DWSP-MT: Discovery of workers sensing preferences to match tasks for improving data collection quality in MCS

Abstract

Mobile CrowdSensing (MCS) is a new sensing paradigm that leverages the widespread use of mobile devices to collect data quickly and enable various key applications in the Internet of Things (IoT). Collecting high-quality data is essential for MCS to design and provide a high-quality service. However, it is difficult for the platform to detect dishonest workers who submit fake data. Therefore, MCS applications face a significant challenge. Although there are a number of studies on truth discovery, these studies mainly failed to consider the sensing preferences of workers, resulting in poor data quality enhancement. To overcome these issues, a Discovery of Workers Sensing Preferences to Match Tasks (DWSP-MT) scheme is proposed to improve the quality of MCS data collection. The DWSP-MT scheme primarily includes three components: 1) Unmanned Aerial Vehicles (UAVs) are employed to sense data as ground truth data and an unsupervised anomaly detection algorithm is adopted to identify the data truth of tasks submitted by unidentified workers. 2) The data truth of unidentified workers in historical tasks is evaluated to calculate their sensing preferences, which are used to identify trusted workers. 3) To improve the quality of data collection, a task assignment model based on the sensing preferences of trusted workers is proposed for the first time, which can ensure that tasks are assigned to their areas of most excellent expertise. At last, two open-access datasets were used to conduct experiments comparing the DWSP-MT scheme with existing truth discovery methods. The outcomes of these experiments indicate that our approach outperforms the state-of-the-art research and improves F1-score by 65.74 %, improves accuracy by 36.62 %, and reduces data bias by 60.61 %