Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Software Engineering (11)
- Databases and Information Systems (9)
- Engineering (5)
- Artificial Intelligence and Robotics (4)
- Information Security (4)
-
- Numerical Analysis and Scientific Computing (4)
- Social and Behavioral Sciences (4)
- Computer Engineering (2)
- Operations Research, Systems Engineering and Industrial Engineering (2)
- Public Affairs, Public Policy and Public Administration (2)
- Transportation (2)
- Computer and Systems Architecture (1)
- Data Storage Systems (1)
- Electrical and Computer Engineering (1)
- Geographic Information Sciences (1)
- Geography (1)
- Programming Languages and Compilers (1)
- Sociology (1)
- Work, Economy and Organizations (1)
Articles 1 - 26 of 26
Full-Text Articles in Physical Sciences and Mathematics
Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim
Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …
Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim
Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result …
Crowdtc: Crowd-Powered Learning For Text Classification, Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng
Crowdtc: Crowd-Powered Learning For Text Classification, Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng
Research Collection School Of Computing and Information Systems
Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform …
Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng
Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …
Crowdbc: A Blockchain-Based Decentralized Framework For Crowdsourcing, Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jiannan Liu, Yang Xiang, Robert H. Deng
Crowdbc: A Blockchain-Based Decentralized Framework For Crowdsourcing, Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jiannan Liu, Yang Xiang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized …
Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham
Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Prior research reported that workers on Amazon Mechanical Turk (AMT) are underpaid, earning about $2/h. But the prior research did not investigate the difference in wage due to worker characteristics (e.g., country of residence). We present the first data-driven analysis on wage gap on AMT. Using work log data and demographic data collected via online survey, we analyse the gap in wage due to different factors. We show that there is indeed wage gap; for example, workers in the U.S. earn $3.01/h while those in India earn $1.41/h on average.
Project Sidewalk: A Web-Based Crowdsourcing Tool For Collecting Sidewalk Accessibility Data At Scale, Manaswi Saha, Michael Saugstad, Hanuma Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, Jon Froehlich
Project Sidewalk: A Web-Based Crowdsourcing Tool For Collecting Sidewalk Accessibility Data At Scale, Manaswi Saha, Michael Saugstad, Hanuma Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, Jon Froehlich
Research Collection School Of Computing and Information Systems
We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement …
Sybmatch: Sybil Detection For Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Kan Yang, Yinghui Zhang, Xiaohua Jia, Robert H. Deng
Sybmatch: Sybil Detection For Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Kan Yang, Yinghui Zhang, Xiaohua Jia, Robert H. Deng
Research Collection School Of Computing and Information Systems
The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. In this paper, we focus on the privacy leaks and sybil attacks during the task matching, and propose a privacy-preserving task matching scheme, called SybMatch. The SybMatch scheme can simultaneously protect the privacy of publishers and subscribers against semi-honest crowdsourcing service provider, and meanwhile support the sybil detection against greedy subscribers and efficient user revocation. Detailed security analysis and thorough performance evaluation show that the SybMatch scheme is secure and efficient.
Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng
Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, …
A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham
A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, …
Slade: A Smart Large-Scale Task Decomposer In Crowdsourcing, Yongxin Tong, Lei Chen, Zimu Zhou, H. V. Jagadish, Lidan Shou
Slade: A Smart Large-Scale Task Decomposer In Crowdsourcing, Yongxin Tong, Lei Chen, Zimu Zhou, H. V. Jagadish, Lidan Shou
Research Collection School Of Computing and Information Systems
Crowdsourcing has been shown to be effective in a wide range of applications, and is seeing increasing use. A large-scale crowdsourcing task often consists of thousands or millions of atomic tasks, each of which is usually a simple task such as binary choice or simple voting. To distribute a large-scale crowdsourcing task to limited crowd workers, a common practice is to pack a set of atomic tasks into a task bin and send to a crowd worker in a batch. It is challenging to decompose a large-scale crowdsourcing task and execute batches of atomic tasks, which ensures reliable answers at …
Introducing People With Asd To Crowd Work, Kotaro Hara, Jeffrey P. Bigham
Introducing People With Asd To Crowd Work, Kotaro Hara, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Adults with Autism Spectrum Disorders (ASD) are unemployed at a high rate, in part because the constraints and expectations of traditional employment can be difficult for them. In this paper, we report on our work in introducing people with ASD to remote work on a crowdsourcing platform and a prototype tool we developed by working with participants. We conducted a six-week long user-centered design study with three participants with ASD. The early stage of the study focused on assessing the abilities of our participants to search and work on micro-tasks available on the crowdsourcing market. Based on our preliminary findings, …
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Research Collection School Of Computing and Information Systems
We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …
Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang
Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang
Research Collection School Of Computing and Information Systems
High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agentoperated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a …
Metroeye: Smart Tracking Your Metro Rips Underground, Weixi Gu, Ming Jin, Zimu Zhou, Costas J. Spanos, Lin Zhang
Metroeye: Smart Tracking Your Metro Rips Underground, Weixi Gu, Ming Jin, Zimu Zhou, Costas J. Spanos, Lin Zhang
Research Collection School Of Computing and Information Systems
Metro has become the first choice of traveling for tourists and citizens in metropolis due to its efficiency and convenience. Yet passengers have to rely on metro broadcasts to know their locations because popular localization services (e.g. GPS and wireless localization technologies) are often inaccessible underground. To this end, we propose MetroEye, an intelligent smartphone-based tracking system for metro passengers underground. MetroEye leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and infers the state of passengers (Stop, Running, and Interchange) during an entire metro trip using a Conditional Random Field (CRF) model. MetroEye further provides arrival …
Incentive Mechanism Design For Heterogeneous Crowdsourcing Using All-Pay Contests, Tie Luo, Salil S. Kanhere, Sajal K. Das, Hwee-Pink Tan
Incentive Mechanism Design For Heterogeneous Crowdsourcing Using All-Pay Contests, Tie Luo, Salil S. Kanhere, Sajal K. Das, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities, 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 for each potential winner (worker). We prove and characterize the unique equilibrium of this contest, and …
A Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Archan Misra, Randy Tandriansyah Daratan, Thivya Kandappu
A Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Archan Misra, Randy Tandriansyah Daratan, Thivya Kandappu
Research Collection School Of Computing and Information Systems
By effectively utilizing smartphones to reach out and engage a large population of mobile users, mobile crowdsourcing can become a game-changer for many urban operations, such as last mile logistics and municipal monitoring. To overcome the uncertainties and risks associated with a purely best-effort, opportunistic model of such crowdsourcing, we advocate a more centrally-coordinated approach, that (a) takes into account the predicted movement paths of workers and (b) factors in typical human behavioral responses to various incentives and deadlines. To experimentally tackle these challenges, we design, develop and experiment with a real-world mobile crowd-Tasking platform on an urban campus in …
A Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Archan Misra, Randy Tandriansyah Daratan, Thivya Kandappu
A Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Archan Misra, Randy Tandriansyah Daratan, Thivya Kandappu
Research Collection School Of Computing and Information Systems
By effectively utilizing smartphones to reach out and engage a large population of mobile users, mobile crowdsourcing can become a game-changer for many urban operations, such as last mile logistics and municipal monitoring. To overcome the uncertainties and risks associated with a purely best-effort, opportunistic model of such crowdsourcing, we advocate a more centrally-coordinated approach, that (a) takes into account the predicted movement paths of workers and (b) factors in typical human behavioral responses to various incentives and deadlines. To experimentally tackle these challenges, we design, develop and experiment with a real-world mobile crowd-Tasking platform on an urban campus in …
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Research Collection School Of Computing and Information Systems
Crowdsourcing has shown great potential in obtaining large-scale and cheap labels for different tasks. However, obtaining reliable labels is challenging due to several reasons, such as noisy annotators, limited budget and so on. The state-of-the-art approaches, either suffer in some noisy scenarios, or rely on unlimited resources to acquire reliable labels. In this article, we adopt the learning with expert~(AKA worker in crowdsourcing) advice framework to robustly infer accurate labels by considering the reliability of each worker. However, in order to accurately predict the reliability of each worker, traditional learning with expert advice will consult with external oracles~(AKA domain experts) …
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Research Collection School Of Computing and Information Systems
In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we …
Multi-Agent Task Assignment For Mobile Crowdsourcing Under Trajectory Uncertainties, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra
Multi-Agent Task Assignment For Mobile Crowdsourcing Under Trajectory Uncertainties, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra
Research Collection School Of Computing and Information Systems
In this work, we investigate the problem of mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually browse and filter tasks to perform, we intend to automatically make task recommendations based on workers' historical trajectories and desired time budgets. However, predicting workers' trajectories is inevitably faced with uncertainties, as no one will take exactly the same route every day; yet such uncertainties are oftentimes abstracted away in the known literature. In this work, we depart from the deterministic modeling and study the stochastic task recommendation problem where …
Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han
Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han
Research Collection School Of Computing and Information Systems
In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.
Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander
Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander
Research Collection School Of Computing and Information Systems
We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time …
Stfu Noob!: Predicting Crowdsourced Decisions On Toxic Behavior In Online Games, Jeremy Blackburn, Haewoon Kwak
Stfu Noob!: Predicting Crowdsourced Decisions On Toxic Behavior In Online Games, Jeremy Blackburn, Haewoon Kwak
Research Collection School Of Computing and Information Systems
One problem facing players of competitive games is negative, or toxic, behavior. League of Legends, the largest eSport game, uses a crowdsourcing platform called the Tribunal to judge whether a reported toxic player should be punished or not. The Tribunal is a two stage system requiring reports from those players that directly observe toxic behavior, and human experts that review aggregated reports. While this system has successfully dealt with the vague nature of toxic behavior by majority rules based on many votes, it naturally requires tremendous cost, time, and human efforts. In this paper, we propose a supervised learning approach …
Free Market Of Crowdsourcing: Incentive Mechanism Design For Mobile Sensing, Xinglin Zhang, Zheng Yang, Zimu Zhou, Haibin Cai, Lei Chen, Xiang-Yang Li
Free Market Of Crowdsourcing: Incentive Mechanism Design For Mobile Sensing, Xinglin Zhang, Zheng Yang, Zimu Zhou, Haibin Cai, Lei Chen, Xiang-Yang Li
Research Collection School Of Computing and Information Systems
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 …
Free Market Of Crowdsourcing: Incentive Mechanism Design For Mobile Sensing, Xinglin Zhang, Zheng Yang, Zimu Zhou, Haibin Cai, Lei Chen, Xiang-Yang Li
Free Market Of Crowdsourcing: Incentive Mechanism Design For Mobile Sensing, Xinglin Zhang, Zheng Yang, Zimu Zhou, Haibin Cai, Lei Chen, Xiang-Yang Li
Research Collection School Of Computing and Information Systems
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 …