Research Operation ManagerBrownien Lab

R&D DepartmentCAT Telecom PCL

System architecture of IIoT and Trends

Dr. –Ing. Somrak Petchartee

www.brownien_lab.cattelecom.com

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• Education:

• Federal Armed Forces University (UniBW), Faculty of Aerospace Engineering, Munich, Germany - Dr. -Ing., Doctoral degrees in engineering - Research Topic: Object Manipulation and Force Control by Using Tactile Sensors - Scholarship Donor: Research Assistant

Asian Institute of Technology (AIT), Thailand- M. Eng., Computer Science Program - Thesis Topic: Cooperation of Multiple Robot Arms By Kinematic-Coordinate Transformation Distribution- Scholarship Donor: Royal Thai Government (RTG)

King Mongkut’s Institute Of Techonology Ladkrabang (KMITL), Thailand - B.Eng., Telecommunication Engineering - Thesis Topic: G3 Facsimile Protocol Analyzer - 1st Class Honors, Class Rank 1 - Scholarship Donor: first position in entrance exam.

• Work Experiences:

• - Intelligent Robot Laboratory, Faculty of Aerospace Engineering, University of Federal Armed Forces,

• Germany;

• - Siemens Automotive Company, Germany; solutions and services for automotive production

• - Dallmeier Electronic Company, Germany; image processing and recording, the CCTV monitoring

• field, image transmission

• - Brownien Lab, R&D Department, CAT Telecom PCL, Thailand

Biography

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Working Experients• Siemens Automobile, Germany• Dallmeier Electronic, Germany

Research Interests

• Machine Vision• SDR , FPGA , Wireless Sensor• Fiber Optic , Optical Path Design• Robotic and Automation

Award and Honors

• Highly Commended Award Winner, Industrial Robotic Journal, Vol.35 No.4, 2008.Emerald Group Publishing Limited , England.

• Award SEPO Thailand: Outstanding Innovation Award (2016).• 22 papers in the international conferences, 10 journal articles, 1 book

Joint Management Committee of Engineering Institute of ThailandAdjunct Faculty of Asian Institute Of Technology (AIT), School Of Engineering and Technology.The luminaries funding committee: Office for Educational Technology Development Fund. The

Permanent Secretary, Ministry of Education

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• The Internet of Things (IoT)- is the network of physical objects accessed through the Internet. These objects contain embedded technology to interact with internal states or the external environment.

• The Industrial Internet Of Things (IIoT)- In simplistic terms the IIoT connects the world of industrial Things like sensors, actuators, controllers, robots, etc to computational capabilities residing in Internet-based storage and analytics.

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IOT vs IIOT

IOT IIOT

FOCUS ON Convenience for individual consumers. Return on investment by improving efficiency, safety, and productivity.

SYSTEM BREAKDOWN Do not immediately create emergencysituation. Important but not critical.

Often creates life threatening or other emergency situations. Mission critical.

DRIVING PHILOSOPHY Human Productivity Machine Productivity

APPLICATIONS Consumer level-devices : Wearable fitness tools Smart home thermometers

Systems used in high stakes industries : Manufacturing Aerospace Defences Energy

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FirstIndustrialRevolution

through the intro-duction of mechanical production facilities with the help of water and steam power

SecondIndustrialRevolution

through the intro-duction of a division of labor and mass production with the help of electrical energy

FourthIndustrialRevolution

through the use of cyber-physical systems

ThirdIndustrialRevolution

through the use of electronic and IT system that further automate production

Time

1800

First mechanical Boom, 1784

First assembly line Cincinnati slaughter houses, 1870

First programmable login controller (PLC), Modicon 084, 1969

Degree of complexity

1900 2000 Today

The path to Industry 4.0 is via the IIoT

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ERP/CRM INTEGRATION

ANALYTICS AND OPTIMIZATION SOFTWARE

BIG DATA

COMMUNICATION HUBS, GATEWAYS, SWITCHES

FIELD SENSORES, DISTRBUTED PLCs, INDUSTRIAL PCs, SCADA

The automation systems stack that enables IIoT

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HIGHER PERFORMANCE/EARLY FAILURE

INDICATION

ENERGY/PROCESS EFFICIENCY

BUSINESS ENTERPRISE

INTEGRATION

ASSET OPTIMIZATION PROCESS OPTIMIZATION BUSINESS OPTIMIZATION

1 2 3

Benefits of the IIoT.

All the factory/process data is online (cloud), so software analysis can help with asset optimization, then process optimization, and eventually business optimization.

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1) Cloud Storage

2) Cloud computing

Hub

Sensor Node

Actuator

Communication Unit

Mobile Application

HW- Concept and Problem Statement

Local Analytic Engine

Sensor

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Fields Sensors Distributed PLCs Robotic Manipulator

IoT big data platform

Global CloudGlobal Cloud

Knowledge Knowledge

Storage

The goal of this project is to combine ubiquitous and heterogeneous sensing, smart objects,semantic, big data and cloud computing technologies in a platform enabling the Internet ofThings process consisting of continuous iterations on data ingestion, data storage,analytics, knowledge generation and knowledge sharing phases, as foundation for cross-border information service provision.

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Top 10 algorithms in data mining

The 10 algorithms identified by the IEEE International Conference on Data Mining (ICDM)

“Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classifcation Task”, Nesma Settouti, Mohammed El Amine Bechar and Mohammed Amine Chikh International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, Nº1, 2016 13

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Hardware Play a Major Role:Support from software platform for IoT implementation : software frameworks, platform, libraries and components, which range from :

The hardware components do play a major role in IoT enablement due to the following things.

Hardware components play an important role in processing life cycle in terms of speed and performance. IoT decision making process is as local as it is centralized. This means that IoT processing has to depend on decision making in two distinct places. -One in a centralized Cloud repository-A localized decision making process that predicts the events that takes a quicker decision much - before a centralized cloud repository could realize them.

Unlike Big Data processing which is one way , i.e. from source ‘on premise' location towards cloud, IoT processing is Bi-directional which means the origin of the source of data has to constantly receive the information back from decision makers and act on it. Due to the increased security fears associated with IoT in todays world, most organizations won't be comfortable with a reverse flow of decision from Cloud back to the devices directly, rather would be comfortable with an intermediary hardware that is fully controllable at the source of data and where the decisions from the cloud server are handled and processed.

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Google supercharges machine learning tasks with TPU custom chip

Street View, Maps and navigation AlphaGo Inbox Smart Reply, Voice search

Tensor Processing Unit board (TPU)

TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation. Because of this, we can squeeze more operations per second into the silicon, use more sophisticated and powerful machine learning models and apply these models more quickly, so users get more intelligent results more rapidly. A board with a TPU fits into a hard disk drive slot in our data center racks.

For TensorFlow and Cloud Machine Learning

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Gforce –G80 Quadro -K6000

Tesla C2050

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Example Local Analytic Engine (The Parallella Computer: Adapteva)

• 18-core credit card sized computer• #1 in energy efficiency @ 5W• 16-core Epiphany RISC SOC• Zynq SOC (FPGA + ARM A9)• Gigabit Ethernet• 1GB SDRAM• Micro-SD storage• Up to 48 GPIO pins• HDMI, USB (optional)• Open source design files• Runs Linux• Starting at $99

a scalable array of simple RISC processors programmable in bare metal C/C++ or in a parallel programming frameworks like OpenCL, MPI, and OpenMP.

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FPGA ACCELERATED COMPUTING

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IoT Analytics : Advanced Object Tracking

• Camera networks are already installed across cities and large production lines providing live video streams.

• IoT analytics can be used to track objects in real-time video streams, across extensive camera networks.

• 3D tracking and hand-over between cameras is made possible using camera network topology determinations and accurate geo positioning.

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IoT Analytics : Big Data Anomaly Detection Engine-intro

• Increased amount and variety of sensors deployed across various IoT applications signifies challenges.

• How can we process, quickly and accurately, exceptionally large volumes of disparate data from different data types.

• How can it be learnt what is normal, and detect abnormal events in big data in real time.

• The analytics engines will answer these.

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IoT Analytics : Mobility Pattern Analytics

• Many devices connected to the Internet of Things can determine and report their own locations.

• They use GPS, RFID or Bluetooth beacons to report.

• Mobility pattern analytics enable insights from these types of mobility traces through learning and automatic identification of characteristic mobility patterns.

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IoT Analytics : Modern Behavioral Learning and Prediction

• New concept behavioral analytics automatically extract typical behavior patterns in a building from sensor data, and correlates them with external influencing factors (e.g., project deadlines; VIP visits; holiday seasons).

• Once learned, these patterns are used to continuously monitor sensor data and automatically detect external influencing factors on the facility

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The system integration challenge in MicroPLCs is evident here where analog and digital components are visible on system boards

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Distributed Control with Wireless/Wireline

• Communication range (Coverage Area)

• Network capacity (maximum number of nodes in a network)

• Battery lifetime or low power

• Robustness to interference

• One-way vs two-way communication

• Network architecture

The critical factors in a IoT wireless communication

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DDS-compliance provides Vortex with unique advantages that enable it to be the most scalable and secure data-connectivity infrastructure for the IoT. Via Gateway, Vortex also seamlessly integrates telemetry protocols such as MQTT or CoAP

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Connext DDS does not require any centralized message brokers, services or servers. This makes it easy to embed and deploy while eliminating bottlenecks and single points of failure.

• Integration “glue” for IIoT applications spanning data centres

to edge sensors

- Creates virtual, decentralised global data space abstraction

- Excellent performance with real-time guarantees

- Proven-interoperable products from

multiple vendors

- Available for safety-critical

systems to DO-178C Level A

- Integrated security framework

- Fine-grained access control

- Highly scalable

- Proven in multiplemission-critical applications

Data Distribution Service

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Approved DDS Standards

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Real-Time Innovations, Inc

(Real-Time Publish-Subscribe)

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RTPS protocol was developed by Real-Time Innovations, Inc. as wire protocol for Data Distribution System.

The RTPS protocol is designed to run over an unreliable transport such as UDP/IP

The Real-Time Publish-Subscribe (RTPS) Wire Protocol provides two main communication models: the publish-subscribe protocol, which transfers data from publishers to subscribers; and the Composite State Transfer (CST) protocol, which transfers state.

RTPS takes advantage of the multicast capabilities of the transport mechanism, where one message from a sender can reach multiple receivers.

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RTI Connext® DDS Professional is the first connectivity platform designed for the demanding requirements of the Industrial Internet of Things (IIoT).

Trend Micro Security Predictions for 2017

TM predict a 25% growth in ransomware families

Key barriers in adopting the Industrial Internet

What are the greatest barriers inhibiting business from adopting the industrial Internet?

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Gartner 2016

What are the three most important actions the IT industry (e.g., hardware, software and serviceproviders) can take to help accelerate the adoption of the Industrial Internet?

Top actions for the IT industry

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Gartner 2016

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IIC’s reference model for industrial analytics covers most of the bases

Multi-tiered approach Sensing vs Actuating Different time horizons Open vs Closed loop

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OMG: Object Management Group (consortium)

• One of the most successful forums for creating open integration standards in the computer industry- Middleware platforms (DDS, CORBA & related specs)- Modelling platforms (UML, BPMN, SysML & related work)- Systems Assurance (SACM, DAF for SSCD ...)- Vertical domain specifications (C4I, Robotics, Healthcare ...)

• Member-controlled industrial consortium

- Both vendors and users

- Not-for-profit

• Interfaces freely available to all

- Visit http://www.omg.org

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Worldwide Membership

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The mathematical term well-posed problem stems from a definition given by Jacques

Hadamard. He believed that mathematical models of physical phenomena should have the properties that A solution exists The solution is unique The solution's behavior changes continuously with the initial conditions.

Problems that are not well-posed in the sense of Hadamard are termed ill-posed.

Well-posed problem VS ill-posed problem

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0222222222 kjzhygxfxzeyzdxyczbyax

khygxdxybyaxyxfz 222),( 22 Explicit Form

Implicit Form

Level of analytic and control theory

-Bang–bang control

-Classical control theory-Proportional control (PID)-Neural network / Fuzzy Logic control/ SVM

-Rule based / Knowledge base control-Expert systems control

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Deployment Models

The Predix Machine software can be deployed in three ways

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Predix Time Series

Streaming Ingestion

Efficient storage

Indexing the data for quick retrieval.

Guaranteed data processing

Highly available and scalable.

Millisecond data point precision

Support for String and Numbers

Secured Access

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Predix Time Series Architecture

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Support Interpolation

Aggregations (percent, avg, sum,

count)

Filter by Attributes, Quality and Value

Support for Limit and Order By

Both GET and POST to retrieve data

points

Sub-second query performance

Predix Time Series API

Sample{

"tags": [{

"name": ["WIND_SPEED"],

"filters": {"attributes": {

"farm":["CA"]}

},"limit": 1000,

"groups": {}

}]

}

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Windowing Support

• Application window • Sliding/Tumbling Window• Checkpoint window• No artificial latency

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End