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With the advancement of smartphones, mobile crowdsourcing has become a new computing paradigm to efficiently support novel mobile applications. Achieving good service quality of these applications, however, necessitates the participation of large number of smartphones, which can be obtained via providing suitable incentives to smartphone users. Nonetheless, most existing incentive mechanisms assume a centralized platform for recruiting smartphones, which is prone to expose the privacy of both smartphones and task requesters. In this context, this article studies a distributed truthful incentive mechanism (DTIM) for mobile crowdsourcing, where multiple auction rounds can be conducted locally in each smartphone and task requester. Specifically, in each auction round, the participating smartphones act as the sellers and submit bids to compete for their intended crowdsourcing tasks. The task requesters, on the other hand, act as the buyers that decide on the sellers and corresponding payments, depending on their submitted bids. Finally, based on the offered payment, each smartphone selects a buyer for trading to optimize its utility. It is shown that the proposed incentive mechanism is strategy-proof, budget balanced, individually rational, and computationally efficient. Numerical results provided corroborate the beneficial properties of the scheme. [ABSTRACT FROM AUTHOR]

Mobile crowdsourcing is an emerging complex problem solving paradigm that makes use of pervasive mobile devices equipped with multi-functional sensors. Recently, vehicles have also been increasingly adopted for mobile crowdsourcing, as the vehicles, as well as drivers, can provide diverse sensing capability and predictable mobility. Existing mobile crowdsourcing algorithms mostly recruit workers to complete one kind of sensing tasks, i.e., location-based query tasks or automatic sensing tasks. In this paper, we investigate the possibility of recruiting a set of vehicles to simultaneously complete these two categories of tasks, so as to maximize the sensing utility of each participant. We first model the worker recruitment for vehicle-based crowdsourcing as a bi-objective optimization problem with respect to the sensing capability and predictable mobility of vehicles. The recruitment problem is proven to be NP-hard, and we design two heuristic algorithms based on the bi-objective greedy strategy and the multi-objective genetic algorithm to find the solutions. The experimental results with a real-world traffic trace data set show that the proposed algorithms outperform some existing algorithms in finding solutions that maximize both objectives. [ABSTRACT FROM AUTHOR]

Ubiquitous smartphones with various built-in sensors have boosted development of many crowdsensing applications in recent years. To achieve good performance with this class of applications, a large number of participating users with sensed data are needed. Two important considerations for improving user participation in crowdsensing are the incentive mechanism for motivating users to contribute their sensing capabilities, and the techniques for protecting user privacy. In this article, we survey the works that address these issues by integrating privacy techniques with incentive mechanisms. We also advocate two future research directions to enhance incentive mechanisms for mobile crowdsensing. [ABSTRACT FROM AUTHOR]

Spatial crowdsourcing and crowdsensing are two emerging crowdsourcing paradigms, which enable a variety of location-based query and sensing tasks. In spatial crowdsourcing, mobile workers are required to travel physically to target locations in order to complete query tasks. Most existing work, hence, has focused on designing efficient query task assignment schemes to maximize the task completion rate under traveling constraints of workers for spatial crowdsourcing systems. In crowdsensing, on the other hand, sensor recordings of workers’ smartphones are of interest and have been collected to build various applications. Therefore, work concerning crowdsensing has strived to maximize the coverage area of sensor trajectories by selecting a set of workers. In this paper, we investigate the integration of these two paradigms. We notice a key link between these paradigms: While a worker is traveling to the target location of a query task, his trajectory may provide valuable coverage for a sensing task. Therefore, we propose a task management framework, named SpatialRecruiter, to efficiently match workers to the merged query and sensing tasks. We propose two coverage estimation functions to compute the coverage potential of a worker. Then, we design a greedy heuristic to select and assign workers. The experimental results on a real-world dataset demonstrate that the proposed strategies are efficient and effective in meeting the requirements of both paradigms. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing systems aim to provide various novel applications by employing pervasive smartphones. A key factor to enable such systems is substantial participation of normal smartphone users, which requires effective incentive mechanisms. In this paper, we investigate incentive mechanisms for online scenarios, where users arrive and interact with a task requester in a random order, and they have preferences (e.g., photographing) or limits (e.g., travel distance) over the sensing tasks. In existing online mechanisms, the task requester has limited power in assigning tasks to the selected users, i.e., it has to pay for all of the tasks specified by the selected users, although some of these tasks are of little value. To accommodate this, we investigate a more flexible setting, where the requester can actively assign most valuable tasks to the selected users. We design two online incentive mechanisms motivated by a sampling-accepting process and weighted maximum matching. We prove that the designed mechanisms achieve computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness. By carrying out extensive experiments on two real-world geographical datasets, we demonstrate the practical applicability of the proposed mechanisms. [ABSTRACT FROM PUBLISHER]

Off-the-shelf smartphones have boosted large scale participatory sensing applications as they are equipped with various functional sensors, possess powerful computation and communication capabilities, and proliferate at a breathtaking pace. Yet the low participation level of smartphone users due to various resource consumptions, such as time and power, remains a hurdle that prevents the enjoyment brought by sensing applications. Recently, some researchers have done pioneer works in motivating users to contribute their resources by designing incentive mechanisms, which are able to provide certain rewards for participation. However, none of these works considered smartphone users’ nature of opportunistically occurring in the area of interest. Specifically, for a general smartphone sensing application, the platform would distribute tasks to each user on her arrival and has to make an immediate decision according to the user’s reply. To accommodate this general setting, we design three online incentive mechanisms, named TBA, TOIM and TOIM-AD, based on online reverse auction. TBA is designed to pursue platform utility maximization, while TOIM and TOIM-AD achieve the crucial property of truthfulness. All mechanisms possess the desired properties of computational efficiency, individual rationality, and profitability. Besides, they are highly competitive compared to the optimal offline solution. The extensive simulation results reveal the impact of the key parameters and show good approximation to the state-of-the-art offline mechanism. [ABSTRACT FROM AUTHOR]

Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations. [ABSTRACT FROM AUTHOR]