Multi-Armed Bandits on Partially Revealed Unit Interval Graphs

Xu, Xiao; Vakili, Sattar; Zhao, Qing; Swami, Ananthram

doi:10.1109/tnse.2019.2935256

Cited by 3 publications

(1 citation statement)

References 28 publications

(46 reference statements)

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“…In the current work, many mobile group intelligence perception systems assume that the user's perceived quality is known, and, on this basis, specific optimization goals are used to recruit users [13]. However, in real life, the perceived quality of mobile users is often unknown.…”

Section: Introductionmentioning

confidence: 99%

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

Jiang

Chen

Zhang

et al. 2022

Discrete Dynamics in Nature and Society

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In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user’s perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user’s perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user’s perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user’s perceptual quality according to the completion of the task. This algorithm sorts the users’ perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high-quality users to realize task assignments.

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Section: Introductionmentioning

confidence: 99%