Jiangtao Wang et al. Energy Saving Techniques in Mobile Crowd Sensing: Current State and. Future Opportunities. IEEE Communications Magazine. (in press ...
Jiangtao Wang, Peking University
2018/02 @UNSW, Sydney
Outline
Backgrounds
1 2
New Perspectives
3
Future Work
Power of the Crowds
Lior Zoref TED Talk
Crowds + X New Paradigm or Research Topic
Urban Sensing MCS
Creative Work
Software Engineering
Crowdsourcing
Knowledge Graph Refinement
Data Mining
Mobile Crowd Sensing (MCS): Research Background Human mobility, embedded sensor Collect and report data in mobile device
Cloud Server
MCS 任务 任务 任务 Task
Data integration Air quality Noise level
Flow of citizens
Traffic congestion status
Jiangtao Wang et al. Energy Saving Techniques in Mobile Crowd Sensing: Current State and Future Opportunities. IEEE Communications Magazine. (in press, IF:10.4).
Framework of MCS Research Research on MCS Applications Environment Sensing
Social Sensing
Infrastructure Sensing
Research on Core Supporting Techniques Task creation
Task allocation
Task execution
Data integration
End-user programming
Quality control
Energy saving
Missing data inference
Cost control
Privacy preserving
Micro-Task design
visualization
My Early Research Experience in MCS (PhD student)
MCS application research: MCS-based queue time estimation system • Queue behavior detection with accelerometer sensor. • Using acoustic context to improve accuracy Task creation techniques: From the perspective of software engineering • Help task creators with or without professional programming skills to create their tasks efficiently. • • •
J Wang, Y Wang, D Zhang, L Wang, C Chen, J Lee, Y He: Real-Time and Generic Queue Time Estimation based on Mobile Crowdsensing. Frontiers of Computer Science 12/2016. J Wang, Y Wang, S Helal, D Zhang: A Context-Driven Worker Selection Framework for Crowd-Sensing. International Journal of Distributed Sensor Networks 03/2016; 2016(3):1-16. J Wang, Y Wang, L Wang, Y He (2017). GP-selector: a generic participant selection framework for mobile crowdsourcing systems. World Wide Web, 1-24.
Framework of MCS Research Research on MCS Applications Environment Sensing
Social Sensing
Infrastructure Sensing
Research on Core Supporting Techniques Task creation
Task allocation
Task execution
Data integration
End-user programming
Quality control
Energy saving
Missing data inference
Micro-Task design
Cost control
Privacy preserving
visualization
The focus of the recent work (2016~now)
Task Allocation in MCS: An Overview Various data quality measures, incentive models, optimization goals and constraints… Single-objective vs Multi-objective
Goal & Constraint
Problem formulation of MCS task allocation Online vs Offline
Single task vs Multi-task
Hot topics in top venues of recent years: CSCW, UBICOMP, TMC, ICDE, INFOCOM….
Outline
Backgrounds
1 2
New Perspectives
3
Future Work
Our Recent Work: New Perspective in MCS Task Allocation
P1 P2 P3
• From single task to multi-task • Multi-task allocation with single-task quality considered
• Hybrid task allocation
Research Perspective 1：From single-task to multi-task “single task assumption “ in existing work：tasks are independent popularity of MCS resource competition (e.g., budget and participants sensing bandwidth) among multiple tasks,“single task assumption "does not hold any longer
Multi-task allocation in MCS
Scenario A One Organizer Multiple Tasks
Goal：overall utility Constraint: total budget Naï ve method based on divided budget： overall utility is not optimal
Scenario B Multiple Organizers Multiple Tasks
Goal：overall utility Constraints: sensing bandwidth + task budget Single task oriented approach participant overload
Research Perspective 1：From single-task to multi-task Basic idea: (1) participant mobility prediction； (2)overall utility modeling；(3) greedy selection with submodular theory (near-optimal)
Jiangtao Wang, Yasha Wang, Daqing Zhang, Haoyi Xiong, Leye Wang, Helal Sumi, Yuanduo He, Feng Wang: Fine-Grained Multi-Task Allocation for Participatory Sensing with a Shared Budget. IEEE IOT JOURNAL, VOL. 3, NO. 6, DECEMBER 2016 (IF: 7.6) Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Yuanduo He, Liantao Ma: PSAllocator: MultiTask Allocation for Participatory Sensing with Sensing Capability Constraints. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2017). ACM. (CCF A)
Our Recent Work: New Perspective in MCS Task Allocation
P1 P2 P3
• From single task to multi-task • Multi-task allocation with single-task quality considered
• Hybrid task allocation
Research Perspective 2：multi-task allocation with single-task quality considered Existing work optimize overall utility without considering single task quality Low quality of single task MCS service unavailable resource wasting. (e.g., navigation service: 20% coverage of traffic status monitoring) Optimizing overall utility with single-task quality considered (set a minimum quality threshold and re-define overall utility) Baseline 1
Baseline 2
when utility increase of each item is zero random selection
using relaxed utility
Shortcoming: Overall utility may not be near-optimal
Shortcoming: Resource wasting (the quality of tasks with assigned participant does not reach to the minimum threshold
Basic idea：assign resources to the tasks that can finally reach to the minimum quality requirement (avoid resource wasting)
Research Perspective 2：multi-task allocation with single-task quality considered
Basic idea: participant mobility prediction+ descent greedy approach •
• •
Assign all resources by “ignoring” the bandwidth constraint of each participant. Delete task-participant pair one by one with minimum overall utility reduction. For tasks failing to reach minimum quality: abandon task and release corresponding resources
Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Haoyi Xiong, Chao Chen, Qin Lv, Zhaopeng Qiu. Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance. IEEE Transactions on Mobile Computing, 2018.
Our Recent Work: New Perspective in MCS Task Allocation
P1 P2 P3
• From single task to multi-task • Jointly consider individual task and overall multi-task utility
• Hybrid Task Allocation
Research Perspective 3: Hybrid Task Allocation (1/3)
Figures borrowed from Guo et al
Opportunistic Mode
+ low cost, less intrusive
- tasks may not be completed
Hybrid mode
Participatory Mode
+ completion assured - intrusive, high cost
Research Perspective 3： Hybrid Task Allocation (2/3)
Payment： • Opportunistic worker: fixed reward • Participatory worker: in proportion to movement distance
Constraint: total budget Goal: maximize the number of completed tasks
Naïve method: divide the budget into two parts may not find a optimal division Hence, we need to design more sophisticated method----jointly
consider and optimize task allocation of these two modes.
Research Perspective 3： Hybrid Task Allocation (3/3) Key challenge: how to select opportunistic workers by jointly considering future task allocation for participatory workers ?
Prefer to select OPP workers Visit more task locations
Visit task locations where the participatory workers are sparsely distributed
Submitted to IEEE TMC (under review)
Outline
Backgrounds
1 2
New Perspectives
3
Future Work
New Trend in Crowd Computing ?
Crowd Learning 4 Crowd
Crowd 4 Learning
Learning Jiangtao Wang et al. , Crowd-Assisted Machine Learning: Current Issues and Future Directions, submitted to IEEE Computer (under review).
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Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Yuanduo He, Liantao Ma: PSAllocator: Multi-Task Allocation for Participatory Sensing with Sensing Capability Constraints. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1139-1151). ACM. (CCF A) Jiangtao Wang, Yasha Wang, Daqing Zhang, Feng Wang, Haoyi Xiong, Chao Chen, Qin Lv, Zhaopeng Qiu. Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance. IEEE Transactions on Mobile Computing, 2018. (CCF A) Jiangtao Wang, Yasha Wang, Daqing Zhang, Haoyi Xiong, Leye Wang, Helal Sumi, Yuanduo He, Feng Wang: Fine-Grained Multi-Task Allocation for Participatory Sensing with a Shared Budget. IEEE Internet of things journal, vol. 3, no. 6, 2016. (IF:7.6) Jiangtao Wang, Yasha Wang, Daqing Zhang, Sumi Helal. Energy Saving Techniques in Mobile Crowd Sensing: Current State and Future Opportunities. IEEE Communications Magazine ( IF:10.4). Jiangtao Wang, Yasha Wang, Yafei Wang (2017). CAPFF: A context-aware assistant for paper form filling. IEEE Transactions on Human-Machine Systems, 47(6), 903-908. Jiangtao Wang, Yasha Wang, Leye Wang, Yuanduo, He. GP-Selector: A Generic Participant Selection Framework for Mobile Crowdsourcing Systems. World Wide Web Journal , Springer ,2017. Full text downloading：https://www.researchgate.net/profile/Jiangtao_Wang4
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