Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing

Abstract

In the era of the Internet of Things (IoT), the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world. As a part of the IoT ecosystem, task assignment has become an important goal of the research community. Existing task assignment algorithms can be categorized as offline (performs better with datasets but struggles to achieve good real-life results) or online (works well with real-life input but is difficult to optimize regarding in-depth assignments). This paper proposes a Cross-regional Online Task (CROT) assignment problem based on the online assignment model. Given the CROT problem, an Online Task Assignment across Regions based on Prediction (OTARP) algorithm is proposed. OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments. The first stage uses historical data to make offline predictions, with a graph-driven method for offline bipartite graph matching. The second stage uses a bipartite graph to complete the online task assignment process. This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies. To encourage crowd workers to complete crowd tasks across regions, an incentive strategy is designed to encourage crowd workers’ movement. To avoid the idle problem in the process of crowd worker movement, a drop-by-rider problem is used to help crowd workers accept more crowd tasks, optimize the number of assignments, and increase utility. Finally, through comparison experiments on real datasets, the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.