Mobile crowdsensing has shown great potential in addressing large-scale data sensing
problems by allocating sensing tasks to pervasive mobile users. The mobile users will …

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

Emerging crowdsensing paradigm enables a large number of sensing applications, where
much attention is drawn to the fundamental problems for maximizing the system utility and …

Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale
sensing tasks. Many incentive mechanisms have been proposed to encourage user …

We propose a novel context-aware task allocation framework for mobile crowdsensing in the
scenario of edge computing to enable the crowdsensing platform effectively and real-timely …

Mobile crowdsensing is a new paradigm by which a platform can recruit mobile workers to
perform some sensing tasks by using their smart mobile devices. In this paper, we focus on a …

In large-scale mobile crowd sensing systems, multi-task-oriented worker selection has
shown an increased efficiency in workers' allocation. However, existing solutions for multi …

The computing frontier is moving from centralized mega datacenters towards distributed
cloudlets at the network edge. We argue that cloudlets are well-suited for handling power …

Federated Learning (FL) is a new distributed machine learning (ML) approach which
enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models …

Exploiting the computing capability of mobile devices with specialized engines (eg, Neural
Engine in iPhone), an attractive paradigm of federated learning that combines the mobile …