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