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Leveraging Live Data To Realize The Smart Cities Vision


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Owing to Internet of Things (IoT), the volume of Live Data is expected to grow exponentially for the foreseeable future. In this regard, a recent Cisco report (from June 2017) mentioned, in part, the following:

* There will be 3.5 networked devices per capita and 27.1 billion networked devices by 2021.
* Live internet video will make up 13% of internet video traffic by 2021.
* Live video will grow 15-fold from 2016 to 2021.
* IDC is predicting 44 zettabytes of newly created data by 2020 and 165 zettabytes by 2025.
* Virtual reality (VR) and augmented reality (AR) traffic will increase 20-fold between 2016 and 2021 globally, a compound annual growth rate of 82%.

The above highlights the dire need for a generic high throughput and ultra-low latency messaging and reactive platform which can democratize the availability of Live Data and real-time processing. To this end, Satori recently announced its Live Data platform. The platform can potentially be leveraged in a very wide variety of domains. For instance, Satori's platform can be used to help realize the promise of Smart Cities along the following fronts:

* Transportation: Fleet management, smart logistics, smart roadways, connected vehicles
* Safety: Emergency response, pedestrian and bike safety, crime forecasting, flood detection
* Environment: Energy efficiency, air quality, water management, smart street lighting

In this talk, we shall walk the audience through the architecture of Satori, its salient features and a concrete country-scale case study. In particular, Satori has partnered with the New Zealand Transportation Agency (NZTA) to deliver a Mobility as a Service (MaaS) Marketplace of smart city apps and services that reduce traffic congestion in high-growth urban areas. Satori is closely collaborating with public agencies and private transit agencies in New Zealand for NZTA's MaaS project. In collaboration with site planners, data managers, and city administrators, Satori is coalescing streaming data feeds from all transportation data sources into a single live and reactive open data channel for the entire country of New Zealand.

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Leveraging Live Data To Realize The Smart Cities Vision

  2. 2. PROJECTIONS 2 2050 2.5B increase in urban population Asia and Africa 90% increase in urban population North America, Latin America, the Caribbean, Europe Top urbanized regions 100 cities-1M people in the next 10 years Cities to be built 1950 30% of the world’s population urban 60M increase/year Urban Residents 2014 54% of the world’s population urban
  4. 4. DATA Four Quadrants DATA QUADRANTS
  5. 5. LIVE DATA Why? 5 Obviate the need for massive storage Reduce energy footprint Efficiency New Use Cases Business Opportunities React faster Speedup decision making Improve prediction Eliminating Silos
  6. 6. SMART CITIES Overview and Case Study 6 Smart Transport Car or Train or Bus, … Smart Living Energy, lighting Smart Environment Pollution, waste mgmt Smart Planning Routing, Life Organization Data Disparate sources Smart Monitoring Surveillance
  7. 7. Smart Screens NYC’s City 24/7 SMART CITY SOLUTIONS 7 Connected public lighting with smart cities Amsterdam’s Intelligent Lighting Networks cloud connecting various entities Busan Metropolitan Government IBM & Nice partnership Smart ligthing, smart circulation Collecting real-time data Chicago’s Array of Things Singapore’s Smart City Monitor everything Queenstown’s MAAS Real-time transport app
  8. 8. IOE AND SMART CITIES Move from IOT to IOE 8 • machine-to-machine (M2M) communication • smart grids • smart buildings • smart cities • person-to-machine (P2M) • person-to-person (P2P) With the world becoming more connected… P2M P2P M2M INTELLIGENCE People Data Things IoT (Internet of Things) IoE (Internet of Everything)
  9. 9. P2P:PERSONALIZATION 9 Personalized Social Billboards Advertising Social hotspots Route Recommendation Hotspot Recommendation Group Behaviors Public Transport Commute Incentivization Citizen Services Applications Clustering Matching data from devices to match people with like-minded people using clustering
  10. 10. SMART SANTANDER Case Study on IOE and Smart Cities 10 Santander is the capital of the autonomous community and historical region of Cantabria, situated on the north coast of Spain. Smart Santander ❖ In 2011, the city began “SmartSantander” to improve city operations and give residents a greater sense of involvement in the operation of the city. ❖ The City Council oversees implementation of the SmartSantander project. ❖ The equipment, including the sensors, is owned and maintained by the city. ❖ Data gathered via the system is also owned by the city but is shared widely with the general public.
  11. 11. SMART SANTANDER Case Study on IOE and Smart Cities 11 Objective ❖ Improve city operations ❖ Improve quality of life Strategy ❖ Secure leadership and support ❖ Leverage academic relationships Solution ❖ Network of > 25K sensors for monitoring ❖ Open access to data and encouraging interaction Impact ❖ 80% reduction in traffic congestion ❖ Reduction in travel times and environmental pollution
  12. 12. ROLE OF DEEP LEARNING 12 Object Detection Anomaly Detection Computer Vision Machine Translation Sentiment Analysis Topic Modeling Natural Language Processing Cost Optimization Self Driving Cars Traffic Light Control Robotics Deep Reinforcement Learning Text to Speech Audio Classification Audio Analysis ROLE OF DEEP LEARNING
  13. 13. CASE STUDY 13 Computer Vision Self Driving CarsTraffic Light Control
  14. 14. 14 Look at the same location and take pictures from two different times Which place appears safer? Map an entire city Computer Vision Approach SAFE CITIES } Live Updates Safe Car Navigation Safe Pedestrian Navigation
  15. 15. SMART BUILDINGS 15 Detect environmental and occupancy changes Adjust lighting Lighting control Use sensor and occupancy data Direct cooling or heating or ventilation Smart Aire Provide detailed, non-intrusive views of workspaces and employee movement Increase productivity, drive cost-savings Smart Space Walking around mode Dialogue mode Study mode Watching TV mode “Applications of Human Motion Tracking: Smart Lighting Control”, CVPRW 2013
  16. 16. TRANSPORTATION An Integral Component of Smart City Initiatives 16 Smart Sensing Smart Transportation Smart Cities
  17. 17. TRAFFIC ANALYSIS Congestion Control Distribute Traffic Pollution, Noise Surveillance Crime Prevention Debris/ Maintenance Capacity Planning Road Building Routing Public Transport
  18. 18. CONGESTION CONTROL 18 Emergency Control Optimization Objectives Average Trip Time Average Delay/Suffering Average Noise Average Pollution Per Inch
  19. 19. PARKING SMARTLY 19 EXAMPLES City of Valencia, Spain Sensity Systems, Sunnyvale, CA, USA BENEFITS Saving infrastructure costs Saving parking search in term reducing traffic jams OBJECTIVES Improve the efficiency in the management of parking lots Real-time visibility into the availability of parking spaces to citizens CHALLENGES Reorganizing parking space Addressing changes in traffic flow
  20. 20. SMART CITIES CHALLENGES 20 Is there a solution addressing all these challenges? Social Accepting sharing data Political A lack of shared goals Economical Reduced budgets Operational inefficiencies Technological Advances have increased data available and communication Privacy City sharing data including images, videos
  21. 21. SATORI Satori is the only live data platform that enables immediate integration, interaction, correlation, and intelligent response at high throughput and ultra-low latency. OVERVIEW A Unified Live Data Platform 21
  22. 22. 22
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  24. 24. NEW ZEALAND TRANSPORTATION Case Study 24 ✦Identify and support sustainable forms of transportation ✦Build intelligent public transportation systems based on live information ✦ Increase mobility ✦ Reducing: ✴ congestion ✴ fuel consumption ✴ gas emissions ✴ energy consumption ✦ Improve citizens lives Challenges Outcome
  25. 25. NEW ZEALAND TRANSPORTATION Command Center 25
  26. 26. NEW ZEALAND TRANSPORTATION Command Center with AI 26 ✦Traffic Routing ✦Fleet Management ✦Passenger Load Scheduling ✦Point ✦Trend ✦Spatial ✦Changepoints Transportation Anomaly Detection
  27. 27. CONTACT US 29 @FrancoisOrsini_ FRANCOIS ORSINI @arun_kejariwwal ARUN KEJARIWAL @sandraskaff SANDRA SKAFF @choudharydhruv DHRUV CHOUDHARY
  28. 28. That’s all.
  29. 29. READINGS 31 ✦ “Transforming the City of New York New Platform for Public-Private Cooperation Ushers in Smart Cities of the Future”, CISCO REPORT 2012. ✦ ✦ “France's Nice Cote d'Azur Region Taps IBM to Help Build a Smarter, Sustainable City”, SMART CITIES COUNCIL 2013. ✦ “Smart+Connected City Services Cloud-Based Services Infrastructure Enables Transformation of Busan Metropolitan City”, CISCO REPORT 2011. ✦ “Dutch port taps smart street lighting, with IoT on the horizon”, LEDs MAGAZINE 2017. ✦ “Singapore Is Taking the ‘Smart City’ to a Whole New Level”, WALL STREET JOURNAL 2016. ✦ “Choice - the new real-time transport app”, app/. ✦ “IoE-Driven SmartSantander Initiative Reduces Traffic Congestion, Pollution, Commute Times”, CISCO REPORT 2014. ✦ “Computer vision uncovers predictors of physical urban change”, PNAS 2017. ✦ “Applications of Human Motion Tracking: Smart Lighting Control”, CVPRW 2013. ✦ ✦ “Success Story: How Infopulse Applied IoT and Computer Vision to Create Two Smart Parking Solutions”, INFOPULSE 2017.