相关论文: Social-Network-Assisted Worker Recruitment in Mobi…

Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, multi-task-oriented worker selection is essential and…

The wide spread of mobile devices has enabled a new paradigm of innovation called Mobile Crowdsourcing (MCS) where the concept is to allow entities, e.g., individuals or local authorities, to hire workers to help from the crowd of connected…

Mobile Crowdsourcing (MCS) is a novel distributed computing paradigm that recruits skilled workers to perform location-dependent tasks. A number of mature incentive mechanisms have been proposed to address the worker recruitment problem in…

Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS). Current recruitment systems assume that a pre-defined pool…

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain…

Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the…

Mobile Crowd Sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and user's mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in…

Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages the diverse embedded sensors in massive mobile devices. A key objective in MCS is to efficiently schedule mobile users to perform multiple sensing tasks. Prior work…

Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS…

The increasing demand for sensing, collecting, transmitting, and processing vast amounts of data poses significant challenges for resource-constrained mobile users, thereby impacting the performance of wireless networks. In this regard,…

Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However,…

Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference is incorporated into the MCS process for reducing sensing costs while its quality is guaranteed. Since the sensed data from different cells (sub-areas) of the…

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 users for a single MCS task, multi-task-oriented participant…

This paper investigates a novel hybrid worker recruitment problem where the mobile crowd sensing and computing (MCSC) platform employs workers to serve MCSC tasks with diverse quality requirements and budget constraints, under uncertainties…

Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In…

Mobile crowd sensing (MCS) is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices such as smartphones, music players, and in-vehicle sensors at the edge of the Internet, to collect data. The new paradigm will fuel…

Mobile Crowd Sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are…

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we…

Mobile Crowdsensing (MCS) is a sensing paradigm that has transformed the way that various service providers collect, process, and analyze data. MCS offers novel processes where data is sensed and shared through mobile devices of the users…

Spatial Mobile Crowdsourcing (SMCS) can be leveraged by exploiting the capabilities of the Social Internet-of-Things (SIoT) to execute spatial tasks. Typically, in SMCS, a task requester aims to recruit a subset of IoT devices and…