IoT Day 2014 - Results and challenges ahead for IoT

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Presentation given at IoT Day 2014 in Trento on 9th April 2014

Presentation given at IoT Day 2014 in Trento on 9th April 2014

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  • 1. IoT Day 2014, Trento Trento at the centre of the Italian Internet of Things Connect your Things! Don’t be an alien! organised by @iotitaly hashtag for this event #trentoiot iotitaly
  • 2. iCore: smart things for smart applications Raffaele Giaffreda, IoT Day Italy, TrentoRaffaele Giaffreda, IoT Day Italy, Trento 9th April 2014
  • 3. outline • the scale of Internet of Things • the bottlenecks • some research results • some future trends• some future trends • conclusions
  • 4. some numbers • 9.3bln people on earth by 2050 (70% urban, source WHO) – 2.5bln in 1950 • increasing stress on planet resources – up to 50% world produced food goes to waste (source IMECHE – 2013) – 30% water lost in transit (source WHO and various reports)– 30% water lost in transit (source WHO and various reports) – substantial room for improvement • age 65+: 516m 2009, 1.53bln by 2050 (source US Census Bureau) healthcare issues! • future cities have no choice but to become smart...IoT gives them the opportunity to sense problems and adjust... • 50bln connected things by 2020?
  • 5. some more numbers • BOSCH: 7bln by 2015 • ABI research: 30bln by 2020 • Cisco: 50bln by 2020• Cisco: 50bln by 2020 • Morgan Stanley: 75bln by 2020
  • 6. wearables at CES2014
  • 7. substantial demands on communication infrastructure substantial demands for resource constrained IoT datafrom50blndevices infrastructure substantial demands on centralised targeted user network
  • 8. some research results pics from IoT Italy Hackathon 2013
  • 9. The Dumb IoT The Craft IoT The Cognitive IoT YESTERDAY TODAY TOMORROW from IoT Italy 2013 Bear with us, we are building it!
  • 10. An example... • “I am hot” • thermostat lowers temperature • but... • energy efficiency (target)• energy efficiency (target) • light sensor, outdoor temperature (available) • cognitive IoT system lowers the blinds instead or it shuts the windows or ... compare user intervention and automated system based on templates
  • 11. other examples • “I am going out of the house” – switch off lights, water plants, activate iRobot, close curtains... • “I am unwell”• “I am unwell” – monitor health parameters, stream data to doctor, remind me of taking medicines • moreover “social” things can discover and support each other based on relatedness
  • 12. the basics – how do things become smart? • acquire and log data • analyse it and learn patterns • create predictive models – anticipate user intentions in routine jobs (water– anticipate user intentions in routine jobs (water the plants, feed the fish, switch off lights) – produce non-expert alerts (a leak, a fire, a fault in a car etc.) how is that achieved?
  • 13. iCore Real World and System Knowledge models Real World Knowledge (RWK) Models derive patterns of ... interpret data data goldmine System Knowledge (SK) Models presence derive patterns of ... What are these good for? data goldmine
  • 14. Service Execution Domain Expert / Developer Service- requesting Actor RWK Model Adp. Hypothesis ML trained algorithm Service Request (maintain room comfort) Service Execution Request CVO observers CTRL PLANE RWK Model RED: associated with RWK modeling CHANGING THE RWK MODEL DE for SK DE for RWK Observed (and Actuated) RW Entities Observed (and Impacted) Actor Real World Sensor Source Data Source/Sink Virtualization Sensor Source Sensor Source Actuator Sink Virtual Sensor Virtual Actuator Service Execution Hut. Virtual Sensor Cor. Adp. RWK Validation CVO Execution CVO Actuate VOs DATA PLANE FB1 FB2 qr s p CVO observers EXEC PLANE BLUE: associated with CVO / VO CHANGING THE ROOM CONDITIONS SK ModelSK Validation CVO
  • 15. Some examples please? • tracking cars in a smart city • medical equipment tracking and asset management
  • 16. tracking cars in a smart city Best demo award at FuNeMS 2013 courtesy of Marc Roelands (Bell Labs – Alcatel Lucent) more info: http://www.iot-icore.eu/attachments/article/66/iCore_FuNeMS%2713_ALU.pdf
  • 17. tracking medical equipment Validate Train Execute RWO parameterreconfiguration recommendations toimprove energy efficiencyof location sensorsIn the demo implementation, Databaseof location information(spatial & temporal)of objects 3 4 5 Train efficiencyof location sensorsIn the demo implementation, locationdata of objects is simulated 1 2 2a 4a 6 7 8
  • 18. Trento Hospital S. Chiara Trilogis + ZIGPOS
  • 19. where are we heading? • connected things will be able to “socialise” their status (i.e. friends by ownership) – relevance, proximity based discovery and selectionselection • integration with existing “human” social networks – spontaneous service provisioning L. Atzori, A. Iera, G. Morabito From “Smart Objects” to “Social Objects”: The Next Evolutionary Step of the Internet of Things IEEE Communications Magazine, Jan 2014 B. Kim, T. Kim, D. Lee, and S. J. Hyun, "SpinRadar: A Spontaneous Service Provision Middleware for Place-aware Social Interactions," PERSONAL AND UBIQUITOUS COMPUTING, April 2013
  • 20. cloud and IoT for big-data “avoidance”? • tackling the other potential bottlenecks (wise use of network resources and resource constrained nature of IoT) • need to ensure robustness for IoT • low-latency services • high-interoperability• high-interoperability • use of edge cloud technology and distributed data processing Data Sources Data to Apps: Hadoop and batch processing Data Sources Apps requirements: live stream (processing “guidelines” travel opposite compared to data)CBA transmitted data sizes
  • 21. datafrom50blndevices edge processing loaded network application domainsdatafrom50blndevices network networkingcomputation
  • 22. Conclusions • Internet of Things is part of our present • efficient usage of planet Earth resources is a challenge with growing and aging population • tremendous growth potential (50bln only 3%• tremendous growth potential (50bln only 3% of all objects :-) • there is work to do
  • 23. Further info / links [REF2] P. Vlacheas, R. Giaffreda et al. "Enabling Smart Cities Through a Cognitive Management Framework for the Internet of Things“, IEEE Communications Magazine - Special Issue on Smart Cities (June 2013) [REF1] IERC April 2013 Newsletter – Foreword (see THIS LINK) [REF3] iCore website (www.iot-icore.eu/latest-news)[REF3] iCore website (www.iot-icore.eu/latest-news) Best Demo Award at FuNeMS 2013
  • 24. Thank you! Raffaele Giaffreda Smart IoT (RIoT) Research Area Head (CREATE-NET) EU FP7 iCore Project CoordinatorEU FP7 iCore Project Coordinator raffaele.giaffreda@create-net.org Websites: www.create-net.org/research/research-areas/riot www.iot-icore.eu