Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.

Privacy-Aware Task Allocation and Data Aggregation in Fog-Assisted Spatial Crowdsourcing

Abstract: Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g., sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency.

User Motivation and Knowledge Sharing in Idea Crowdsourcing

Abstract: We investigate how the propensity to trust, intrinsic motivation, and extrinsic motivation drive the intentions of individuals to share knowledge in idea crowdsourcing. Building on motivation theories and Uses & Gratifications (U&G) approach, we conducted a web-based survey within IdeasProject, an open innovation and brainstorming community dedicated to harvesting ideas. Based on a sample of 244 users, our research shows that the key driver of knowledge-sharing intentions is made up of two intrinsic motivations — social benefits and learning benefits. We also found that recognition from the host company affects intention to share knowledge. From the management point of view, the relative importance of social integrative benefits calls for better facilities available for users to be able to help each other in formulating and developing their ideas. Learning and creativity could be inspired by feedback from professionals and experts, while providing insight into technological advances and features dealing with the current tasks.

Rise of the Indoor Crowd: Reconstruction of Building Interior View via Mobile Crowdsourcing

Abstract: Crowdsourcing is a technology with the potential to revolutionize large-scale data gathering in an extremely cost-effective manner. It provides an unprecedented means of collecting data from the physical world, particularly through the use of modern smartphones, which are equipped with high-resolution cameras and various micro-electrical sensors. In this paper, we address the critical task of reconstructing the indoor interior view of a building from crowdsourced data. We propose, design, and prototype IndoorCrowd2D, a smartphone-empowered crowdsourcing system for indoor scene reconstruction. We first formulate the problem via trackable models and then employ a divide and conquer approach to address the inherently incomplete, opportunistic, and noisy crowdsourced data. By utilizing the image information and sensory data in a coordinated way, our system demonstrates high result-accuracy, as well as allows a gradual build-up procedure of the hallway skeleton. Our evaluation result shows that IndoorCrowd2D achieves a precision around 85%, a 100% recall and a F-score around 95% for reconstructing college buildings from 1,151 datasets uploaded by 25 users. This reveals that our image and sensor hybrid method is more robust to overcome errors and outliers as compared to image-only method.

Incentive Mechanism Design for Heterogeneous Crowdsourcing Using All-Pay Contests

Abstract: Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers’ types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

Online crowdsourcing subjective image quality assessment

Abstract: Recently, HodgeRank on random graphs has been proposed as an effective framework for multimedia quality assessment problem based on paired comparison method. With the random design on large graphs, it is particularly suitable for large scale crowdsourcing experiments on Internet. However, to make it more practical toward this purpose, it is necessary to develop online algorithms to deal with sequential or streaming data. In this paper, we propose an online rating scheme based on HodgeRank on random graphs, to assess image quality when assessors and image pairs enter the system in a sequential way in a crowdsourceable scenario. The scheme is shown in both theory and experiments to be effective by exhibiting similar performance to batch learning under the Erdos-Renyi random graph model for sampling. It enables us to derive global rating and monitor intrinsic inconsistency in the real time. We demonstrate the effectiveness of the proposed framework on LIVE and IVC databases.