IEEE Education Society: Reshaping the Future of Technology

1. European Erasmus Plus Projects and Certification in Cloud Computing and IoT IN-CLOUD & IoT4SMEs Projects Prof. Dr. Manuel Castro, http://www.slideshare.net/mmmcastro/ Electronics Technology Professor, UNED, Madrid, SPAIN IEEE Fellow, Sr Past President, IEEE Education Society

3. EDUCATION SOCIETY http://ieee-edusociety.org/

4. Motivation  SMEs are fundamental for European economy …  But many fail in the first 5 years  New strategic skills must be acquired to ensure survival. E.g.: Cloud computing

5. Cloud computing benefits Availability encrease Shorter time to market Flexible model Cost reduction On-demand scaling Try before buying

6. Reasons why SME don’t give the jump to the cloud  Lose control over their infrastructures  Lack of understanding of infrastructures, costs and application scenarios  ICT knowledge of users, managers and entrepreneurs

7. IN-CLOUD aims 1. Raise awareness on how the cloud can boost economic growth and innovation to:  SMEs, public administrations and Universities 2. Create VET qualifications, based on sector analysis, to give training about cloud computing

8. IN-CLOUD partners  8 partners  6 countries  Geographic coverage  6 languages

9. IN-CLOUD courses will provide …  Business and financial skills  Technical skills  Cloud project management  Contract negotiation  Security  Data integration

10. Certifications 1. Cloud Professional for Public Administrations 2. Cloud Professional for Business 3. Cloud Professional for Education 4. Cloud Technology Professional

11. Cloud Professional for Public Administrations  Unit 1: Introduction to cloud computing  Unit 2: Security basics  Unit 3: Cloud models  Unit 4: Cloud services and applications for public administrations and for citizens/community  Unit 5: Legal and technical aspects of cloud computing for public administration and for citizens/community

12. Cloud Professional for Business  Unit 1: Introduction to cloud computing  Unit 2: Security basics  Unit 3: Cloud models  Unit 4: Cloud business services and applications  Unit 5: Legal and technical aspects of cloud computing for business

13. Cloud Professional for Education  Unit 1: Introduction to cloud computing  Unit 2: Security basics  Unit 3: Cloud models  Unit 4: Cloud services and applications for education and training  Unit 5: Legal and technical aspects of cloud computing for education and training

14. Cloud Technology Professional  Unit 1: Cloud security  Unit 2: Cloud models and providers  Unit 3: Cloud architecture  Unit 4: Cloud virtualization  Unit 5: Cloud service and application development and implementation

15. Motivation  6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015, and will reach 20.8 billion by 2020. Gartner  While in 2014 just 300,000 developers contributed to the IoT, 4.5 million developers by 2020 are expected: 57% compound annual growth rate and a massive market opportunity. VisionMobile  Expected to grow from 130.33 B$ in 2015 to 883.55 B$ by 2022.  IoT market value in the EU will exceed one trillion euros in 2020

16. How many things?

17. National initiatives  USA: “Manufacturing USA” network of excellence institutes and labs to support the diffusion of the Industry 4.0 and ICT related technologies, through a PPP., with about 500 M$ public investment.  UK: national investment of 40 M£ on IoT announced in 2015, including a 10 M£ single project to realize a demonstrator to exhibit the capacity of IoT in a city region.  Germany: “Industrie 4.0” action plan sponsored at the federal level with the involvement of large industry players and technology public, with 1 B€ public investment.  France: “Industrie du Futur” reindustrialization plan and investment in technologies I4.0 , with a public commitment € 10 B€.  Italy: “Piano Nazionale Industria 4.0” launched in Sept. 2016, with 10 B€ public investments and 32 B€ private investments in 4 years.

18. Objectives Aim: Qualifying new professionals able to support the digital transformation of the European companies exploiting to the advantages offered by the IoT technology. This objective is reached by pursuing the specific objectives of:  raising awareness among European Small and Medium Enterprises of the IoT technologies and applications and of the potential benefits for their competitiveness and economical growth;  creating VET qualifications for professionals inside European Companies, enhancing their digital competences and training them to introduce  and manage IoT technologies and applications.

19. Partnership

20. Certifications 1) Iot Decision Maker 2) IoT with microcontrollers 3) IoT with microprocessor 4) IoT Data Analyst

21. 1. Iot for Decision Makers 1.1) IoT technology 1.2) IoT business stragegy 1.3) Overview of data analysis 1.4) Legal aspects 1.5) Basics of networking and security

22. 2. IoT with microcontrollers: Arduino 2.1) Introduction (technology + business) 2.2) Device architecture and sensors for microcontrollers 2.3) Programming microcontrollers 2.4) Platforms for microcontrollers and applications 2.5) Networking and Security (for microcontrollers)

23. 3. IoT with microprocessor: Raspberry Pi 3.1) Introduction (technology + business) 3.2) Device architecture and sensors for microprocessors 3.3) Programming microprocessors 3.4) Platforms for microprocessors and applications 3.5) Networking and Security (for microprocessors)

24. 4. IoT Data Analyst 4.1) Introduction (technology + business) 4.2) Device architecture and sensors 4.3) Networking and Security 4.4) IoT data analysis 4.5) IoT platforms

25. MOOC - Foundations to Open Education and OERs repositories IEEEx > joint effort with edX  First MOOC of the IEEE Education Society  19th August – 17th September 2017  Future actions

26. MOOC - Foundations to Open Education and OERs repositories  Week 1. Introduction to Open Education & OERs  Week 2. Repositories  Week 3. Applications to Academia & Industry  Week 4. OER Applications

28. IEEE EDUCATION SOCIETY: RESHAPING THE FUTURE OF TECHNOLOGY Internet of Vehicles and autonomous vehicles Unai Hernández Jayo Unai.hernandez@deusto.es June 2017 Mobility Unit, DeustoTech

29. 2 Faro (Portugal). June 2017 Unai Hernández Jayo Motivation Context • Thanks to the concept of Internet of Things (IoT), all kind of devices, stuffs, etc. can be interconnected, “without” human interaction

30. 3 Faro (Portugal). June 2017 Unai Hernández Jayo Purpose of IoT • “The purpose of the IoT is to give humans superpowers” (Tim O’Reilly and Cory Doctorow) – Produce and consume information in real time – Automatic response of “the system” according to the environment – Humans provide intelligence to the devices

31. 4 Faro (Portugal). June 2017 Unai Hernández Jayo New paradigms • Smart cities

32. 5 Faro (Portugal). June 2017 Unai Hernández Jayo New paradigms • Industry 4.0

33. 6 Faro (Portugal). June 2017 Unai Hernández Jayo New paradigms • Smart Grid

34. 7 Faro (Portugal). June 2017 Unai Hernández Jayo IoT combines a lot of concepts

35. 8 Faro (Portugal). June 2017 Unai Hernández Jayo Then… Imagine that vehicles could talk

36. 9 Faro (Portugal). June 2017 Unai Hernández Jayo Internet of Vehicles • The Internet of Vehicles (IoV) is the convergence of the mobile Internet and the Internet of Things. • Comprising new and current vehicles, either fitted or integrated with RF equipment. • Vehicles already have: – Human machine interface – Sensors – Actuators

37. 10 Faro (Portugal). June 2017 Unai Hernández Jayo IoV and ITS • Although the IoV is a fashion concept, Intelligent Transport Systems (ITS) combine all the technologies that have been included in this term

38. 11 Faro (Portugal). June 2017 Unai Hernández Jayo What is the IoV? • IoV technology refers to dynamic mobile communication systems that communicate between vehicles and public networks using V2V (vehicle‐to‐ vehicle), V2I (vehicle‐ to‐Infrastructure) and V2P (vehicle‐to‐ Pedrestians) interactions.

39. 12 Faro (Portugal). June 2017 Unai Hernández Jayo What is the IoV? • It enables information sharing and the gathering of information on vehicles, roads and their surrounds. Moreover, it features the processing, computing, sharing and secure release of information onto information platforms It could be a distributed or centralized architecture

40. 13 Faro (Portugal). June 2017 Unai Hernández Jayo ITS station architecture

41. 14 Faro (Portugal). June 2017 Unai Hernández Jayo ITS architecture

42. 15 Faro (Portugal). June 2017 Unai Hernández Jayo Cooperative autonomous vehicles

43. 16 Faro (Portugal). June 2017 Unai Hernández Jayo Autonomous driving levels Source: https://iq.intel.com/autonomous‐cars‐road‐ahead/

44. 17 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: autonomous or cooperative • Isolated autonomous vehicles:

45. 18 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous or cooperative • Isolated autonomous vehicles:

46. 19 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous or cooperative • Isolated autonomous vehicles:

47. 20 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous or cooperative • Advantages of isolated autonomous vehicles: – Without the need for a driver, cars could become mini‐ leisure rooms – Increase auto‐safety? – Travelers would be able to journey overnight and sleep for the duration – Reduced or non‐existent fatigue from driving? – Speed limits could be increased? – Reduction in insurance premiums for car owners? – Efficient travel also means fuel savings – (…)

48. 21 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous or cooperative • Connected and Autonomous Vehicles: combination of IoV + Autonomous Vehicles. Source: http://www.4erevolution.com/en/la‐meteo‐prochain‐secteur‐a‐etre‐uberise/

49. 22 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous or cooperative • Connected and Autonomous Vehicles: combination of IoV + Autonomous Vehicles. Source: http://safecarnews.com/australia‐stablishes‐connected‐vehicle‐network‐c‐its‐by‐2017//

50. 23 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous or cooperative • Advantages of connected and Autonomous Vehicles: – Public safety – Traffic management – Traffic coordination – Traffic information support – Emission reduction – New business models – (…) Source: http://safecarnews.com/australia‐stablishes‐connected‐vehicle‐network‐c‐its‐by‐2017//

51. 24 Faro (Portugal). June 2017 Unai Hernández Jayo So… what could be the future?

52. Thanks for your interest

53. Commercial Of-The-Shelf (COTS) Wearables Supporting Self-Quantification Manuel Caeiro Rodríguez UNIVERSITY OF VIGO TELEMATIC ENGINEERING DEPARTMENT

54. SNOLA 2

55. SNOLA

56. VIRTUAL CITY ENVIRONMENT FOR ENGINEERING PROBLEM BASED LEARNING 2014-2015 http://ecity-project.eu VIRTUAL CITY ENVIRONMENT FOR ENGINEERING PROBLEM BASED LEARNING 2014-2015 http://ecity-project.eu THIS PROJECT HAS BEEN FUNDED WITH SUPPORT FROM THE EUROPEAN COMMISSION. THIS PUBLICATION REFLECTS THE VIEWS ONLY OF THE AUTHOR, AND THE COMMISSION CANNOT BE HELD RESPONSIBLE FOR ANY USE WHICH MAY BE MADE OF THE INFORMATION CONTAINED THEREIN. © eeGeo Virtual City Design and manage your own city Problem-based Learning 8 scenarios avaliable to engage into engineering

57. eCity Results: a set of 8 problems Energy Distribution Renewable Energies Internet Server Providers Mobile Connection Earthquake Protection Flood Protection Pollution Public Transportation

58. Wearables 6

59. COTS Wearables 7 Source:IDC

60. Wearables Variety: Wrist (186) 8 Source:IDC

61. Wearables Variety: Head (81) 9 ● Google Glasses ● Snapchat Spectacles ● Microsoft Hololens ● Sony Playstation VR Source vandrico.com/wearables

62. Wearables Variety: Ear (12) 10 ● Jabra Elite Sport Source vandrico.com/wearables

63. Wearables Variety: Feet (13) 11 ● IDM Perform ● Kinemaitx Tune Source vandrico.com/wearables

64. Wearables Variety: Arm (10) 12 ● Shot Tracker ● Qardio Qardioarm Source vandrico.com/wearables

65. Wearables Variety: Chest (20) 13 ● Polar H7 ● Monbaby Source vandrico.com/wearables

66. Wearables Variety: Other Locations 14 ● Ankle (4) ● Back (1) ● Body (10+39) ● Fingers (9) ● Legs (14) ● Neck (11) ● Pelvis (2) ● Shoulders (3) ● Thighs (2) ● Waist (10) Source vandrico.com/wearables

67. Wearable Applications 15 Lifestyle Medical Industrial Entertainment Fitness Gaming

68. Self-quantification 16

69. Wearables and Self-quantification 17 When do you wear your wearable? ● 40% of wearable users say they feel naked when not wearing them ● 25% even sleep with them Wearables are the most personal devices Source Ericson Consumerlab Wearable Technology and Internet of Things, 2016

70. COTS Wearables: Problems and Issues Whom to share personal data? ● 50% of wearable users share data from wearables online ● 67% are open to sharing data with third party entities provided its anonymous ● 70% perceive wearable manufacturers to be serious in protecting their wearable data 18 Source Ericson Consumerlab Wearable Technology and Internet of Things, 2016 Wearable Manufactures as Personal Data Brokers

71. Wearable Applications: Education 19 Education Education ● Two different kinds of projects o As educational tools o As quantification supports to feature students  Sleep  Stress

72. Objectives ●Collecting data: different solutions ●Homogenization: data models, reference points, sensors (Heart Rate, Skin Temperature, Galvanic Skin Response, Accelerometer). ●Proposing indicators for an academic environments: o Sleep indicators: sleep quality, sleepiness, chronotype, sleep sensitivity and sleep regularity. o Stress indicators: snapshot, accumulated, latent and variability. 20

73. COTS Wearables: Problems and Issues How to collect Data? ● Wearables are still tethered to the smartphone via Bluetooth and follow a walled garden approach ● 4 data transfer mode o Warehouse vs. wearable transfer o Direct vs. indirect access 21

77. COST Wearables Data models: ●Data integration problems oDifferences in the names oDifference in the beginning of measurements oDifferences related to the sensors available. ●Lack of in accuracy sensors 25

78. Sleep indicator ●Sleep apps 26

79. Sleep indicator ●Chronotype 27 Type Start Bed Time Values End Bed Time Values Extreme morning -/21:30 2 -/05:00 2 Moderately morning 21:30/22:45 1 05:00/06:30 1 Intermediate 22:45/00:45 0 06:30/08:30 0 Moderately evening 00:45/02:00 1 08:30/10:00 −1 Definitely evening 02:00/- 2 10:00/- −2

80. Proposal for education 28

81. 29

84. Proposal for education ●Services for teachers/developers 32 ● Moderately morning ● Traditional evaluation ● Most stressful activities: 3.2, 4.1, 5.2 ● No problems detected ● Moderately evening ● Continuous evaluation ● Most stressful activities: 2.2, 4.1, 5.2 ● Irregular sleep quality warning ● Latent stress high

85. Conclusions ●Problems in wearables oLow battery duration oInteroperability problems oSensors precision oTethered to smartphones ●Opportunities oApplication in new environments oFast evolution 33

86. Thanks for your attention. 34 mcaeiro@gist.uvigo.es farriba@uvigo.com The images shown in this presentation have been used without any commercial purpose

87. Reconfigurable Computing A new computing paradigm Manuel G. Gericota Department of Electrical Engineering School of Engineering – Polytechnic of Porto & Faculty of Technology, Natural Sciences and Maritime Sciences University College of Southeast Norway Kongsberg, Norway

88. Official At Last: Intel Completes $16.7 Billion Buy of Altera Barb Darrow Dec 28, 2015 Chip giant Intel has completed its $16.7 billion mega-deal to buy Altera, thus getting a big toe hold in the burgeoning market for a new type of chip that is much more flexible than the microprocessors Intel has ridden to fame and fortune. Manuel Gericota 2

89. Why and what? • Why Intel paid an astonishing $16.7 billion dollars for it? • What chips are that? • What can be better than Intel microprocessors? Manuel Gericota 3

90. The Moore’s Law “The complexity for minimum component costs has increased at a rate of roughly a factor of two per year. Certainly over the short term this rate can be expected to continue, if not to increase.” G. E. Moore, “Cramming more components onto integrated circuits,” Electronics, vol. 38, no. 8, pp. 114‐117, April 19, 1965. Manuel Gericota 4

91. Device technology • Device technology is the silicon area used by a circuit, expressed by the length of the smallest transistor that can be fabricated Manuel Gericota 5 Source: International Technology Roadmap for Semiconductors, 2011

94. Microprocessors • Computers based on microprocessors • Implement software solutions based on pure microprocessor systems • Fixed hardware → one or more generic and/or specific processors (GPU (Graphic Processing Unit), DSP (Digital Signal Processor), …) Manuel Gericota 8

95. Microprocessors • General purpose computing • 1945 ‐ John Von Neumann computer architecture • simple, fixed structure, able to execute any kind of computation, given a properly programmed control, without the need for hardware modification • Sequential instruction fetch • a program is coded as a set of instructions to be executed sequentially, instruction after instruction • In general, the execution of an instruction on a computer can be done in five cycles: Instruction Fetch, Instruction Decode, Operand Fetch, Instruction Execute, Write Back Manuel Gericota 9

96. Microprocessors • Von Neumann architecture constraints • main advantage of the Von Neumann (VN) computing paradigm • flexibility → it can be used to program almost all existing algorithms • algorithms must be coded according to the VN rules → ‘The algorithm must adapt itself to the hardware’ • temporal use of the same hardware for a wide variety of applications → VN computation is often characterized as ‘temporal computation’ • all algorithms must be sequentially programmed  many algorithms cannot be executed with their potential best performance Manuel Gericota 10

97. Microprocessors Manuel Gericota 11 Frequency (GHz) 41% /year Source: Semiconductor Industry Association ‐ International Technology Roadmap for Semiconductors, 2011 • Microprocessors’ frequency of operation is no longer increasing due to • limits on switching speed • limits on dissipated power • limits on device scaling due to leakage current

98. The big question • The big question • how to increase performance without increasing frequency? • The small answer! • parallelism → multiple core processors • Extra cores on a single processor  multiplies the amount of data that could be handled by the CPU • Adding more CPU cores never results in perfect scaling Manuel Gericota 12 Intel core i9 18 cores

99. Amdahl’s Law • The performance benefit of parallelism is limited by the amount of the application that must run serially, e.g. is not or cannot be parallelized • relevant if the microprocessor is running several applications at a time • unfitted to accelerate single applications Manuel Gericota 13 Gene Amdahl (1922‐2015)

100. Multicore constraints • The software must be written to support multithreading • if not, threads will be primarily run through a single core thus degrading the efficiency • Thermal restrictions  cores will be running at lower speeds • Sharing the same system bus and memory bandwidth limits the performance improvement • DRAM speed limits memory access • CPU clock vs DRAM latency is increasing • Multicores in a single chip drive production yields down Manuel Gericota 14

101. Microprocessors • In sum… • microprocessors are running out of steam • Moore’s Law collapsed due to heat and leakage issues • DARK SILICON → portions of a device that need to be shut down in order to avoid overheating Manuel Gericota 15

102. DARK SILICON Manuel Gericota 16 DARK SILICON trends for different technology nodes Source: M. Shafique, S. Garg, J. Henkel, D. Marculescu, "The EDA challenges in the dark silicon era," in 51st ACM/EDAC/IEEE Design Automation Conference (DAC), 2014, pp.1‐6

103. Heterogeneous Computing • More than one type of processor is used to perform specialized processing capabilities • e.g. graphics rendering system = CPU + GPU • GPU to render 3D scenes and perform mathematically intensive computations on large datasets • CPU to perform operating system and data networking tasks • CPUs are reaching performance limits & GPUs are difficult to program + they still use the Von Newman approach  remain sequential machines  performance is limited Manuel Gericota 17

104. Application‐Specific Processors (ASIPs) • Processor tailored to run one application only • the processing unit is designed and optimized for that particular application • the hardware ‘adapts’ itself to the application • instruction cycles are eliminated • instruction set of the application directly implemented in hardware • input data stream in the processor through its inputs → the processor performs the required computation → results collected at the outputs • ASIP = Application‐Specific Integrated Circuit (ASIC) Manuel Gericota 18

105. In an ideal world • Ideally, we would like to have the flexibility of the general purpose processors + the performance of the application‐specific processors in the same device Manuel Gericota 19

106. Manuel Gericota 20

107. Field‐programmable devices • The most versatile non‐ASIC technology • An array of prefabricated generic logic cells and a general interconnect structure both programmable • programmable semiconductor “switches” open‐ or short‐circuited • customization is done by configuring the device with a specific data file Manuel Gericota 21 FPGA (Field‐Programmable Gate Array) architecture

108. Exponential amounts of data • Internet of Things (IoT) • Installation of sensors everywhere • Increases in Internet connectivity • Data requires extensive processing • from the point of origin, through the cloud, into systems that perform analysis  need for a new approach to hardware Manuel Gericota 22 generate an exponential amount of data

109. Internet of Everything • In the future, cheap sensors will increasingly be attached to almost everything • 5G data connectivity = ‘ubiquitous’ connectivity  almost everything will be connected to one or more sensors and processed by the communications infrastructure Manuel Gericota 23

110. Faster switching and routing • Large‐scale data centres + cloud computing + wireless applications  high‐speed serial transceivers for fast switching and routing • dual‐mode transceivers  56 Gbps enable terabit data rates • supports both optical and copper interfaces for applications including chip‐to‐chip, backplane and direct attach cable Manuel Gericota 24

111. Ubiquitous data and security • Around 30 billion devices connected by 2020 • Handling security screening  substantial computing capabilities • FPGAs are ideally suited to performing these tasks • they operate quickly (hardware performance) • can be reconfigured when needed to upgrade security settings → can be used to process streams of data that use rapidly changing encryption or keys (software flexibility) Manuel Gericota 25

112. Project Catapult (Microsoft) • Makes extensive use of FPGAs “By exploiting the reconfigurable nature of FPGAs, at the server, the Catapult architecture delivers the efficiency and performance of custom hardware without the cost, complexity and risk of deploying fully customized ASICs into the datacentre. In doing so, [Microsoft has] achieved an order of magnitude performance gain relative to CPUs with less than a 30 percent cost increase, and no more than a 10 percent power increase.” from Microsoft’s blog Manuel Gericota 26

113. FPGAs in datacentres and search engines • Microsoft has been developing a hardware infrastructure for its Azure Cloud computing centres • The technology is also being used to accelerate the speed of Internet searches at its Bing search engine • In cases where a server has to handle a mix of workloads (making ASICs impractical), FPGAs are a very energy‐efficient, high performance solution Manuel Gericota 27

114. FPGA flexibility advantages Manuel Gericota 28 Results from a Forbes survey of more than 300 CTOs and system architects Forbes Insights – The coming data avalanche – and how we’ll handle it Forbes Insights in association with Intel ‐ October of 2016

115. Intel processors and FPGAs • Intel has introduced hybrid devices that combine Intel® Xeon® processors with an FPGA • The combination of the multiple processors on the same silicon will enable designers to create products with considerably higher performance than individual devices • Intel is simplifying the interfaces between FPGAs and other system components, and developing programming libraries  enable developers to more easily program an FPGA accelerating the development of new devices Manuel Gericota 29

116. Partial and dynamic reconfiguration • The whole Virtex series and 7‐ series, UltraScale and UltraScale+ families, including Zynq, from Xilinx, and Stratix V and Stratix 10 from Intel are partial and dynamically reconfigurable Manuel Gericota 30

117. A new computing paradigm • Reconfigurable Computing (RC) • The ability to provide a hardware platform that can be customized on a per‐application basis under software control Manuel Gericota 31

118. Reconfigurable computing Manuel Gericota 32

119. Resource sharing • Multiple applications share the same logic resources in the spatial and temporal domains  increase application performance Manuel Gericota 33

120. In the future… Will hardware implementations be as flexible as software ones !? Manuel Gericota 34

121. Manuel Castro, Unai Hernandez-Jayo, Manuel Caeiro & Manuel Gericota presented by Javier García-Zubía, Manuel Gericota Spanish and Portuguese Education Chapter Chairs IEEE Education Society Reshaping the future of technology

IEEE Education Society: Reshaping the Future of Technology

Du liest eine Vorschau.

Hier ansehen

IEEE Education Society: Reshaping the Future of Technology

Weitere Verwandte Inhalte

Diashows für Sie (20)

Ähnlich wie IEEE Education Society: Reshaping the Future of Technology (20)

Weitere von Manuel Castro (20)

Aktuellste (20)