The field of intelligent multimedia systems, which rely heavily on multimodal models trained
on large amounts of high-quality data, has been revolutionized by the use of deep learning …

Abstract Many Multi-Armed Bandit (MAB) based workers selection schemes have been
proposed to select high-quality workers to enhance the quality of tasks. However, in Mobile …

Abstract Mobile Crowd Sensing/Souring (MCS) is a novel sensing approach that leverages
the collective participation of users and their mobile devices to collect sensing data. As large …

Abstract In Mobile Crowdsensing system, many sensing tasks are time-sensitive, hence,
time validity is essential cause data outside the time frame is useless which will not only …

Accurate data collection from workers is crucial for the success of Mobile Crowd Sensing
(MCS) applications. However, Current studies exhibit several drawbacks. Firstly, the …

Abstract Mobile Crowd Sensing (MCS) is a promising computing paradigm for data
collection harnessing ubiquitous workers equipped with sensing devices. While some …

Multi-task assignment is widely used in mobile crowdsensing (MCS) to efficiently utilize
limited resources such as shared user pool, user capability constraints and so on. In MCS …

The security and privacy of data are major concerns in the mobile crowdsensing (MCS)
environment due to the huge amount of heterogeneous data received from various users …

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a
crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these …

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable
devices and smartphones have expanded a market for digital health and have emerged as …