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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 • • • • • • Introduction State of the Art and Practice of MCS Cloud-Assisted MCS Architecture MCMR Incentive Mechanism Challenges Conclusions Introduction • Traditional Traffic Sensors – – – – – – – 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) • • 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 • • • • 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 • • • • 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]