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

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

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

A1 A2 A3 S T

0.15 0.2 0.1 0.1 0.4 0.9

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

Reliability Delivery ¡Cost 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

iRobot ¡Create ¡ as ¡vehicles ¡ Laptops ¡or ¡tablets ¡ ¡ as ¡in-­‑vehicle ¡CPU ¡ USRP ¡B210 ¡boards ¡ ¡ as ¡DSRC ¡radios ¡

DSRC messages Movement commands

Radio control, robot control, measurements …

Radio setting adjustments Sensor data

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

Leader ¡sends ¡movement ¡command ¡via ¡DSRC Follower ¡repeats ¡the ¡movement Baseline ¡DSRC: ¡4/10 ¡commands ¡received OnCAR ¡DSRC: ¡10/10 ¡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 ¡ ¡Update 1-­‑Hop ¡Dissemina7on ¡ U7lity ¡ ¡Computa7on Probabilis7c ¡ ¡ Broadcast ¡Test Conten7on ¡Window ¡ Size ¡Assignment

A ¡new ¡packet ¡p Discard ¡packet

Broadcast

Fail P V ( p ) = 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

• nline 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

Charging Station Renewable Energy Charging Station Renewable Energy EV EV EV EV EV EV

Sta$on ¡1 15 ¡mile, ¡$3.15 Sta$on ¡2 5 ¡mile, ¡$5.00 SOC: ¡60% . ¡. ¡. Choose? Choose?

EV ¡Customer Charging ¡Sta$on ¡Network

Prices Decisions

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

40 80 120 160 200 10

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10

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10

5

10

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V Time Average of Queue Backlog (kWh)

CF−BE GreenBroker Delay−Aware GreenBroker 40 80 120 160 200 10

3

10

4

10

5

10

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V Time Average of Queue Backlog (kWh)

CF−BE GreenBroker Delay−Aware GreenBroker 40 80 120 160 200 0.5 1 1.5 2 2.5 3x 10

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V Time Average of Total Revenue ($) GreenBroker Delay−Aware GreenBroker 40 80 120 160 200 1 2 3 4 5x 10

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V Time Average of Total Revenue ($) GreenBroker Delay−Aware GreenBroker

1000 ¡EVs: ¡Delay 1000 ¡EVs: ¡Revenue 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

A B A B

SOC: 5/25 SOC: 20/40 SOC: 35/40 SOC: 20/25 +15 +15 Park and Charge

Efficient allocation

A B A B

SOC: 5/25 SOC: 20/40 SOC: 30/40 SOC: 25/25 +10 +20 Park and Charge

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

• Bid ¡1

2-­‑3pm, ¡$0.50, ¡5kWh ¡ ¡ Bid ¡2 3-­‑4pm, ¡$2.00, ¡9kWh SOC: ¡60% . ¡. ¡. Won Lose

EV ¡Customer ¡1

Bid ¡1 2-­‑3pm, ¡$1.50, ¡6kWh ¡ ¡ Bid ¡2 3-­‑4pm, ¡$3.00, ¡8kWh SOC: ¡30% . ¡. ¡. Won Won

EV ¡Customer ¡N

. ¡. ¡. ¡.

Bids Bids A l l

• c

a K

• n

¡ a n d ¡ P a y ¡ D e c i s i

• n

AllocaKon ¡and ¡ Pay ¡Decision

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

100 200 300 400 500 0.5 1 1.5 2 2.5 3 Number of Electric Vehicles

Ratio of Offline/Online Social Welfare

Auc2Charge OffOptimal 100 200 300 400 500 0.2 0.4 0.6 0.8 1 Number of Electric Vehicles Average of User Satisfaction Ratio

T=12 T=18 T=24

100 200 300 400 500 0.1 0.2 0.3 0.4 0.5 Number of Electric Vehicles Average of Unit Payment

T=12 T=18 T=24

12 18 24 1 2 3 Number of Time Slots

Ratio of Offline/Online Social Welfare

Auc2Charge OffOptimal

Social ¡Welfare ¡Ra-o: ¡T=12 Social ¡Welfare ¡Ra-o: ¡100 ¡EVs 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

Future of CPS – Smart City

Research Opportunities

Efficient Market Mechanism

Single ¡Microgrid Mul.ple ¡Microgrids

Mechanism ¡design ¡for ¡microgrid-­‑based ¡electricity ¡market ¡

Data Security and Privacy

Develop ¡unified ¡differen/al ¡privacy ¡solu/on ¡for ¡CPS ¡data ¡management ¡ Figure sources: www.finextra.com, ourenergypolicy.org Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 19/ 20

Future of CPS – Smart City

About Me

Nankai ¡University Wayne ¡State ¡University McGill ¡University Dad: ¡Architect Mom: ¡Nurse

I ¡am ¡devoted ¡to ¡u=lizing ¡ informa=on ¡technology ¡ ¡ to ¡improve ¡people’s ¡daily ¡life.

Qiao Xiang (McGill) Senseable City Lab, MIT 05/07/2015 20/ 20