Mobile Crowdsourcing Applications Research Articles

Mobility-Aware Privacy-Preserving Mobile Crowdsourcing.

The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant’s location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant’s location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user’s time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant’s dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability.

How Artificial Intelligence and Mobile Crowd Sourcing are Inextricably Intertwined

Mobile Crowd Sourcing (MCS) has been an enabler in the development of artificial intelligence (AI) in general, and machine learning in particular. From collecting data to giving meaning to the data, there has been considerable work supporting the use of MCS in AI. While successful, current MCS solutions still suffer from limitations such as workers recruitment, data quality, trust, and so on, that can benefit a great deal from AI. However, the integration of AI in MCS is still at a nascent stage, thus opening various opportunities for further research. In this article, we review and discuss the integration of AI in MCS solutions, highlight its research challenges, and suggest means to address them. We also propose a novel architecture for AI-based MCS, where AI techniques are integrated and embedded in the different layers of MCS framework to provide efficient and trusted MCS applications. In particular, a machine learning (ML)-based selection using behaviors of individual workers is proposed, and its efficacy is gauged by analyzing a use-case study. The results show that by implementing a hybrid approach, the efficiency of selection was considerably improved. This article demonstrates a clear overview of AI-based MCS solutions and provide guidelines on applying AI to solve the current challenges and open issues.

Traffic Differentiated Clustering Routing in DSRC and C-V2X Hybrid Vehicular Networks

Vehicles equipped with sensors can participate in mobile crowdsourcing applications. Vehicular Ad Hoc Networks (VANETs) based on Dedicated Short Range Communication (DSRC) are used to carry sensing data. However, multi-hop transmissions for gathering data to Road Side Units (RSUs) in VANETs suffer from low data rate and long end-to-end delay, which can hardly meet the QoS requirements of delay-sensitive services. This triggers the consideration of constituting a DSRC and Cellular-Vehicle-to-Everything (C-V2X) hybrid vehicular network. Nevertheless, using cellular links to carry traffic can cause high cellular bandwidth costs. In this paper, we propose a Traffic Differentiated Clustering Routing (TDCR) mechanism in a Software Defined Network (SDN)-enabled hybrid vehicular network. The proposed mechanism includes a centralized one-hop clustering approach and a data delivery optimization method. Particularly, the optimization is to make a tradeoff between cellular bandwidth cost and end-to-end delay, for Cluster Heads (CHs) delivering their aggregated data either by multi-hop Vehicle-to-Vehicle (V2V) transmissions or by cellular networks. Since the problem is proven to be NP-hard, a two-stage heuristic algorithm is designed. We carry out simulations to evaluate the performance of our data collection scheme and the results show that it performs better than traditional mechanisms.

A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems

With the development of Mobile Crowd Sensing Networks (MCSN), more and more Mobile Crowdsourcing applications emerge. The mobile sensing technologies and theories have become the research hotspot. How to design an effective incentive mechanism to maximize social welfare is the key research in mobile crowdsourcing. This paper proposes an online auction algorithm through combining multi-attribute auction and reverse auction to select the candidate set of crowd workers dynamically. In order to further optimize the utility of platform-centric mobile crowdsourcing, the candidate set of crowd workers is optimized through improving the Discrete Particle Swarm Optimization (DPSO), which can determine the winner set of crowd workers. Through comparison experiments on simulation dataset and real dataset, the effectiveness and performance of the proposed worker-selection incentive mechanism are verified. From experimental results, it can be inferred that the proposed worker-selection incentive mechanism can inspire users to participate in crowd tasks and maximize the utility of mobile crowdsourcing systems effectively.

Exploring incentive mechanisms for mobile crowdsourcing: sense of safety in a Thai city

ABSTRACTThe rapid adoption of mobile devices enables capture and transmission of a variety of sensor and user-contributed data, creating a new data collection paradigm and a wide range of services, often termed as mobile crowdsourcing. However, we are still investigating factors contributing towards the effectiveness of such systems. One key factor to succeed in mobile crowdsourcing applications is the incentive mechanism, which motivates people to contribute to a crowdsourcing effort. In this research, we conducted field experiments that compared the effectiveness of non-monetary and monetary incentive mechanisms, using both quantitative and qualitative methods. The focus was on an exploration of how these mechanisms motivate users’ performance in a mobile crowdsourcing environment. In the experiment, we developed a smart zoning application that allows users to share areas of the cities they perceived as safe or unsafe. The results from this study contribute to research in mobile crowdsourcing for urban understanding. Taken together, these results suggest that payment-related incentives can not only engage people’s interest in project participation but also help improve their work performance in terms of productivity and quality. Notably, a fixed-price financial reward mechanism is best suited for short period data collection and achieving data quality. Also, referral incentive mechanisms, if properly designed, have the potential to extend user coverage, both spatially and temporally. These results can be helpful with regard to the formulation of guidelines on how to create and organize effective payment-based incentives for crowd involvement in cities.

Mobile crowdsourcing: framework, challenges, and solutions

SummaryCrowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built‐in sensors and multiple ratio interfaces, mixing smartphone‐based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real‐time and location‐sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications. Copyright © 2016 John Wiley & Sons, Ltd.