4 module09 iot
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The document discusses the growth of connected devices and the "Internet of Everything", where billions more "things" will be connected to the Internet by 2020. It describes how each connected device is part of a "digital shadow" that can provide contextual information about individuals to enable personalized services. By collecting data from these new sources, systems can gain a better understanding of human needs, behaviors, and intents.
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4 module09 iot
- 4. Microsoft Confidential, For Internal Use Only • Wave 1 - 1990s' fixed Internet: 1 billion users • Wave 2 - 2000s' mobile wave: another 2 billion connected endpoints • Wave 3 - Internet of Everything: connect 10X - 20X as many "things" to the Internet by 2020 EXPLOSION OF CONNECTED POSSIBILITY
- 5. Microsoft Confidential, For Internal Use Only Mobile Gateway Car Gateway Desktop Gateway In-room Gateway Wearable Gateway Portable Devices Desktop Devices Floortop Devices Ambient Sensors Wearable Sensors Wearable Devices • Context - Each “thing” or connected device is part of the digital shadow of a person, essential to enable deeply contextualized services. • Assistance & Task Completion - This informs business operations and services as much as it informs an individual’s engagement and behavior. • Understanding Intent - Every new dimension of data increases predictive power, enabling an agent, bot, or application to contextually answer the question: “What does the human want?” • Knowledge & Insight - From the data streams that implement the “digital shadow”, we can use predictive analytics to understand people’s needs and behaviors better than ever before. ‘THINGS’ - THE DIGITAL SHADOW OF A PERSON The most interesting aspect of the Internet of Things is the world of humans that use it
- 6. Microsoft Confidential, For Internal Use Only
- 7. Real World IoT Use Cases Electric charging stations Street sweepers Postboxes Aircrafts Auto Elevators Factory floor Oil equipment Cows Engines Vending machines Buildings Fryers Medical devices Vaccine dispensers Trucks BusesDogs Oil distribution Smart meters Internet of Things Power plant Surveillance Power tools Racing Mining equipment Smart grids
- 8. 业务愿景 为全球超过50%港口重型 机械器具提供智能服务 战略合作 在数字化码头业务转型、 全球化服务平台构建、 领先的行业软件开发中 心构建、全球化协同办 公平台构建等方面进行 全面合作，共筑全球重 工行业标杆 业务收益 • 通过全球化服务平台能力拓展全球 业务 • 快速实现一带一路战略 • 避免国际化专利纠纷及合规风险 “振华重工将会集中精力攻克全自动化码头核心技术，走一体化、系统 化集成发展之路，转型为全自动化码头整体解决方案的工程总承包商.” — 宋海良, 董事长, 振华重工 振华重工—— 成为全球全自动化码头的领导者
- 10. AZURE REGIONS 42Announced Azure regions world wide Hyper-Scale Capacity 3.5 Trillion Messages / Week Hyper-Scale Azure Footprint AZURE IOT REGIONS 12 Azure IoT regions world wide
- 11. Azure IoT Suite Remote Monitoring - BasicDevices Back end systems and processes Cosmos DB Web App Logic AppsIoT Hub C# simulator Microservices Active Directory
- 12. Custom Cloud Gateway Custom Cloud Gateway Field Gateway Device VPN/ExpressRoute OPC, HTTP, CoAP,… Custom Protocols Client SDK IoT Edge AMQP Management app Device registry/Management Monitoring Send commands Client SDK Management app Device registry/Management Monitoring Send commands Client SDK Cloud protocol gateway sample Cloud protocol gateway sample AMQP, MQTT, HTTPS Applications - Companion apps - Dashboards - Controllers - … CoAP, AllJoyn, OPC,… Device Device CoAP, AllJoyn, OPC, … Device Device Device IoT Hub Field Gateway
- 14. IoT in the Cloud and on the Edge IoT in the Cloud IoT on the Edge Symmetry
- 19. Well site Azure IoT Edge Replaying pump readings
- 21. IoT Device Device Twin Methods Properties Desired Reported IoT Hub Device Twin Tags Methods Telemetry Properties Desired Reported Telemetry channel Cloud owned, device visible Device owned, cloud visible Cloud only, device metadata Cloud initiated C2D with response Commands Cloud initiated C2D message
- 24. Azure IoT Starter Kits Raspberry Pi 3 Kit Windows 10 and Raspbian Samples in C and C# Feather M0 Wi-Fi Kit RTOS Samples in Arduino IDE and C Feather Huzzah ESP8266 Kit RTOS Samples in Arduino IDE and C ThingDev Kit RTOS Samples in Arduino and C Intel Edison Kit Linux Yocto Samples in JavaScript (Node.js) http://azure.com/iotstarterkits
Editor's Notes
- http://www.goldmansachs.com/our-thinking/outlook/iot-infographic.html Last year, 4.4 ZB of data was created… Human Genome has 3B Base Pairs = 750 MB, Humanity’s Genome = 7.2B x 750 MB = 4.7 EB …equals data created every 9 hours The Library of Congress has 24M books = 189 TB of information …equals data created every 1.26 seconds In 2016, we will create 13 ZB of data In 2020, we will create 44 ZB of data Source: Digital Universe Forecast, IDC, April 2014
- http://venturebeat.com/2014/03/28/looking-beyond-the-internet-of-things-hype-heres-whats-in-store/
- 8
- So why choose Azure? The answer is our unmatched hyper-scale footprint. As you can see Azure provides truly hyper scale with the largest public cloud infrastructure in the world. There are 38 announced datacenters worldwide with more planned each year and Azure IoT is present in a large number of these with more added each quarter depending on business demand. Azure is the only cloud provider recognized in the industry to have leading solutions in IaaS, PaaS, and SaaS. Azure PaaS platform services can help you be more productive and increase your ROI. In addition, Azure has the most comprehensive compliance coverage of any cloud provider with 50 compliance offerings. To protect your organization, we embed security, privacy and compliance in our development methodology.
- Describe Rod Pumps: I am sure you have seen those pumps around, extracting oil from wells that sometimes are 1 mile deep. These pumps are usually in very remote locations with little to no connectivity and no human supervision most of the time. Traditionally the pumps have been running continuously at the same pace, without taking into account the changing conditions such as temperature, oil pressure downhole, making them all but really efficient. Finally, maintenance teams have to visit pumping sites often for no reason most of the time and too late in some cases to prevent damage or loss. Schneider solution: Schneider Electric created a SCADA solution called Realift to optimize efficiency, extend well life and lower operating expenses. Realift consists in monitoring well sites and oilfields, collect real time data from sensors attached to pumps and analyze this data to detect anomalies and optimize efficiency. Today, when a problem occurs, the SCADA system will notify over SMS or email an expert who will interpret data from sensors to diagnose the issue. Schneider ML models Schneider wants to be proactive and started working with Azure Machine Learning to do predictive maintenance. Considering the remote location of the pumps and their limited connectivity, Schneider needs to run the ML models as close as possible to the pumps.
- Data analyzed by ML models: Sensors measuring pump position and fluid load at the surface of the well allow creating an X-Y plot ‘dynacard’ that provides visibility of downhole conditions that can impact pump fill. Machine Learning models trained with millions of dynacards can instantly and automatically detect anomalies and downhole conditions. No need for a human to look at hundreds, thousands of dynacards to diagnose an issue or determine when next maintenance is needed. Here is the challenge: Rod pumps being in remote locations with little to no connectivity it is often difficult and costly to have them send all sensors data to the cloud for analysis. Furthermore, if a defect or anomaly is detected that requires immediate action to stop the pump, then the round trip to the cloud and bad connection can be in the way of taking action in a timeline manner. Last but not least, in the oil & gaz industry (like in many other ones) this data is very sensitive and customers prefer not seeing it leave the production site.
- Schneider Realift SCADA solution allows real time monitoring of well sites. [Animate] A SCADA Remote Terminal Unit installed onsite collects data from pump sensors through a field bus connection like CAN. [Animate] A local SCADA application, monitors sensors through the RTU over a Modbus connection [Animate] When an issue occurs, the local SCADA application will notify the supervision site sending an SMS or email. [Animate] As most often sites are not connected or transmitting data is very expensive, an expert has to be dispatched onsite to diagnose the issue looking at dynacards data. [Animate] If an expert cannot be dispatched, data needs to be brought back to the supervision site over an expensive connection or manually. [Animate] Data collected when issues occur or during regular onsite maintenance is also used to optimize pump efficiency. But as you can imagine, having an expert looking at every single dynacard to determine issues and trends is subject to optimization. For this, Schneider experts developed and trained Machine Learning models in the Cloud with the dynacards data from sites all over the world. These ML models help determine trends to optimize pumps usage efficiency and also plan for onsite maintenance. While this solution has already demonstrated it can save a lot of energy, and minimize costs, it is still reactive. The intelligence is in the Cloud and data is hard to bring up there.
- What Schneider wants to do is to bring the intelligence at the Edge to make their SCADA solution proactive, implementing predictive maintenance. And we have it working here for real! Here is what you will see in the demo in a minute [Animate] On the Well site, we have added a gateway device. [Animate] This device runs Azure IoT Edge [Animate] An edge module has been developed to collect dynacards data from the RTU over ModBus. If the IoT Edge device is connected, this module can send real time data to Azure for remote monitoring as well as further training ML models in the cloud [Animate] The data is then converted by another module in a format that will be readable by the ML model, itself running on IoT Edge as a module. [Animate] The ML model scores the dynacards and if it detects an anomaly it can notify the SCADA system for it to adjust the pump operation or stop it. In case of alert the SCADA system will send an alert over SMS or email (if connectivity permits) [Animate] Once again, if the IoT Edge device is connected, it can send the alert up to the cloud itself to trigger maintenance processes automatically in LOB applications. [Animate] For the demo we are replaying readings from a real pump and I will be playing the local operator directly connected to the RTU to show you what’s happening
- Device Twin: 裝置對應項