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In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and…
Spatial crowdsourcing (SC) enables the assignment of location-based tasks to mobile users who must travel to specific locations to perform sensing or service activities. However, SC systems often operate in strategic environments where both…
In a crowdsourcing market, a requester is looking to form a team of workers to perform a complex task that requires a variety of skills. Candidate workers advertise their certified skills and bid prices for their participation. We design…
Modern decision making tools are based on statistical analysis of abundant data, which is often collected by querying multiple individuals. We consider data collection through crowdsourcing, where independent and self-interested agents,…
Incentives are key to the success of crowdsourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowdsourcing…
Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption…
The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We…
This paper evaluates the benefit of integrating vehicle-based mobile crowd-sensing tasks into the ride-hailing system through the collaboration between the data user and the ride-hailing platform. In such a system, the ride-hailing platform…
For job scheduling systems, where jobs require some amount of processing and then leave the system, it is natural for each user to provide an estimate of their job's time requirement in order to aid the scheduler. However, if there is no…
When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the…
Crowd sensing is a new paradigm which leverages a large number of sensor-equipped mobile phones to collect sensing data. To attract more participants to provide good quality, bidding mechanisms that solicit the Vickrey-Clarke-Groves (VCG)…
We investigate a type of emerging user-assisted mobile applications or services, referred to as Dynamic Mobile Ad-hoc Crowd Service (DMACS), such as collaborative streaming via smartphones or location privacy protection through a crowd of…
Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the…
Crowdsourcing can be applied to the Internet-of-Things (IoT) systems to provide more scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive…
With the rapid development of crowdsourcing platforms that aggregate the intelligence of Internet workers, crowdsourcing has been widely utilized to address problems that require human cognitive abilities. Considering great dynamics of…
Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring, spectrum sensing). In this study, a socially-aware crowdsensing system is advocated, in…
Crowdsensing is a promising sensing paradigm for smart city applications (e.g., traffic and environment monitoring) with the prevalence of smart mobile devices and advanced network infrastructure. Meanwhile, as tasks are performed by…
This paper addresses the scheduling problem in mobile social networks. We begin by proving that the approximation ratio analysis presented in the paper by Zhang \textit{et al.} (IEEE Transactions on Mobile Computing, 2025) is incorrect, and…
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…
Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a…