Designing Real-Time, Reliable and Efficient Cyber-Physical Systems for Future Smart City

Transcription

1 Designing Real-Time, Reliable and Efficient Cyber-Physical Systems for Future Smart City Cyber-Physical Systems: Integration of computational algorithms and physical processes Deployed in various areas, e.g., automobile, healthcare, manufacturing, transportation, energy and etc. Our Focus 1 Wireless Networked Sensing and Control 2 Intelligent Transportation Systems 3 Electric-Vehicle-Integrated Smart Grid Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 1/ 20

2 Wireless Networked Sensing and Control(WNSC) Wireless Networked Sensing and Control(WNSC) Deployed in Many Mission-Critical CPS Applications Wireless Sensor Networks: communication infrastructure of WNSC In-Network Processing: reduce data traffic flow in WNSC Challenges a) Stringent QoS Requirement; b) Resource- constraint; c) Dynamic environment. To cope with these challenges, we investigate Joint optimization between In-Network Processing and QoS Real-time packet packing scheduling Optimal network-coding-based routing Figure source: environment.ucla.edu Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 2/ 20

3 Wireless Networked Sensing and Control(WNSC) Packet Packing and Network Coding S 0.9 A1 A2 A T Packet Packing Scheduling: tpack Network- Coding- Based Rou:ng: ONCR NetEye Sensor Testbed@Wayne State University Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 3/ 20

4 Wireless Networked Sensing and Control(WNSC) ONCR: Optimal Network-Coding-Based Routing Protocol Reliability Delivery Cost Goodput Rou4ng Diversity Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 4/ 20

5 Intelligent Transportation Systems Intelligent Transportation Systems A Smarter and Safer Transportation Network Dedicated Short Range Communication(DSRC): communication infrastructure specified by U.S. DoT Challenges: Dynamic Channel Under High Mobility Severe Broadcast Storm To cope with these challenges, we explore the correlation between transmission power and data rate during broadcast vehicle s data preference when collecting safety-data Figure source: Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 5/ 20

6 Intelligent Transportation Systems Online Control Approach of Power and Rate (OnCAR) Online Control Approach of Power and Rate (OnCAR) Adaptively controls transmission power and data rate of DSRC Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 6/ 20

7 Intelligent Transportation Systems Online Control Approach of Power and Rate (OnCAR) VSmart: DSRC-Enabled Smart Vehicle Testbed Radio control, robot control, measurements Laptops or tablets as in- vehicle CPU Sensor data DSRC messages irobot Create as vehicles USRP B210 boards as DSRC radios Movement commands Radio setting adjustments Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 7/ 20

8 Intelligent Transportation Systems Online Control Approach of Power and Rate (OnCAR) OnCAR in VSmart: Adaptive Cruise Control Leader sends movement command via DSRC Baseline DSRC: 4/10 commands received Qiao Xiang (McGill) Follower repeats the movement OnCAR DSRC: 10/10 commands received Senseable City Lab, MIT 05/07/2015 8/ 20

9 Intelligent Transportation Systems Data Preference: A New Perspective of Safety Data Dissemination PVCast: A Packet-Value-Based Dissemination Protocol Vehicles have preferences when collecting safety data: Spatial preference: closer over farther; Temporal preference: newer over older; Type preference: emergency over routine. Quantify these preferences on a per-packet level Packet Value = Spatial Value Temporal Value Type Value. A new packet p Packet Value Update PV(p)=0 1- Hop Dissemina7on U7lity Computa7on Discard packet Fail Broadcast Conten7on Window Size Assignment Probabilis7c Broadcast Test Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 9/ 20

10 Intelligent Transportation Systems Data Preference: A New Perspective of Safety Data Dissemination PVCast: a Packet-Value-Based Dissemination Protocol Throughput Delay Coverage Emergency Throughput Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

11 Electric-Vehicle-Integrated Smart Grid Electric-Vehicle-Integrated Smart Grid Intersection of Smart Energy and Transportation Systems Challenges a) Unpredictable supply and demand; b) Limited informa7on exchange; c) Lack of market mechanism. To cope with these challenges, we develop demand-response-based optimal operation strategy for commercial EV charging stations online auction framework for EV park-and-charge Figure source: Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

12 Electric-Vehicle-Integrated Smart Grid Green Revenue: Demand-Response-Based Charging Station Green Revenue: Demand-Response-Based Charging Station EV SOC: 60% EV Charging Station Renewable Energy Charging Station Renewable Energy EV Prices Decisions Sta$on 1 Choose? 15 mile, $3.15 Sta$on 2 Choose? 5 mile, $ EV EV Charging Sta$on Network EV EV Customer Charging stations are not good Samaritans. They pursue profit. GreenBroker: an online distributed operation strategy achieving an [O(V ), O(1/V )] tradeoff between customer charging delay and charging station revenue. Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

13 Electric-Vehicle-Integrated Smart Grid Green Revenue: Demand-Response-Based Charging Station Green Revenue: Demand-Response-Based Charging Station Time Average of Queue Backlog (kwh) Time Average of Queue Backlog (kwh) CF BE 1 GreenBroker 0.5 GreenBroker Delay Aware GreenBroker Delay Aware GreenBroker V V 1000 EVs: Delay 1000 EVs: Revenue Time Average of Total Revenue ($) Time Average of Total Revenue ($) x x CF BE GreenBroker 1 Delay Aware GreenBroker GreenBroker 10 3 Delay Aware GreenBroker V V 2000 EVs: Delay 2000 EVs: Revenue 4 3 Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

14 Electric-Vehicle-Integrated Smart Grid Auc2Charge: Online Auction for EV Park-and-Charge Auc2Charge: Online Auction for EV Park-and-Charge Electricity Allocation in Park-and-Charge Inefficient allocation A SOC: 20/40 B SOC: 5/25 Efficient allocation A SOC: 20/40 B SOC: 5/25 Park and Charge Park and Charge A SOC: 35/40 B SOC: 20/25 A SOC: 30/40 B SOC: 25/25 Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

15 Electric-Vehicle-Integrated Smart Grid Auc2Charge: Online Auction for EV Park-and-Charge Auc2Charge: Online Auction for EV Park-and-Charge SOC: 60% Bid 1 Lose 2-3pm, $0.50, 5kWh Bid 2 Won 3-4pm, $2.00, 9kWh... Bids AllocaKon and Pay Decision.... AllocaKon and Pay Decision Bids SOC: 30% Bid 1 Won 2-3pm, $1.50, 6kWh Bid 2 Won 3-4pm, $3.00, 8kWh... EV Customer 1 EV Customer N Existing pricing scheme could jeopardize the allocation efficiency and the social welfare Auc2Charge: An online, truthful, individual rational and efficient mechanism with social-welfare guarantee Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

16 Electric-Vehicle-Integrated Smart Grid Auc2Charge: Online Auction for EV Park-and-Charge Auc2Charge: Online Auction for EV Park-and-Charge Ratio of Offline/Online Social Welfare Average of User Satisfaction Ratio Auc2Charge OffOptimal Number of Electric Vehicles Social Welfare Ra-o: T=12 T=12 T=18 T= Number of Electric Vehicles User Sa-sfac-on Ra-o Average of Unit Payment Ratio of Offline/Online Social Welfare Number of Time Slots T=12 T=18 T=24 Auc2Charge OffOptimal Social Welfare Ra-o: 100 EVs Number of Electric Vehicles Unit Charging Payment Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

17 Future of CPS Smart City What is the Future of CPS? Smart City: A System of Many Inter-Connected CPS Figure source: holyroodconnect.com Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

18 Future of CPS Smart City Research Opportunities Exploring larger physical space in CPS design Joint Scheduling of Genera3on and Deferrable Load in Microgrid Exploring interaction between different CPS Connec&ng Intelligent Transporta&on System and Smart Grid through EV Figure sources: Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

19 Future of CPS Smart City Research Opportunities Efficient Market Mechanism Data Security and Privacy Single Microgrid Mul.ple Microgrids Mechanism design for microgrid- based electricity market Develop unified differen/al privacy solu/on for CPS data management Figure sources: ourenergypolicy.org Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20

20 Future of CPS Smart City About Me Dad: Architect Mom: Nurse Nankai University Wayne State University I am devoted to u=lizing informa=on technology to improve people s daily life. McGill University Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/ / 20