Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

Abstract

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

“…After winning the bids, they are supposed to complete the tasks and submit the data truthfully. Finally, they will the rewards to cover the cost of resources, effort, and time consumed by completing the tasks themselves [31] , [32] , [33] . There are two modes of workers’ participation in the task.…”

“…The success of MCS applications depends on the massive data contributed by the workers [31] , [32] , [33] . In completing tasks, workers will have some costs, such as transportation fees, time-consuming, communication overhead, and device loss.…”

Thompson Sampling for Combinatorial Network Optimization in Unknown Environments

“…Given one such allocation, the corresponding super arm would be the set S = {(c j , j)} W j=1 and the expected reward of it can be written as r(S, p) = (i,j)∈S p i,j . Allocating orthogonal channels to secondary users can also be conceptualized as allocating tasks to workers in a mobile crowdsourcing platform [6], [43]. Then, p i,j would be the probability of worker j completing task i successfully and r(S, p) would be the expected number of completed tasks.…”

“…How should a base station allocate its users to channels to maximize the system throughput [5]? How should a mobile crowdsourcing platform dynamically assign available tasks to its workers to maximize the performance [6]? How can we identify the most reliable paths from source to destination under probabilistic link failures [7]?…”

Compact Scheduling for Task Graph Oriented Mobile Crowdsourcing

“…Specifically, a typical mobile crowdsourcing platform schedules spatial tasks released by crowdsourcers to a group of participant workers, and workers are required to physically move to some specified spatial locations and conduct these tasks. Recently, mobile crowdsourcing has spurred a wide interest from both academia and industry [3], [4]. Many MC applications and systems have also been developed, e.g., Gigwalk, TaskRabbit, etc.…”