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

IoT and Big Data
Building Large-Scale Applications for the IoT
Dirk Slama, Director Business Development, Bosch Software innovations

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
“

IoT and Big Data
Bosch Group – Example Products

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

IoT and Big Data
Key Elements of the IoT Ecosystem
Enterprises
Partners
Users
Things

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

IoT and Big Data
So this brings us to…

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

IoT and Big Data
A new mindset and technology is
required for IoT
A changing approach to
databases in the Internet of
Things

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

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

IoT and Big Data
IoT Foundation: Bosch Suite for IoT
A
D
C
B
Scale
Flexibility
Analytics
Unified View

IoT and Big Data
Use Case 1: Handheld Power Tools

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

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

IoT and Big Data
UI (work in progress)
1

IoT and Big Data
Phase II (future)
HAIP: High Accuracy Indoor Positioning
MES

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

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

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

IoT and Big Data
 Key goal: Provide usability for multistructured and semistructured
data from a variety of sources

IoT and Big Data
 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

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

IoT and Big Data
Any questions?

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

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