 I N T E G R A T I O N W I T H A Z U R E E V E N T H U B & I O T H U B Azure Stream Analytics has built-in, first class integration with Azure Event Hubs and IoT Hub Data from Azure Event Hubs and Azure IoT Hub can be sources of Streaming Data to Azure Stream Analytics. The connections can be established through the Azure Portal without any coding. (see next slide) Azure Blob Storage is supported as a source of Reference Data. Azure Stream Analytics supports compression across all data stream input sources (Event Hubs, IoT Hub, and Blob storage). Azure Event Hubs Azure IoT Hub Streaming Data Streaming Data Reference Data Azure Stream Analytics Azure Blob Storage

32 S T R E A M I N G - C A N O N I C A L O P E R A T I O N S

33 B I G D A T A S T R E A M I N G P A T T E R N W I T H A Z U R E SENSORS AND IOT (UNSTRUCTURED) AZURE ML STUDIO R SERVER AZURE DATABRICKS (Spark ML) REAL-TIME APPLICATIONS r LOGS, FILES AND MEDIA (UNSTRUCTURED) EVENT HUBS IoT HUB KAFKA on HDINSIGHT STREAM ANALYTICS AZURE DATABRICKS (Spark Streaming) STORM on HDINSIGHT BUSINESS / CUSTOM APPS (STRUCTURED) REAL-TIME DASHBOARDS

34 Advanced Analytics Advanced Analytics is the process of applying machine learning and/or deep learning techniques to data for the purpose of creating predictive/prescriptive insights. Advanced Analytics We are trying to predict and prevent in advance

35 A D V A N C E D A N A L Y T I C S - C A N O N I C A L O P E R A T I O N S

36 Collect Data Prepare Data Train Model Evaluate Model Deploy Model Model Creation & Deployment Process

37 IoT Pattern: Gaining Insight Azure IoT Hub Things Cloud Gateway Insights Actions Azure Stream Analytics

38 Consistency

39 IoT Pattern + Edge Azure IoT Hub Insights Things Actions Cloud Gateway Insights Actions

40 Azure IoT Edge Deployment Azure Container Service IoT Edge Device Azure IoT Hub Azure Machine Learning Azure Stream Analytics Azure Functions Azure Cognitive Services