Crowdsourcing and Cloud Computing Towards Trustful Pavement Condition...
Transcript of Crowdsourcing and Cloud Computing Towards Trustful Pavement Condition...
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Crowdsourcing and Cloud Computing Towards Trustful Pavement Condition Ranking
ZhiQiang Chen, PhD Assistant professor of civil and computer engineering
University of Missouri, Kansas City; Email: [email protected]
Yueying Bian President GreenBeanCity, Inc. Email: [email protected]
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Scope
• Focusing on pedestrian pavements
• Using crowdsourcing (human computing) as a general means to reporting, assessing, and managing pavement condition
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Outline
• Pedestrian pavement condition
• Related inspection technologies for pavement
• Why Crowdsourcing?
• A new paradigm – Mobile-cloud computing aided crowdsourcing
• Preliminary Performance Evaluation
• My Vision and Conclusions
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Pedestrian Pavement
• Everyone is a pedestrian • Crowdsourcing basis
• Equally as important component as highway/street pavements • Crowdsourcing incentive
• Lost cost to construct; relatively high cost to maintain • Motivation for using
crowdsourcing as an alternative technology
(Huber et al; FHWA SA-13-037)
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Pavement Inspection Technologies
• Visual inspection • Predominant to date;
• ‘Automatic’ inspection technologies • Guralnick et al 1993; earlier imaging technique
• Wang 2000; vehicle mounted at highway speed
• Li et al 2000; 3D scanning
• Huang and Xu 2006; real-time imaging processing
• Lu et al 2013; rapid and mobile acoustic surface wave
• All methods involve professional training, equipment, and usually feature high cost
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General Promises of Crowdsourcing Technology
• Real-time or Rapid problem solving o Enabled by the ubiquitous use of smartphones,
wearable computing devices, social networks, and connection via 3G/4G cellular networks
• Highly intelligent processing o Humans are better than existing computer-based
analysis; especially for recognition of highly complex visual patterns
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Types of Crowdsourcing
• May categories; for example (Liu 2014) • Crowd-Feeding: active, two-way feedback loop where
information or tasks conducted by the crowd are fed or shared back to the crowd.
• Crowd-Harvesting: passive and one-way gathering of data to search for information from the crowd information.
• Crowd-Seeding: strategic and one-way request that pre-identifies certain members in a crowd to source from and empower them with tools.
• Crowd-Sourcing: active and one-way request between the crowd and entity requesting information from the crowd (the crowd-sourcer).
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Crowdsourcing for Transportation-related Applications
• OpenStreetMap – probably the largest crowdsourcing project in the world
• Google Map Traffic – ‘borrow’ your position information when you drive and use Google Map for navigation (when turned on)
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Smart Technologies for Transportation Assessment
• NCHRP-14-29 Questionnaire from 50 states' DOT officials indicated strong interest in using mobile technologies for transportation structure assessment.
• Smart apps aided mobile-cloud computing are acceptable for transportation agencies
• Crowdsourcing or enabling the public for transportation structure assessment?
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Challenges of Crowdsourcing Technology
• Towards making real-time crowdsourcing-based pavement or in general built environment assessment: • Truthfulness: taken by users who volunteer and commit the
responsibility
• Relevance: reporting imaging-centric data related to pavement conditions
• Quality: reporting metadada ready for advanced extraction and quantification of damage
• Other Perspectives • Data provenance or
• Unstructured data to structured data
• Big Data issues (when scaled up)
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Our Solution – Collaborative Mobile-Cloud Computing (Chen et al
2013)
• Active, strategic and two-way crowdsourcing through smart apps and mobile-cloud computing infrastructure
• Front-end: users with smart apps that realize ‘human’ sensing and computing • Trustful, relevant and quality
• Back-end: a mobile-cloud computing infrastructure that serves the front-end and the users
A Trustful Crowdsourcing Unit (TCU)
Imaging Computing
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Collaborative Mobile-Cloud Computing
• An emerging and still-advancing computing paradigm!
• Proposed a CMCC framework for crowd-based Civil Infrastructure Condition Assessment • Context-enabled imaging
• On-demand cloud-based image analysis
Mobile Imaging and Computing Devices
Step - 1
Context-enabled ImagingStep - 2
Interactive Imaging and processing
Step - 3
Mobile-Cloud Web AnalyticsStep - 5
On-demand Advanced Image Analysis
Step - 4
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Basic System Design
• Using Android smartphones, tablets, and digital cameras as the front ends
• Using IBM Smart Cloud as the cloud infrastructure
• Android SDK and OpenCV to develop mobile fronts: context enabled imaging/computing
• Other potential enabling technology: natural language processing
• Using OpenCV to implement vision algorithms
• Other potential enabling technology: Hadoop-based big data management and basic processing
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Development and Implementation
• We have developed a prototype system • Using Android smartphones, tablets, and digital cameras as the front
ends • Using IBM Smart Cloud as the cloud infrastructure
• Android SDK and OpenCV to develop mobile fronts: context enabled
imaging/computing • Other potential enabling technology: natural language processing
• Using OpenCV to implement vision algorithms • Other potential enabling technology: Hadoop-based big data management and
basic processing
• jQuery Mobile, HTML5, Google Map APIs for disaster damage mapping and analytics
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Operational Flow A Trustful Crowdsourcing Unit (TCU)
Imaging Computing
Context-enabled Imaging • Imaging with the best of view • Interactive context-data input: gesture/voice
• Damage types (crack/pothole) • Pavement types/materials • Bounding box of specific damage
patterns
Collaborative Mobile-Cloud computing Mobile computing in the background • Context-aided processing in the
Cloud • Hosting of damage analytics for the
mobile ends
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Reporting of A Long crack Using multiple images
Video stream data From a mobile phone
Cloud processing (image stitching)
GIS Gateway for visualizing the damage
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Performance Evaluation: Concurrent Imagery Transmission
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1 3 5 7
Tran
smiss
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Rat
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/sec)
Number of Concurrent Mobile Devices
100KB 800KB 1MB
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Mobile Processing Performance
Simple Image Enhancement
Simple Contour Detection and Damage Quantification
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Conclusions and Vision
• Crowdsourcing – tremendous potential for realizing pedestrian pavement damage mapping
• Because of its imaging-centric nature, a mobile-cloud paradigm shows promises • The mobile end fulfills trustful and relevant reporting • The back-end servers realize high-performance
computing, data processing, and GIS-based analytics
• A crowd-based pavement ranking system? • As possible and feasible as Google Traffic and
OpenStreetMap
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Thanks much!