Transcript Slide - SWiMSys Lab

Cloud-Assisted Mobile Crowdsensing
for Urban Transportation
Houbing Song, Ph.D.
West Virginia University Institute of Technology
West Virginia Center of Excellence for Cyber-Physical Systems
Outline
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Introduction
State of the Art and Practice of MCS
Cloud-Assisted MCS Architecture
MCMR Incentive Mechanism
Challenges
Conclusions
Introduction
• Traditional Traffic Sensors
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Inductive-Loop Detectors
Video Image Processing System
Pneumatic Tubes
Global Positioning System (GPS)
Acoustic/Ultrasonic Sensors
Aerial/Satellite imaging
RFID Technology
Introduction
• Mobile sensing devices
– One data point: 1.75 billion smartphone users
– Sensors embedded in a smartphone: GPS, accelerometer, gyroscope, ambient
light, proximity, microphone, and camera sensors
• Mobile crowdsensing (MCS)
– refers to applications that leverage consumer mobile devices (GPS, smart phones,
and car sensors) to collect and share data about the user or the physical world,
either interactively or autonomously, towards a common goal
– without requiring major investments in the sensing infrastructure
Mobile sensor data
collection, analysis, and
consumption
Unique Challenges presented by MCS
• The population of mobile sensing devices is highly dynamic.
There may be excess or gaps in sensing capabilities at times.
• Depending on resource availability on the device, the sensing
function is not always available for external use.
• Crowdsensing data may contribute to many diverse use
cases, while a conventional sensor network typically supports
a single use case
• Human participants are an important part of MCS.
– A social architecture with incentive mechanisms is required
to recruit, engage, and retain the human participants.
– The privacy of the human participants must be preserved.
Mobile Cloud Computing (MCC)
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What?
– an infrastructure where both the data storage and data processing happen
outside of the mobile device
– Mobile cloud applications move the computing power and data storage
away from the mobile devices and into powerful and centralized
computing platforms located in clouds, which are then accessed over the
wireless connection based on a thin native client.
Why?
– Mobile devices face many resource challenges (battery life, storage,
bandwidth etc.)
– Cloud computing offers advantages to users by allowing them to use
infrastructure, platforms and software by cloud providers at low cost and
elastically in an on-demand fashion.
– MCC provides mobile users with data storage and processing services in
clouds, obviating the need to have a powerful device configuration (e.g.
CPU speed, memory capacity etc), as all resource-intensive computing
can be performed in the cloud.
State of the Art and Practice
• MIT CarTel: Traffic using mobile phones
• Microsoft Nericell: Monitoring road and traffic conditions using
mobile phones
• Mobile Century and Mobile Millennium by Berkeley and Nokia
• ParkNet
• CrowdITS in Queen’s Univ.
• GreenGPS: Fuel consumption
Route choice only; No incentive mechanism; No cloud support
Cloud-Assisted MCS Architecture
• Layered Architecture (Bottom-Up)
– Mobile Device Computational Layer : Data Collection
– Location Computational Layer: Data Fusion
– Cloud Computational Layer :Data Mining
• Functioning
– Raw sensor data are collected on devices and processed by
local analytic algorithms to produce consumable data for
applications (Localized Analytics)
– The data may then be modified to preserve privacy and is sent
to the cloud for aggregation and mining (Aggregate Analytics)
Traffic situation (e.g., hotspots,
and congestion levels)
Convenience services (e.g.,
carpooling, shared taxis, or
the use of remote parking)
Decision-making of
traffic authorities
Incentive mechanisms
Traffic recommendation
Aggregate analytics
Security and privacy
Inter-cloud
Social networks
Traffic cloud
Wireless network environment
MobileId, location, speed,
direction, and mode
Localized analytics
Transportation mode
Core Components
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Localized Analytics
Aggregate Analytics
Incentive Mechanisms
Traffic Recommendation
– Route
– Departure Time
– Mode
• Social Networks
• Security and Privacy
Cloud-Assisted MCS Traffic Congestion Control
Methods
Mobility
Cloud support
Cost
Service contents
Traditional Approaches by
Employing Loop Detectors
and Road-side Cameras
No
Optional
High
Limited
Vehicular Ad hoc
Networks (VANETs)
Yes
Optional
Medium
Abundant
Mobile Crowd Sensing
Yes
Optional
Low
Abundant
MCMR-based MCS with
Cloud Support
Yes
Supporting
Low
Very abundant
Application Paradigm
Services
Non-participants
Service components (e.g.,
incentive mechanisms)
Traffic cloud
Task
distribution
Participant
Sensing
reports
MCMR-based MCS w/ Cloud Support
• Incentive Mechanism: More Contributions
More Return (MCMR)
– Automatic Sensing and Uploading Approaches
– An Initiative Report Given by Drivers
– Service Process
Logic Flowchart
Login the cloud and
input the destination
Check the user
characteristics
Get the status from the
cloud
No
Are the
participants?
Automatic
sensing
An initiative
report
Yes
Enable incentive
mechanisms
Provide the convenient
feedback services (e.g.,
customer service)
Traffic
cloud
Send the status to the users
Get the convenient services and
lessen the traffic congestion
Get the status from the
cloud
Challenges
• System Architecture for multiple uses (safety,
mobility and environmental protection)
• Resource Limitations
– Energy
– Bandwidth
– Computation
• Security, Privacy and Data Integrity
• Incentive Mechanisms
Conclusions
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Architecture
MCMR
Challenges
Future Work
– Test on cloud computing testbeds
• Chameleon (http://www.chameleoncloud.org/)
• CloudLab (http://www.cloudlab.us/)
References
• Ganti, R.K.; Fan Ye; Hui Lei, "Mobile
crowdsensing: current state and future
challenges," IEEE Communications Magazine,
vol.49, no.11, pp.32,39, November 2011
• D. Zhang, H. Song, S. Zhao, “Cloud-Assisted
Mobile Crowdsensing for Urban Transportation”,
IEEE Transactions on Intelligent Transportation
Systems (Under Revision)
THANK YOU!
WV Center of Excellence for Cyber-Physical Systems
Security and Optimization for Networked Globe Laboratory (SONG Lab)
Email: [email protected]