MongoDB IoT City Tour STUTTGART: Industrial Internet, Industry 4.0, Smart Factory & Big Data. By, Bosch SI

I o T E U R O P E A N C I T Y T O U R
S T U T T G A R T
TechMahindra, MongoDB and Bosch Software Innovations
© TechMahindra, MongoDB and Bosch Software Innovations GmbH 2014. All rights reserved, also regarding any disposal,
exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.

Industrial Internet, Industry 4.0, Smart
Factory & Big Data

Some Big Numbers:
14bn Connected Devices | Bosch SI
50bn Connected Devices | Cisco
309bn IoT Supplier Revenue | Gartner
1,9tn IoT Economic Value Add | Gartner
3
IoT Predictions (by 2020-22)
7,1tn IoT Solutions Revenue | IDC
http://postscapes.com/internet-of-things-market-size

Some Big Numbers:
14bn Connected Devices | Bosch SI
50bn Connected Devices | Cisco
309bn IoT Supplier Revenue | Gartner
1,9tn IoT Economic Value Add | Gartner
4
IoT Predictions (by 2020-22)
7,1tn IoT Solutions Revenue | IDC
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

Indiegogo.com
Many Ideas
5
Sonymobile.com
Amazon.com
allaboutsymbian.com
Indiegogo.com
Neuerdings.com
Inspired by: C Weiss, Holisticon

Bosch Group – IoT Candidates?
6

7
Circle of Concern today
This is where the story ends

Circle of Concern tomorrow
8
This is where the story starts

9
How the ‘Internet of Things’ impacts global manufacturers
Industrie 4.0 @ Hannover Messe 2013
Chancellor Dr. Angela Merkel receives the report „Industrie 4.0“
from Prof. Dr. Henning Kagermann (acatech) and
Dr. Siegfried Dais (Bosch)

Smart Factory
 New business models through the convergence of IT & production
10
Customer
Integrated
Engineering
Resilient
Factory
Networked
Production
Predictive
Maintenance
Technology
Marketplace
Adaptive
Logistics
Up-Cycling
Source: Forschungsunion Wirtschaft & Wissenschaft

11
The Internet of Things: Nobody can do it alone
Component & Device Manufacturer Telcos IT Companies and System Integrators
Cloud
Services
Platform Application Integration
Connec-tivity
Chipset Module Equipment
Software Innovations Offering
M2M Value Chain
Machine-to-Machine = applications
without human intervention
„embedded world“ „IT world“

Example: Indoor Asset Tracking
12

13
TechMahindra Track & Trace

Asset Details Page
14

Indoor-Localization using WiFi
15

Architecture Blueprint
Customer Manufacturer Operator
HTML5/Javascript (Bootstrap/AngularJS)
Identity Management
BPM
- Cisco Position
- Mail notification
Bosch Software Innovations Suite
Cisco
MSE
M2M
- Nutrunner drivers
- Smartcards
BRM
-Event evaluation
Third Party
Systems
Payment
Data
Bases
Mongo
Mongo
Bosch SI PaaS
TechM/AWS
ERP
NEXO Nutrunner Smart Id Card
16

Outlook: High Accuracy Indoor Localization
17
HAIP: High Accuracy Indoor Positioning

Outlook: Vertical Integration + HAIP
 Vertical Integration
 From Field Level
to ERP
 Automatic creation
of quality lots
 Full transparency
18
Example Architecture

Foundation: Bosch Software Innovations Suite
Business &
End User
User Interface
19
Common Environment
App Store
Design Tools
M2M UI Models
M2M Inform. Models
BPM Process Models
Design Time
Repository
Identity
Management
User Interface Management
Execution Systems
Business Rules
Management
(BRM)
Business Process
Management
(BPM)
Device
Management
(M2M)
Tenants
Users
Roles
Relations
Authorizations
Distributed Devices
Editors
BRM Rule Models
• Modeling
• Optimization
• Administration
• Deployment
• Execution, Monitoring
• Modeling
• Simulation
• Implementation
• Execution
• Monitoring, Reporting
• Device Integration
• SW-Provisioning
• Central Registry
• Event Management

Connecting Physical Devices: Flexible Options
WiFi Bluetooth
Agent
Hub
Agent
Hub
Edge Controller
GSM LTE UMTS
DSL WAN Internet
GSM LTE UMTS
DSL WAN Internet
Edge Node
Communication
Management
• Connection
• Routing
• Events
• Certificates
• Authentication
• Encryption
M2M Back End
Local Network Global Network
1
2
ZigBee
NFC
control
Backend
Hub
GSM LTE UMTS
DSL WAN Internet
BACnet OPC
Edge Controller
Edge Node
Infrastructure
Management
–  : Depending on hardware performance, timing, reliability, communication
cost, different distributed system architectures are supported
Information
Models
Software
Provisioning
3
1 3
20

Managing IoT Data - Information Models as a Key Enabler
Services, Events, Properties
Information Model Implementation
21
 Each physical “thing”
(device/subsystem) is formally
described using WSDL/XML
models as basis for software
abstraction
 The model defines the properties
and capabilities of the (physical)
system
 Enables typical applications like
 Configuration management
 Performance/status monitoring
 Fault management
Device Drivers
Contract
Contract
WSDL, XSD
Java
Java
Defines the services,
events, properties and
also communication
patterns
single device or
subsystem

So this brings us to…

IoT Data - Growth in connections generates big data
Tens Hundreds Thousands Millions Billions Connections
Internet of Things
Machine-to-Machine
Monitored
Building
automation
Manufacturing
Security
Utilities
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
Sensors
Devices
Systems
Things
Processes
People
Industries
Products
Services
Internet of Things
Source: Machina Research 2014

Evolution from M2M to 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)
Source: Machina Research 2014
A
B
C
D

• IoT apps generate multi-structured
data
• Modeled more efficiently
as JSON documents
• Exposed to powerful
analytics
• Developers more
productive
Modeling Complex Data
{
assetId: "NEXO 109",
hour: ISODate("2014-07-03T22:00:00.000Z"),
status: "Online",
type: "Nutrunner",
serialNo : "100-210-ABC",
ip: "127.0.0.1",
position: {
0: {
0: { x: "10", y:"40", zone: "itc-1",
accuracy: "20” },
…,
59: { x: "15", y: "30", zone: "itc-1",
accuracy: "25” }
},
…,
59: {
0: { x: "22", y: "27", zone: "itc-1",
accuracy: "22” },
…,
59: { x: "18", y: "23", zone: "itc-1",
accuracy: "24” }
}
}
}
A B C D

Use Case 2: Systematic Capturing of Field Data
B
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: data capturing and
data analytics point of view
 Large amount of streaming data
A
 Scalability, enterprise features
 References and Support
C D

From Concept to Code
Key Capabilities of Bosch SI Suite & MongoDB
Solutions
Capabilities
Bosch SI IoT Suite
M2M | BPM | BRM | Big Data
A B C D
Scale Flexibility Analytics Unified View
27

Innovative Platforms and Solutions for the IoT
Bosch Software Innovations:
Systems and Software House for IoT Solutions
Manufacturing
Service Portal
Mobility
Fleet Management
eMobility Solutions
Bosch IoT Suite M2M BPM BRM
Energy
Smart Metering
VPP (Virtual Power Plant)
Smart Home
Intelligent Solutions
28

Thank You
29

MongoDB IoT City Tour STUTTGART: Industrial Internet, Industry 4.0, Smart Factory & Big Data. By, Bosch SI

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MongoDB IoT City Tour STUTTGART: Industrial Internet, Industry 4.0, Smart Factory & Big Data. By, Bosch SI