Smart City Australia

Transcription

1 Smart City Australia Slaven Marusic Department of Electrical and Electronic Engineering The University of Melbourne, Australia ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing 1

2 ISSNIP ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing Over 200 researchers Australia, the USA, Europe, Asia, South America 10 Australian universities and partner organisations 30+ Industry linkages Nationwide sensor network research infrastructure Melbourne ISSNIP SmartSantander - Experimental testbed facility The Internet of Things Initiative (IoT-i) SocIoTal 2

3 Smart City Support Infrastructure Infrastructure to underpin the IoT is being rolled out as the government funded National Broadband Network (NBN) to deliver high speed broadband to 100% of Australian premises (93% at one gigabit per second) through a mixture of FTTH, fixed wireless and satellite links. Target applications telehealth services for remote communities; elearning initiatives; intelligent transportation; smart grids. The Smart Grid-Smart City Initiative Victoria is rolling out Zigbee enabled smart meters to all premises allowing remote metering, control, flexible tariffs and energy usage feedback for consumers. 3

4 IoT applications identified by City of Melbourne Citizens Healthcare Emergency services, defence Crowd monitoring triage, patient monitoring, personnel monitoring, disease spread modelling and containment - real-time health status and predictive information to assist practitioners in the field, or policy decisions in pandemic scenarios remote personnel monitoring (health, location); resource management and distribution, response planning; sensors built into building infrastructure to guide first responders in emergencies or disaster scenarios crowd flow monitoring for emergency management; efficient use of public and retail spaces; workflow in commercial environments Transport Traffic management Infrastructure monitoring Services Water Building management Environment Intelligent transportation through real-time traffic information and path optimisation sensors built into infrastructure to monitor structural fatigue and other maintenance; accident monitoring for incident management and emergency response coordination water quality, leakage, usage, distribution, waste management temperature, humidity control, activity monitoring for energy usage management - Heating, Ventilation and Air Conditioning (HVAC) Air pollution, noise monitoring, waterways, industry monitoring

5 Early use case deployments WSN parking meter systems Public transport systems: electronic ticketing systems (Myki), real-time scheduling and timeof-arrival updates (VIcRoads; SmartBus, TramTracker, TaxiTracker). Highway electronic tolling systems together with traffic monitoring systems Infrastructure monitoring: road, rail and bridge monitoring and maintenance Consumer products and household appliances providing another medium for information access. Rural and Agricultural: Livestock monitoring; intelligent irrigation control and waterway management IoT for Smart Cities developed world, urban areas only? 5

6 Research Programs Australian Urban Research Infrastructure Network (AURIN) supplies aggregated datasets and information services for real time information sharing, MUtopia Integrated Modelling platform Creating Smart Cities through IoT ARC Linkage Project (University of Melbourne, City of Melbourne, Arup) ARC LIEF Project, research infrastructure (UniMelb, City of Melbourne, University of South Australia, Adelaide City Council, Deakin University, Queensland University of Technology, Queensland Department of Roads and Transport) Institute for a Broadband Enable Society (IBES) Participatory sensing - enabling interactive local governance. (Dept of EEE and Centre for Public Policy). 6

7 Creating Smart Cities through IoT Focus areas: Energy efficient sensing Uninterrupted collection of environmental parameters such as CO 2 concentration, temperature, humidity and noise levels. Information extraction algorithms to manage spatio-temporal data Visualization strategies to aid decision making using artificial intelligence. Autonomic resource provisioning algorithms for supporting real-time sensor network application services in Cloud computing environments. 7

8 Case study: Noise Mapping Exposure to excessive noise levels is known to negatively impact quality of life. These effects, though largely subjective, can be categorized as: Annoyance (affective emotional response) Affected concentration Communication disturbance Sleep disruption Some of these effects include: stress, anxiety contributing to mental illness; pain (at 120dB); hearing damage (at 85dB); sleep disorders, hypertension; heart diseases 8

9 Deployment Topology 9

10 Noise Monitoring - Architecture Cloud 10

11 Cloud upload End to End development

12 Noise Mapping - Results 12

13 Video Analytics: Crowd Monitoring The cost of video analytic systems comes in making them robust to real world conditions that we all take for granted. What is the issue? The developer needs to make the video analytic system intelligent enough to handle differences in lighting, depth, position of the sun, weather, etc. Challenge: Sensing (existing infrastructure) and analytics capability Informs: High density crowd flows Evacuation strategy and resource deployment Infrastructure design 350 Cameras Ref: Aralia Systems Whitepaper 13

14 Video Analytics - Objectives Existing Surveillance Infrastructure Real-time analysis, and machine learning Use in simulation of new designs Designers and Consultants - New design Develop models to predict events based on current conditions Venue managers and Emergency services - Improved crowd management

15 What would a Zero CO2 emission city look like? Precinct Information Model BIM + GIS IFC + CityGML MUtopia Integrated Modelling Platform

16 CoE on Smart Cities Vision: Technologically empower Australia to manage future urban challenges. Mission: Creation of a technological platform that closes the loop of urban design, management and living Mapping future scenarios Networked Sensing Cloud Computing and Data Management Big-Data Analytics Interpretation and Visualization Sustainable Infrastructure Design RFID Protocols and Routing Unsupervised Deep Learning Information design Smart Transportation Optical Sensors Security and Privacy Spatiotemporal analysis Georeferenced 3D platform Disaster Management MEMS and Nano Sensors Spatial Data Analysis Anomaly and event detection 3D City visualization Structural Health Monitoring Participatory Sensing

17 SmartCities for Citizens Case study: Participatory sensing: Enabling interactive local governance through citizen engagement Focus: identify and address the key hurdles impacting the uptake of this ICT enabled capability from both citizen and government perspectives. Aim: bridge the gap between the needs of local government to be able to deliver effective services on the basis of rich new information streams and the needs of citizens for a local environment that supports their activities. Challenges: (1) Citizen engagement: incentivisation, system security, privacy. (2) Governmental requirements: Data quality, integrity and reliability is necessary to meet specific needs at different levels of government. (city planners, compliance officers, councils). 17

18 Smart City Outcome Better City, Better Communities, Better Life Integration of physical and virtual world Efficient use and real-time monitoring of resources: water, energy etc Improved mobility - effective traffic congestion control Active citizen engagement in a connected city Enhanced quality of experience through assured public safety and liveability Smart tools to increase productivity for business and consumers Scalable to apply to regional, rural and underdeveloped domains that are implicitly linked to cities 18

19 The 9 th International Conference on Intelligent Sensors, Sensor Networks and Information Processing ISSNIP 2014 Singapore, April