Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed Bandits

TL;DR

This paper introduces a novel combinatorial multi-armed bandit approach for diversity-based recruitment in mobile crowdsensing, optimizing task quality under budget constraints while learning worker capabilities.

Contribution

It proposes a dynamic task diversity model and an efficient bandit-based recruitment strategy that accounts for overlapping tasks and variable worker quality.

Findings

Improved task diversity over rounds enhances sensing coverage.

The bandit approach effectively balances exploration and exploitation.

Simulations show significant performance gains over baseline methods.

Abstract

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.