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Building Large-Scale Applications for the Internet of Things at Bosch

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  • Drucksensor BMP085 von Bosch Sensortec setzt Maßstäbe bei Packungsdichte, Ruhestrom und Auflösung
    Der neue digitale MEMS Drucksensor BMP085 von Bosch Sensortec (5 x 5 x 1,2 Millimeter) unterstützt unter anderem den Trend, Navigationsfunktionen in Mobiltelefone zu integrieren. Mit seiner sehr feinen Höhenauflösung von bis zu 25 Zentimetern ermöglicht er eine sinnvolle Unterstützung bei einem fehlenden GPS Signal. Über die ROHS-Konformität hinaus ist der Sensor auch frei von Halogenen.

    UNIMAT Heating Boiler UT-M
    The UT-M boiler series, as flame-tube smoke-tube boilers, built in accordance with the Pressure Vessels Directive, is used to produce high-pressure hot water cheaply in the mid-temperature range up to 190°C.

    http://www.bosch.com/en/com/innovation/insidebosch/powertrains_of_tomorrow/challenges_posed_by_the_electric_powertrain/challenges_posed_by_the_electric_powertrain.html
    Challenges posed by the electric powertrain
    Powerful batteries, the latest power electronics: the electric drive presents engineers with many new challenges.
  • Within the Internet of Things, many if not all objects of our daily life will become „smart“. What does this mean?
    According to Prof. Elgar Fleisch from University of St. Gallen, „Smart Things“ are defined by the combination of „Things“ with IT Hardware and Software. This allows for two kinds of functions: first the original „primary“, „thing-based“ functions. Those are usually local functions, with known business models. A car drives you from A to B, a phone lets you make phone calls, a watch gives you the time, your glasses allow you to see.
    New is the second area: by adding IT based services via the IoT, many new additional „secondary“ functions become possible. They are in many cases not limited to the local physical device, and come often in combination with new business models.
    Examples: a car in an emergency may call the red cross service. Its sensor data may be used to warn other cars behind you about foggy or icy road stretchs. Floating position data from cars and phones may be used to get information about traffic congestions. Your watch will allow for remote monitoring of your health and give you an early warning before a stroke. And so on.
    Those are just a few example of new functions, that become possible with the Internet of Things – new chances to make lifes better, and certainly also significant new business opportunities for companies to create new customer offerings based on the combination of smart things and web-based services.
  • Right: AVIATION TOOLS COMPRESSION RIVETER
  • Quuppa
  • Bosch-Begriff: Connected based validation = kontinuierliches Feedback von Steuergeräten im Feld (von uns übersetzt)
  • Links: Daten von BMW
    Rechts: Daten von Bosch After Market

    Rechts: VIN = Vehicle Identification Nr
    Array 64 – Hashcode Verschlüsselung
    Unidirectionale Übersetzung, d.h. kann wieder zugeordnet werden
    Aber anonym, d.h. Auto kann nicht identifiziert werden
    Ensure Data ownership
  • Links: JSON
    Rechts: RoboMongo

    Lösung als Cloud Service + On-Premises
    IoTS Backend-Platform ist generisch
  • Transcript

    • 1. IoT and Big Data Building Large-Scale Applications for the IoT Dirk Slama, Director Business Development, Bosch Software innovations
    • 2. IoT and Big Data 2 IoT Predictions (by 2020-22) 7,1tn IoT Solutions Revenue | IDC Some Big Numbers: 1,9tn IoT Economic Value Add | Gartner 309bn IoT Supplier Revenue | Gartner 50bn Connected Devices | Cisco 14bn Connected Devices | Bosch SI Some Small Numbers: http://postscapes.com/internet-of-things-market-size Peter Middleton, Gartner: “By 2020, component costs will have come down to the point that connectivity will become a standard feature, even for processors costing less than $1 “
    • 3. IoT and Big Data Bosch Group – Example Products
    • 4. IoT and Big Data THING IT [HW | SW] THING-BASED FUNCTION [Local | Business models known] IT-BASED SERVICE [Global | Business models required] IoT Formula for Success Example SERVICE: Send ambulance in case of accident (detected by sensors) Example FUNCTION: Drive from A to B A B Source: University of St. Gallen, Prof. Dr. Elgar Fleisch
    • 5. IoT and Big Data Key Elements of the IoT Ecosystem Enterprises Partners Users Things
    • 6. IoT and Big Data Vehicle  Equipped with telematics unit  Sensors to monitor moving parts, hydraulics liquids, etc Partners  Service provider  Repair specialist and vehicle manufacturers Vehicle Driver  On-board diagnostics  Information about other vehicles, e.g. to unload harvest Vehicle Operations  Intelligent monitoring of machine KPIs and fluid analysis  Optimum servicing intervals Example: Remote Condition Monitoring
    • 7. IoT and Big Data So this brings us to…
    • 8. IoT and Big DataTensHundredsThousandsMillionsBillionsConnections Internet of Things Machine-to-Machine Isolated (autonomous, disconnected) Monitored Smart Systems (Intelligence in Subnets of Things ) Telemetry and Telematics Smart Homes Connected Cars Intelligent Buildings Intelligent Transport Systems Smart Meters and Grids Smart Retailing Smart Enterprise Management Remotely controlled and managed Building automation Manufacturing Security Utilities Internet of Things Sensors Devices Systems Things Processes People Industries Products Services Growth in connections generates an unparalleled scale of data Source: Machina Research 2014
    • 9. IoT and Big Data A new mindset and technology is required for IoT A changing approach to databases in the Internet of Things
    • 10. IoT and Big Data Data Big data Changing data models Real-time Processing Aggregation Internet of Things Large estates of devices Evolving applications All forms of data Data streaming and processing Pre-IoT (M2M) Limited estate of devices Single purpose applications Structured / Semi- structured Data transfers (sensors and actuators) Evolution from M2M to IoT and Big Data Source: Machina Research 2014
    • 11. IoT and Big Data Data Big data Changing data models Real-time processing Aggregation Databases will need to address new requirements Scalability Flexibility Analytics Unified View Source: Machina Research 2014
    • 12. IoT and Big Data IoT Foundation: Bosch Suite for IoT A D C B Scale Flexibility Analytics Unified View
    • 13. IoT and Big Data Use Case 1: Handheld Power Tools
    • 14. IoT and Big Data MongoDB Schema for Assets & Nutrunners Nutrunner { _id: "Nutrunner1", asset_id: "Asset1", status: "ready", battery_status: "40", total_cycles: "5000", tightenings : [ { timestamp: "2014-06-18 12:00:00", torque: "90", toleranceTorque: "0.2", angle: "20", toleranceAngle: "0.1", duration: "1.9s", status: "OK" } ] } 1 M2M Device Information Model
    • 15. IoT and Big Data Phase I (work in progress) 1 M2M Asset Management M2M as „ESB for Devices“ ID Card, Tightening Tool, Riveter, Paint Spray Gun, … M2M Device Information Model
    • 16. IoT and Big Data UI (work in progress) 1
    • 17. IoT and Big Data Phase II (future) HAIP: High Accuracy Indoor Positioning MES
    • 18. IoT and Big Data Use Case 2: Systematic Capturing of Field Data Project sFDE  Field Data Collection and Analysis  Components: Car brakes, power steering etc (via ECU)  Usage patterns: temperature, voltage, etc.  Predictive maintenance, product optimization Synergies through standardized field data capturing Continuous feedback from control units in the field Reduce return rates Improve quality Why MongoDB: Constantly evolving system, from a data capturing and a data analytics point of view  Large amount of streaming data  Scalability, enterprise features  References and Support
    • 19. IoT and Big Data sFDE Solution in Production SaaS (Sotware as a Service) Asset Management Stream Processing Big Data Management Analytics BRM BRM Rollout Repair Shops 1.000
    • 20. IoT and Big Data sFDE BigData Management Organization, administration and governance of large volumes of both structured and multi-structured data.  Data quality  Accessibility for business intelligence and big data analytics applications  Data enrichment  Providing usability for multi- structured and semi- structured data from a variety of sources  Ensure data security  Data classification to provide smaller sets of data for quickly analyzing  Highly scalable  High performance / low latency  Robustness  Data as a service Goals
    • 21. IoT and Big Data sFDE BigData Management  Key goal: Provide usability for multistructured and semistructured data from a variety of sources
    • 22. IoT and Big Data sFDE BigData Management  Data enrichment with structure information (Diagram types, data ranges, computed geo information, …)  Data transformation (Views, …) _28_AbsPdiffDist: { “DiagTyp”:”dim2” "_0_from_0_to_2p5_s" : { “rangeV”:0, “rangeT”:2.5, "_0_from_0_to_15_bar" : { “rangeV”:0, “rangeT”:15, "unit" : "s", "value" : 71.27999877929688 }, .... "_6_greather_than_200_bar" : { “rangeV”:200, “rangeT”:”max”, "unit" : "s", "value" : 0 } }, "_2_from_6p25_to_12p5_s" : { ....  Accessibility for business intelligence and big data analytics applications  Usability of data
    • 23. IoT and Big Data Capabilities Solutions Summary Bosch SI IoT Suite M2M | BPM | BRM | Big Data A D C B Scale Flexibility Analytics Unified View
    • 24. IoT and Big Data Any questions?