Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

Abstract

With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research.

Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing

“…However, in real-world mobile crowdsensing environments, users are not independent individuals but groups connected through social relationships, and users are more willing to cooperate with social partners, especially in tasks that require privacy sharing. In addition, closer social ties will accomplish collaborative tasks more effectively without incurring significant communication costs [ 21 , 22 , 23 , 24 , 25 ].…”