machine to machine communications
play

MACHINE TO MACHINE COMMUNICATIONS MWC, Barcelona. February 2011 - PowerPoint PPT Presentation

Nov 24, 2023 •159 likes •426 views

MACHINE TO MACHINE COMMUNICATIONS MWC, Barcelona. February 2011 Connecting Things Cluster Home for Future Internet activities in ETSI M2M is the key eHealth (domain) CLOUD (enabler)

  1. MACHINE TO MACHINE COMMUNICATIONS MWC, Barcelona. February 2011

  2. Connecting Things Cluster ����������������� Home for Future Internet activities in ETSI “M2M is the key” eHealth (domain) CLOUD (enabler) Wireless Factory (application) RFID (identification) All things SMART (domain) 2

  3. M2M motivators ����������������� Mobile market becomes saturated � 4,9 Billion people already connected (7,08 Billion population) ─ Market is highly competitive ─ High investments, and high churn, Connected machine market (green field) � 50* Billion connectable machines ─ Market is open to develop ─ Low investment, high return ─ Very little churn * Harbour 3

  4. The ETSI M2M Vision ����������������� Standards M2M Horizontal re-use Service Platform Multi – End to End Application Technology M2M Service Agnostic Capabilities 4

  5. M2M Applications ����������������� Fleet Management Service & Maintenance • Cargo tracking Telemetry • Industrial machines • Stock management • Utility meters • Vending machines • Temperature control • Parking meters • … • Route planning • Industrial meters • Order tracking • Elevators Security & Surveillance • Vehicle diagnostics • Vending Machines • Public surveillance • Asset monitoring • Asset monitoring • Congestion and Telematics / Vehicle movement monitoring • Navigation • Urban management • Traffic / weather info • Road safety • Vehicle diagnostics Sales & Payment • Location services Home Applications • POS terminals e-Health Applications • Vending machines • Heating control • Patient monitoring • Electrical appliances • Gaming & • Remote diagnostics • Alarms and security entertainment • Activity monitoring • Surveillance cameras • Lifestyle suggestions • Garage & garden • Personal security 5

  6. M/441 Smart Metering Mandate ����������������� � In March 2009 the European Commission issued the mandate to the three European Standards Organizations: CEN, CENELEC and ETSI. � Objective: build standards to enable the interoperability of European smart meter deployments. � Very aggressive schedule of 30 months; • Gap analysis of existing standards, • Develop new smart metering standards, • ETSI M2M coordinate the ETSI work on ICT for smart meters, • Part of the eventual Smart Grid work. 6

  7. Application #1 Smart metering ����������������� • Remote Meter • Consumption M2M use the Smart Meter profile reading, info, as ‘proof of concept’ for the M2M • Consumption • Fault details, service platform in Rel-1 management, • Threshold • Pricing info. management. 7

  8. The ETSI M2M Challenge ����������������� Existing M2M solutions are highly fragmented and typically dedicated to a single application (e.g. fleet management, meter reading, vending machines). Multitude of technical solutions and dispersed standardization activities result in the slow development of the global M2M market. Standardization is a key enabler to remove the technical barriers and ensure interoperable M2M services and networks 8

  9. Inverting the pipes ����������������� Multiple applications share common Existing proprietary infrastructure, data and resources ‘stand alone’ vertical applications M2M M2M Business M2M Business M2M Business Business Application 1 Application 2 Application n Application Application Infrastructure Network Infrastructure Existing ICT Infrastructure Converged Network IP & mobile infrastructure Gateways / concentrators Dedicated Devices M2M M2M M2M Device Device Device Source: HP 9

  10. Key M2M Elements ����������������� M2M Device • Device capable of replying to request for data contained within those devices or capable of transmitting data autonomously. M2M Area Network (Device Domain) • Provide connectivity between M2M Devices and M2M Gateways, e.g. personal area network. M2M Gateway • Uses M2M capabilities to ensure M2M Devices inter-working and interconnection to the communication network. M2M Communication Networks (Network Domain) • Communications between the M2M Gateway(s) and M2M application(s), e.g. xDSL, LTE, WiMAX, and WLAN. M2M Applications • Contains the middleware layer where data goes through various application services and is used by the specific business-processing engines. 10

  11. Simple M2M Architecture ����������������� M2M Application M2M Area Network M2M Service Core Capabilities M2M Gateway Client Application 11

  12. … based on existing Technologies ����������������� M2M Area Networks M2M Application Core Networks M2M Area Network • PLC • 3GPP (GPRS, EPC) • SRD • ETSI TISPAN • UWB • ATTM • ZigBee Smart Energy • NGN • M-BUS M2M • Wireless M-BUS Service • IEEE 802.15 Core Capabilities • Smart User Smart User M2M Gateway Smart Health Access Networks Client • xDSL Application • Hybrid FiberCoax • PLC • Satellite Smart Transport • GERAN, UTRAN, eUTRAN • W-LAN • SRDs • UWB • WiMAX 12

  13. M2M standards landscape ����������������� ���� ������������ ��� ������������ ����������� �������������� ������������� ��������� � ������� ������� �������� ���� �������������������� ���� ������������ ������ Source: ALU 13

  14. M2M high level system overview ����������������� �������� ������������� Smart Metering Application �������������� ��������� (including Smart Metering Data Modelling) ������ ���� ��������� �� ����������� ���� ���� �! ������ ��������� #$� ������������ �"���� #���������� "��� ������������ �� ETSI M2M Service Capabilities ��� #$% ������������ WAN (Wide Area Network) &���%�����'�����������%��%�� ()����*$������$+*,��� ()����*$������$+*,��� ����������� �������������� ��� ���� ������������� Service Capabilities ����)�����- ��������� � ������ ������� Local M2M ��������� Network ���� M2M Service �������������������� Capabilities �������������#��� ����������� ���� ������������ ������ &���%�����'�����������%��%����%� ���� ���.��������������/��0����1+���1+�+0+1�� ���� �0*��2��!�����3&����4�5������ ������� ������� Source: ALU 14

  15. M2M high level system overview ����������������� �������� ������������� Smart Metering Application �������������� ��������� (including Smart Metering Data Modelling) ������ ���� ��������� �� ����������� ���� �! ������ ��������� #$� �"���� #���������� "��� ������������ �� ETSI M2M Service Capabilities #$% WAN (Wide Area Network) &���%�����'�����������%��%�� ()����*$������$+*,��� ()����*$������$+*,��� #$% ��� ���� Service Capabilities ����)�����- Local M2M Network M2M Service Capabilities %$� �������������#��� ����������� &���%�����'�����������%��%����%� ���� ���.��������������/��0����1+���1+�+0+1�� ���� �0*��2��!�����3&����4�5������ ������� ������� 15

Recommend

Machine to Machine Communications As a Service Machine-to-Machine (M2M) refers to technologies

Machine to Machine Communications As a Service Machine-to-Machine (M2M) refers to technologies

Machine to Machine Communications As a Service Machine-to-Machine (M2M) refers to technologies that allow both wireless and wired systems to communicate with other devices Its the fastest growing Telecom market today It uses the existing

412 views • 27 slides
CS 126 Lecture A1: TOY Machine Outline Introduction Toy machine Machine language

CS 126 Lecture A1: TOY Machine Outline Introduction Toy machine Machine language

CS 126 Lecture A1: TOY Machine Outline Introduction Toy machine Machine language instructions Example machine language programs Conclusions CS126 9-1 Randy Wang Brief History Leading to the Dominance of von Neumann

446 views • 33 slides
ZERO Digital Converting Machine Machine Overview The fastest Digital Converting Machine ZERO

ZERO Digital Converting Machine Machine Overview The fastest Digital Converting Machine ZERO

ZERO Digital Converting Machine Machine Overview The fastest Digital Converting Machine ZERO RO A newly designed system based on over 20 years of experience and know-how in machine development. Most packaging and display products are

500 views • 13 slides
Who are you ? How can you identify someone? Certificates, Protocols: Machine to Machine

Who are you ? How can you identify someone? Certificates, Protocols: Machine to Machine

Who are you ? How can you identify someone? Certificates, Protocols: Machine to Machine Human to Machine ? Lets have some suggestions Be creative, not necessarily computer oriented Classification of identification methods

1.37k views • 97 slides
Machine to Machine MOIST MEDIA II . MACHINE / LIFE RESEARCH & PROTOTYPE

Machine to Machine MOIST MEDIA II . MACHINE / LIFE RESEARCH & PROTOTYPE

Machine to Machine MOIST MEDIA II . MACHINE / LIFE RESEARCH & PROTOTYPE PRESENTATION PRESENTED BY Eachie Zhou Aimee Tu 01 CHATTING ROOM CONTENTS 02 WHATS THE DIFFERENCE 03 PROTOTYPE 04

467 views • 20 slides
An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning http://www.cs.iastate.edu/~cs573x/bbsilab.html Machine Learning Software Preparing Data Building Classifiers Interpreting Results Machine Learning Software

496 views • 26 slides
Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by which computers can be trained through observation, rather than being explicitly programmed. Basically, a program is given input and adjusts its

564 views • 17 slides
CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

1/22/19 CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine Learning? A Simple Task: Recognize Obama How do you program a computer to Recognize faces? Recommend movies? Decide which web

583 views • 5 slides
SIDMAR Steel Production Plant Crane A Case study of Esprit-LTR Machine 2 Machine 3 Machine

SIDMAR Steel Production Plant Crane A Case study of Esprit-LTR Machine 2 Machine 3 Machine

SIDMAR Steel Production Plant Crane A Case study of Esprit-LTR Machine 2 Machine 3 Machine 1 project 26270 VHS A Case-Study: ( V erification of H ybrid Lane 1 Machine 4 Machine 5 S ystems). Physical plant of SIDMAR Guided

537 views • 5 slides
Machine Learning for Auto Optimization What is Machine Learning? Definition: Machine

Machine Learning for Auto Optimization What is Machine Learning? Definition: Machine

Machine Learning for Auto Optimization What is Machine Learning? Definition: Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task .

396 views • 27 slides
Classifiers: Support Vector Machine 1 MACHINE LEARNING What is Classification? Female Adult

Classifiers: Support Vector Machine 1 MACHINE LEARNING What is Classification? Female Adult

MACHINE LEARNING MACHINE LEARNING Classifiers: Support Vector Machine 1 MACHINE LEARNING What is Classification? Female Adult Children Detecting facial attributes He & Zhang, Pattern Recognition, 2011 Sony (Make Believe) Training set

914 views • 80 slides
Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine Learning Introduction to Machine Learning 1 / 12 Outline 1 Nearest Neighbor Prediction 2 Complexity Considerations 3 The Voronoi Diagram 4 Overfitting

569 views • 12 slides
Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

878 views • 29 slides
Apache PredictionIO End-to-End Machine Learning Server with Apache Spark What is Machine

Apache PredictionIO End-to-End Machine Learning Server with Apache Spark What is Machine

Apache PredictionIO End-to-End Machine Learning Server with Apache Spark What is Machine Learning? What is Machine Learning? Examples of machine learning applications: Facial recognition software E-mail spam detectors Automated

302 views • 17 slides
Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their performance We use machine learning in situations where it is very challenging (or impossible) to define the rules by hand: e.g. face detection

1.05k views • 28 slides
Machine Learning @ Amazon Ralf Herbrich Amazon 6/29/17 1 Overview Machine Learning in

Machine Learning @ Amazon Ralf Herbrich Amazon 6/29/17 1 Overview Machine Learning in

Machine Learning @ Amazon Ralf Herbrich Amazon 6/29/17 1 Overview Machine Learning in Practise Probabilities Finite Resource Machine Learning @ Amazon Forecasting Machine Translation Visual Systems Conclusions

348 views • 34 slides
LK-1901A The machine ensures high-quality bartacks on eyelet buttonholes. The machine is

LK-1901A The machine ensures high-quality bartacks on eyelet buttonholes. The machine is

LK-1900A SERIES Computer-controlled, High-speed, Bartacking Machine LK-1900A-SS (T able stand is optionally available.) LK-1900A SERIES The machine is provided with an active tension mechanism which enables the electronic control of

568 views • 12 slides
Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

404 views • 26 slides
Machine learning for machine data David Andrzejewski - @davidandrzej

Machine learning for machine data David Andrzejewski - @davidandrzej

Machine learning for machine data David Andrzejewski - @davidandrzej Data Sciences, Sumo Logic Strata Conference Machine Data Track February 13, 2014 1

1.1k views • 72 slides
A Brief Introduction to Machine Learning (With Applications to Communications) Osvaldo Simeone

A Brief Introduction to Machine Learning (With Applications to Communications) Osvaldo Simeone

A Brief Introduction to Machine Learning (With Applications to Communications) Osvaldo Simeone Kings College London 11 June 2018 Osvaldo Simeone A Brief Intro to ML + Comm 1 / 126 Goals and Learning Outcomes Goals: Provide an

1.83k views • 147 slides
MACHINE LEARNING kernels 1 MACHINE LEARNING 2012 MACHINE LEARNING Kernels: Intuition How

MACHINE LEARNING kernels 1 MACHINE LEARNING 2012 MACHINE LEARNING Kernels: Intuition How

MACHINE LEARNING 2012 MACHINE LEARNING MACHINE LEARNING kernels 1 MACHINE LEARNING 2012 MACHINE LEARNING Kernels: Intuition How to separate the red class from the grey class? x 2 360 r x 1 Polar coordinates Data

1.04k views • 44 slides
Introduction to Multi-Armed Bandits and Reinforcement Learning Training School on Machine

Introduction to Multi-Armed Bandits and Reinforcement Learning Training School on Machine

Introduction to Multi-Armed Bandits and Reinforcement Learning Training School on Machine Learning for Communications Paris, 23-25 September 2019 Who am I ? . Hi, Im Lilian Besson finishing my PhD in telecommunication and machine

1.73k views • 127 slides
BBM406 Fundamentals of Machine Learning Lecture 2: Machine Learning by Examples, Nearest

BBM406 Fundamentals of Machine Learning Lecture 2: Machine Learning by Examples, Nearest

photo:@rewardyfahmi // Unsplash BBM406 Fundamentals of Machine Learning Lecture 2: Machine Learning by Examples, Nearest Neighbor Classifier Aykut Erdem // Hacettepe University // Fall 2019 When Do We Use Machine Learning? ML is

1.33k views • 110 slides
Neural Networks for Machine Learning Lecture 1a Why do we need machine learning? Geoffrey Hinton

Neural Networks for Machine Learning Lecture 1a Why do we need machine learning? Geoffrey Hinton

Neural Networks for Machine Learning Lecture 1a Why do we need machine learning? Geoffrey Hinton with Nitish Srivastava Kevin Swersky What is Machine Learning? It is very hard to write programs that solve problems like recognizing a

514 views • 48 slides

More recommend