  This paper explores mobile crowdsensing, which leverages mobile devices and
their users for collective sensing tasks under the coordination of a central
requester. The primary challenge here is the variability in the sensing
capabilities of individual workers, which are initially unknown and must be
progressively learned. In each round of task assignment, the requester selects
a group of workers to handle specific tasks. This process inherently leads to
task overlaps in the same round and repetitions across rounds. We propose a
novel model that enhances task diversity over the rounds by dynamically
adjusting the weight of tasks in each round based on their frequency of
assignment. Additionally, it accommodates the variability in task completion
quality caused by overlaps in the same round, which can range from the maximum
individual worker's quality to the summation of qualities of all assigned
workers in the overlap. A significant constraint in this process is the
requester's budget, which demands an efficient strategy for worker recruitment.
Our solution is to maximize the overall weighted quality of tasks completed in
each round. We employ a combinatorial multi-armed bandit framework with an
upper confidence bound approach for this purpose. The paper further presents a
regret analysis and simulations using realistic data to demonstrate the
efficacy of our model.
