TY - GEN
T1 - Worker Selection for On-Demand Crowdsourcing
AU - Tan, Tianxiang
AU - Wu, Yibo
AU - Liu, Zida
AU - Cao, Guohong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The ubiquity of mobile devices allows mobile users to participate in crowdsourcing anywhere, anytime. One potential application is to crowdsource photos/videos on demand to search for interested targets. Crowdsourced photos/videos have much better coverage compared to surveillance cameras, and thus help improve the effectiveness of target search. However, broadcasting the crowdsourcing task to all mobile users can significantly increase the cost in terms of resource and incentive budget. To reduce cost, the crowdsourcing server selects a subset of participating workers, and there are many challenges on worker selection. For example, due to occlusions in the photo/video scene, each worker only covers part of the area with certain probability. Due to the non-deterministic nature of this problem, we study two kinds of optimization problems: max-coverage which maximizes the probability of finding the target given a cost, and min-selection which minimizes the number of workers given the required probability of finding the target. Considering that workers may report exact locations or coarse-grained locations, we formalize four probability-based optimization problems for worker selection, and develop optimal or efficient approximation algorithms to solve them. The effectiveness of the proposed algorithms is evaluated and validated via extensive trace-driven simulations and a real-world demo.
AB - The ubiquity of mobile devices allows mobile users to participate in crowdsourcing anywhere, anytime. One potential application is to crowdsource photos/videos on demand to search for interested targets. Crowdsourced photos/videos have much better coverage compared to surveillance cameras, and thus help improve the effectiveness of target search. However, broadcasting the crowdsourcing task to all mobile users can significantly increase the cost in terms of resource and incentive budget. To reduce cost, the crowdsourcing server selects a subset of participating workers, and there are many challenges on worker selection. For example, due to occlusions in the photo/video scene, each worker only covers part of the area with certain probability. Due to the non-deterministic nature of this problem, we study two kinds of optimization problems: max-coverage which maximizes the probability of finding the target given a cost, and min-selection which minimizes the number of workers given the required probability of finding the target. Considering that workers may report exact locations or coarse-grained locations, we formalize four probability-based optimization problems for worker selection, and develop optimal or efficient approximation algorithms to solve them. The effectiveness of the proposed algorithms is evaluated and validated via extensive trace-driven simulations and a real-world demo.
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U2 - 10.1109/ICCCN54977.2022.9868898
DO - 10.1109/ICCCN54977.2022.9868898
M3 - Conference contribution
AN - SCOPUS:85138407961
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2022 - 31st International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st International Conference on Computer Communications and Networks, ICCCN 2022
Y2 - 25 July 2022 through 27 July 2022
ER -