Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart
terminals equipped with various embedded sensors to collect sensory data for wide …

Centralized particle swarm optimization (PSO) does not fully exploit the potential of
distributed or parallel computing and suffers from single-point-of-failure. Particularly, each …

Mobile crowdsensing (MCS) is an appealing sensing paradigm that leverages the sensing
capabilities of smart devices and the inherent mobility of device owners to accomplish …

Privacy-preserving online multi-task assignment is a crucial aspect of spatial crowdsensing
on untrusted platforms, where multiple real-time tasks are allocated to appropriate workers …

Mobile crowd sensing (MCS) with human participants has been proposed as an efficient
way of collecting data for smart cities applications. However, there often exist situations …

The minimum travel distance of task participants is one of the significant optimization
objectives of privacy-preserving task assignment in mobile crowdsensing (MCS). However …

Mobile crowdsensing (MCS) uses ubiquitous smart devices and network access technology
to migrate sensing tasks from a centralized platform to a distributed computing terminal …

Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of
massive mobile workers to perform various location-based sensing tasks. To assign workers …

Task allocation of large-scale and widely-distributed mobile users in a crowdsensing system
is a challenging issue, especially on time-varying sensing requirements and available users …

In the mobile crowdsensing (MCS) with large-scale data collection and sharing
environments, since a growing number of applications need to exploit multisource sensing …