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  1. 1 A planet of cities In 2007, for the first time in history, the majority of the world’s population — 3.3 billion people — lived in cities. By 2050, city dwellers are expected to make up 70% of Earth’s total population, or 6.4 billion people.
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  5. Smart Cities 5 Smart traffic New smart systems Safety 2012 Future Environmental Sensors Mobile Sensing Smart Energy 2020
  6. Connect our life with social infrastructure and make the life comfortable safer, eco friendly Smart City Vision Water management IT Data center Network communication Transportation Energy Water Shop Station Recycle facility Energy station FactoryFinancial institution Hotel School Hospital Public facility Office building Housing Growing City Energy Transportation Home Energy Management Smart Grid Community Energy Grid Renewable Energy Water, Environment Green Mobility Intelligent Water Smart Navigation City Management ・City Planning ・Security ・Traceability ・Management Support ・Customer Service ・Operation 6IERC documents:
  7. 7 77 Cities require smarter solutions  The systems are under increasing environmental, social and economic pressures  For sustainable prosperity, the systems need to be managed optimally  The systems need to become smarter! Not more… ...but SMARTER!
  8. 8 ‘Smart’ solutions are instrumented, interconnected and intelligent Instrumented Deep discovery, analysis and forecasting Event capture and filtering for timely response Any to any linkage of people, process, and systems Interconnected Intelligent+ + = Smart
  9. Chaotic or Ordered? Emergent, yet governed Semi-deterministic
  10. Now, what’s up? Internet-1 Internet-2 Internet-3 0 Internet-0: the Internet of Things BorrowedfromN.Gershenfeld ON THE INTERNET NOBODY KNOWS YOU’RE A LIGHT BULB!
  11. UMU Smart Building and Smart Campus Project • Smart buildings. Open Data Project. 12
  12. 13 Energy Management Power generation Energy monitoring Efficient Power Management through decision support Tele-monitoring Machines and devices monitoring Fault and anomalies detection Service management Access Control RFID personal identification Number of users per room Indoor Comfort Thermal Visual Air quality Exampleofthe ServicesProvided Smart Campus Use Case
  13. Scenario of Validation More of 30 buildings of the University of Murcia connected to City explorer
  14. UMU Smart Building and Smart Campus Project 15 15 • Smart Buildings Service: Smart Energy Control System Home Automation Module (HAM) N SMART ENERGY CONTROL SYSTEM EIBUS/X10 CAN SERIAL ZIGBEE Generated Energy Environmental Parameters Lighting level ZIGBEE EIBUS/X10 SERIAL CAN CAN NODES SENSOR NETWORKS INPUT DATA HVAC EIBUS/X10 DEVICES SERIAL COM DEVICES LIGHTS SETTING Electrical devices Consumed Energy User Interactions LOCALIZATION SYSTEM User Negotiations Time Data User Location User Identifier 1 2
  15. UMU Smart Building and Smart Campus Project 16
  16. INTRODUCTION Architecture Layers Context Ontology Information Processing Distributed Knowledge Management Actuators Sensors Interaction / Data & Events Capture BBDD Transportation Energy Efficiency Security Smart Buildings Context Data Consumption/Production Monitoring & Control Technologies Context InformationMiddleware Management Services Complex Event Publish- Subscribe Intelligent Data Processing (Filters, DBMS) Complex Event Processing (Rules, Fusion) Data Information Knowledge Services ContextProducer ContextConsumer Services Publish-subscribe PUSH-PULL Broadcasting Intelligent Service-Providing Framework Input Data Abstraction XML RDF RDF-S OWL OTHER (DAML, etc.) OCP EXTENSIONSSOUPA Open Context Platform Ontology Context Service Open Context Platform (OCP) Automation System (DOMOSEC) Home Automation Module (HAM) Scada-Web Indoor Comfort
  17. Smartdata Platform
  18. 21 Smart Campus Use Case: Energy Efficiency Usecasescenarios Considering the facilities and deployments already available in the Region of Murcia, we can focus on three examples of scenario: Scenario 1: Smart Buildings (considering the Pleiades building fully monitored and automated since their early stages of design). Scenario 2: Smart Campus (considering the Campus of Espinardo of the University of Murcia). Scenario 3: Smart Public Facilities (considering the monitoring data available and provided by the INFO partner about the energy consumption of some relevant facilities distributed throughout the Region of Murcia).
  19. UMU Use Case 03/09/2015 22 Totalservicesprovidedforenergyefficiency • Access control management. Services features: • Presence detection • Comfort. Services features: • HVAC management. • Lighting management. • Air quality monitoring. Services features: • Monitor of Environmental Sensors. • Electrical consumption monitoring in some test areas. • Info about voltage • Info about current • Info about active power • Info about reactive power • Info about energy • Energy production monitoring. • Monitoring of inverters connected to solar panels in different areas along the Campus. • Sensors involved: • Power Meters • Temperature and lux meters • Presence sensors • Actuators involved: • ON/OFF lighting • ON/OFF HVAC • Temperature set point HVAC
  20. 23 ExampleoftheScenario – Exampleofactions • Halls and corridors • Lighting control: regulating light intensity depending on presence of people and daylight (readings from luxmeters) • Offices, laboratories and classrooms • Lighting control: automated switch on/off depending on daylight (luxmeters), and presence of people (presence sensors and RFID access control). • HVAC control: regulating HVAC depending on ambient parameters (indoor and outdoor temperature/humidity), presence of people, and window open/close sensors. • Access control management. • Multimedia devices management (in classrooms). • Air quality monitoring. • Electrical consumption monitoring in some test areas. Smart Campus Use Case
  21. 24 “How to connect to the platform…” • Interfaces to connect with the platform are divided in three levels The Smart Energy Management use case includes three different levels of communication, that are Sensor Level, Gateway Level and SCADAWeb Level, each with their interfaces. The interfaces to interact with each level have been set in accordance with the load each device is able to manage. In this sense, sensors as constrained devices will support little load in contrast with the server. • Sensor Level: At this level a CoAP interface can be used to interact with the sensors. CoAP is a protocol targeted for constrained devices due to their special needs. • Gateway Level: This devices are more capable, and are enabled with both MQTT and CoAP interfaces. • SCADA Web Level: At this level supported protocols for the interfaces are MQTT, CoAP and REST.
  22. 03/09/2015 SMARTIE Project - Aveiro Meeting 25 “How to connect to the platform…” • Sensor to platform: IP sensors and actuators. • Gateways to platform: both hardware and software gateways. • SCADAweb to platform: Data Collection Software. Internet LAN EDIFICIO 1 LAN EDIFICIO 2 BACnet 4- 20mA 0-10V Modbus DALI IEEE 802.15.4g Metering RFID KNX X10 Balastr o 6LoWPA N RS232/485 iButto nCAN bus Sensor Level Gateway Level SCADAWeb Level
  23. Odin Solutions Spin-off of the University of Murcia (Spain) with more of 10 years of experience on the design and development of monitoring and control products
  24. 27 ExampleoftheScenario –DataCollectionSoftware Smart Campus Use Case
  25. 28 ExampleoftheScenario1–AutomationIPControllers Smart Campus Use Case
  26. 29 PlatformComponents - Sensors: temperature, humidity, lighting, power meter, presence sensor, RFID System, etc. - Control Panel: Smart Campus Use Case
  27. Smart Campus Use Case IntegralManagement Energyreduction
  28. 31 GraphicEditortodefineEnergySavingStrategies Rules Designer HVAC Control Lighting Control Smart Campus Use Case 23,12% of Annual Energy Saving in Buildings
  29. SMART ENERGY CONTROL SYSTEM Evaluation/Validation and Next Work Line  Impact of users implication with the system operation (understanding system feedback and through their interaction) in terms of: Changes in their behaviour Learning and adaptation of the system Energy consumption  Assessments of the system Next Work Line:  Integrate Mobile Crowd-Sensing Techniques in our mechanism for considering occupant’s devices data.
  30. 38 Smart City Applications Based on Big Data Analytics Cross-correlation between outdoor environmental conditions and indoor temperature
  31. 39 Smart City Applications Based on Big Data Analytics
  32. 40 Smart City Applications Based on Big Data Analytics
  33. 41 Smart City Applications Based on Big Data Analytics the Bayesian NN model implemented is able to estimate the indoor temperature with a mean accuracy of 0.91 oC and a mean error deviation of 0.063 oC
  34. 42 Behaviour pattern application on Energy Management A mean energy saving of 29% meanwhile comfort preferences of occupants was satisfied in the 91% of the cases.
  35. Conclusion • Definition of a platform for IoT supporting privacy and security • Deployment of Smart Building solution based on sensors and actuators • Integration of Energy Efficient Management solution based on the work of OdinS spin-off of UMU • Testbed based on 30 buildings including HVAC, lighting and other components • Integration of heterogeneous sensors in a common IP-based gateways • KNX, HVAC, Deli, CAN, propietary alarms system, etc • 6LoWPAN support for new sensors • SCADA web system for monitoring and actuation over sensors with an editor for defining interactions 43
  36. Thank you for your attention!