TDTA: A truth detection based task assignment scheme for mobile crowdsourced Industrial Internet of Things

Abstract

Mobile Crowdsensing (MCs), a promising strategy for intelligent data collection and task computing, is conducive to constructing industrial system and providing industrial services in the industrial Internet of Things (IIoT). How to ensure data reliability in an industrial environment is a challenging problem. In this article, we proposed a Truth Detection based Task Assignment (TDTA) scheme to assign micro-tasks to reliable workers and establish a credible task execution environment for crowdsourced IIoT. The TDTA scheme mainly contains three methods for different application scenarios: (1) Direct truth detection method. The Edge Node (EN) will calculate partial micro-tasks results as true results when idle, and compares them with the results reported by workers to detect their reliability directly. (2) Indirect truth detection method. After the platform obtains some trusted workers through the direct method, it distributes the same micro-tasks to workers with unknown credibility and trusted workers, and compares their results to verify reliability indirectly. (3) Post audit truth detection method. When idle, the platform recalculates historical reporting results for suspicious workers to verify their credibility. And the effect of time decay on credibility is considered. Moreover, the TDTA scheme also considers the factors of integrity and delay of the results to calculate the Quality of Service (QoS) value. Then the most active and credible workers are chosen to execute the task. Theoretical analysis and experiment results demonstrate the effectiveness of our proposed scheme, which has higher EN resource utilization, task result accuracy, and malicious worker recognition rate.