Social-Network-Assisted Task Selection for Online Workers in Spatial Crowdsourcing: A Multi-Agent Multi-Armed Bandit Approach

Abstract

The popularity of smart devices and the availability of wireless networks bring considerable attention to Spatial Crowdsourcing (SC). Existing studies mainly focus on solutions to different optimization objectives of the SC platform, ignoring the entitlement of workers. This paper starts from the perspective of workers and investigates how to select suitable tasks for each online worker such that everyone can maximize their individual profit. Since the profit is related to the completion degree of tasks that is determined by the prior unknown parameter, we model the problem as a Multi-Agent Multi-Armed Bandit (MAMAB) problem. We propose a Payment-Estimation-Based Solution (PEBS), allowing workers to sequentially make decisions on task selection based on their observations and estimations. Specifically, the proposed PEBS first utilizes the social network among workers and assists workers in learning the information of tasks from the historical data. Then, it introduces the idea of probability matching in Thompson Sampling (TS) to help estimate the profit of workers and deal with the task selection problem. Finally, extensive simulations show that our proposed mechanism is efficient in optimizing the individual profit of workers.