High Quality Participant Recruitment of Mobile Crowdsourcing over Big Data

Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can finish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. Then, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers’ arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

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A Recommendation Scheme for an Optimal Pre-processing Permutation Towards High-Quality Big Data Analytics

Journal of KIISE ◽

10.5626/jok.2020.47.3.319 ◽

2020 ◽

Vol 47 (3) ◽

pp. 319-327

Author(s):

Seounghyun Kim ◽

Young-Kyoon Suh ◽

Byungchul Tak

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

High Quality

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Research on the Construction of "Metal Materials and Heat Treatment" Network Course to Higher Vocational Colleges under the Background of Big Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.851.615 ◽

2016 ◽

Vol 851 ◽

pp. 615-619 ◽

Cited By ~ 1

Author(s):

Zhi Ling Wang

Keyword(s):

Heat Treatment ◽

Big Data ◽

Lifelong Learning ◽

Learning Process ◽

The Internet ◽

High Quality ◽

Open Platform ◽

Treatment Network ◽

Metal Materials ◽

Vocational Colleges

With the development of social information in the background of big data, this paper gives the practical problems in the construction of network course of "metal materials and heat treatment". The aim of the study established on the basis of the Internet will provide us more comprehensive supports and services, and more friendly practical systems in teaching. Our research will build a public open platform for the cultivation of high-quality talents and the promotion of lifelong learning process. This paper has focused on the theoretical value of the network course construction, construction objectives, the content and advantages of the construction. The results of this paper are helpful to improve the teaching effects of the course of "metal materials and heat treatment" in vocational colleges.

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Multistrategy Repeated Game-Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things

Wireless Communications and Mobile Computing ◽

10.1155/2021/6695696 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Chuanxiu Chi ◽

Yingjie Wang ◽

Yingshu Li ◽

Xiangrong Tong

Keyword(s):

Game Theory ◽

Internet Of Things ◽

Incentive Mechanism ◽

Repeated Game ◽

Edge Computing ◽

Quality Data ◽

Mobile Edge Computing ◽

High Quality ◽

Mobile Crowdsourcing

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real-time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long-term participation of users and high quality of tasks. This paper designs a long-term incentive mechanism based on game theory. The long-term incentive mechanism is to encourage participants to provide long-term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game-based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long-term participation and high-quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long-term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright-Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long-term incentive effect for participants in mobile crowdsourcing systems.

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