Designing Real-Time, Reliable and Efficient Cyber-Physical Systems - PowerPoint PPT Presentation

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 Wireless Networked Sensing and Control 1 Intelligent Transportation Systems 2 Electric-Vehicle-Integrated Smart Grid 3 Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 1/ 20

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

Wireless Networked Sensing and Control(WNSC) Packet Packing and Network Coding A 1 � 0.1 � 0.4 � 0.2 � 0.15 � S � A 2 � T � 0.9 � 0.1 � A 3 � 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

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

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: www.gm.com Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 5/ 20

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

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 Radio setting Movement commands adjustments USRP ¡B210 ¡boards ¡ ¡ iRobot ¡Create ¡ as ¡DSRC ¡radios ¡ as ¡vehicles ¡ Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 7/ 20

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

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 . Packet ¡Value ¡ 1-­‑Hop ¡Dissemina7on ¡ A ¡new ¡packet ¡p ¡Update U7lity ¡ ¡Computa7on P V ( p ) = 0 Discard ¡packet Fail Conten7on ¡Window ¡ Probabilis7c ¡ ¡ Broadcast Size ¡Assignment Broadcast ¡Test Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 9/ 20

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 05/07/2015 10/ 20

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: www.gm.com Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 11/ 20

Electric-Vehicle-Integrated Smart Grid Green Revenue: Demand-Response-Based Charging Station Green Revenue: Demand-Response-Based Charging Station SOC: ¡60% EV Sta$on ¡1 Choose? EV EV 15 ¡mile, ¡$3.15 Sta$on ¡2 Choose? Charging Station Charging Station Prices 5 ¡mile, ¡$5.00 Renewable Energy . ¡. ¡. Renewable Energy Decisions EV EV EV Charging ¡Sta$on ¡Network 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 05/07/2015 12/ 20

Electric-Vehicle-Integrated Smart Grid Green Revenue: Demand-Response-Based Charging Station Green Revenue: Demand-Response-Based Charging Station 5 Time Average of Total Revenue ($) 3x 10 6 Time Average of Queue Backlog (kWh) 10 2.5 5 10 2 1.5 4 10 1 CF − BE GreenBroker 0.5 GreenBroker Delay − Aware GreenBroker Delay − Aware GreenBroker 3 0 10 0 40 80 120 160 200 0 40 80 120 160 200 V V 1000 ¡EVs: ¡Delay 1000 ¡EVs: ¡Revenue 5 6 Time Average of Total Revenue ($) 5x 10 Time Average of Queue Backlog (kWh) 10 4 5 10 3 2 4 10 CF − BE GreenBroker 1 GreenBroker Delay − Aware GreenBroker Delay − Aware GreenBroker 3 10 0 0 40 80 120 160 200 0 40 80 120 160 200 V V 2000 ¡EVs: ¡Delay 2000 ¡EVs: ¡Revenue Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 13/ 20

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 Park and Charge A A SOC: 35/40 +15 SOC: 20/40 B B +15 SOC: 20/25 SOC: 5/25 Efficient allocation Park and Charge A A SOC: 30/40 +10 SOC: 20/40 B B +20 SOC: 25/25 SOC: 5/25 Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 14/ 20

Electric-Vehicle-Integrated Smart Grid Auc2Charge: Online Auction for EV Park-and-Charge Auc2Charge: Online Auction for EV Park-and-Charge ���������� � � ��������������� � � � � ����������� Bids ¡ d n a Bids ¡ AllocaKon ¡and ¡ n n o o K i s Pay ¡Decision a i SOC: ¡60% c c o e SOC: ¡30% l D l A ¡ y a Bid ¡1 Lose P Bid ¡1 Won 2-­‑3pm, ¡$0.50, ¡5kWh ¡ ¡ 2-­‑3pm, ¡$1.50, ¡6kWh ¡ ¡ Bid ¡2 Won Bid ¡2 Won 3-­‑4pm, ¡$2.00, ¡9kWh . ¡. ¡. ¡. 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 05/07/2015 15/ 20

Electric-Vehicle-Integrated Smart Grid Auc2Charge: Online Auction for EV Park-and-Charge Auc2Charge: Online Auction for EV Park-and-Charge 3 3 Ratio of Offline/Online Social Welfare Ratio of Offline/Online Social Welfare Auc2Charge 2.5 OffOptimal 2 2 1.5 1 1 0.5 Auc2Charge OffOptimal 0 0 100 200 300 400 500 12 18 24 Number of Electric Vehicles Number of Time Slots Social ¡Welfare ¡Ra-o: ¡T=12 Social ¡Welfare ¡Ra-o: ¡100 ¡EVs 1 0.5 Average of User Satisfaction Ratio T=12 T=12 T=18 T=18 Average of Unit Payment 0.8 0.4 T=24 T=24 0.6 0.3 0.4 0.2 0.2 0.1 0 0 100 200 300 400 500 100 200 300 400 500 Number of Electric Vehicles Number of Electric Vehicles User ¡Sa-sfac-on ¡Ra-o Unit ¡Charging ¡Payment Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 16/ 20

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 05/07/2015 17/ 20

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: www.civicsolar.com , www.gm.com Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 18/ 20