This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

Sensing cost and data quality are two primary concerns in mobile crowd sensing. In this
article, we propose a new crowd sensing paradigm, sparse mobile crowd sensing, which …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Location information of workers are usually required for optimal task allocation in mobile
crowdsensing, which however raises severe concerns of location privacy leakage. Although …

Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker
selection approaches mainly focus on selecting a proper subset of workers for a single MCS …

Mobile devices equipped with rich sensors, such as smartphones, watches, or vehicles,
have been pervasively used all around the world. Their high penetration and powerful …

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While
previous participant selection approaches mainly focus on selecting a proper subset of …

In traditional mobile crowdsensing applications, organizers need participants' precise
locations for optimal task allocation, eg, minimizing selected workers' travel distance to task …

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to
motivate the participation of task participants. However, a monetary incentive mechanism is …

Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and
infer urban-scale sensing data. However, participants risk their location privacy when …