• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.104-106
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• 2023

The mobile crowdsensing platform obtains sensing data from mobile users, and the involvement of the public increases the untrustworthy of collected data. In order to distinguish factual data from inaccurate data provided by untrustworthy users, the truth discovery method has been introduced for accurate data aggregation in mobile crowdsensing (MCS). To explore the application of truth discovery in mobile crowdsensing, we overview the general concepts of truth discovery algorithms. Finally, we summarize the main existing application prospects of truth discovery in mobile crowdsensing.

• ETRI Journal
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• v.38 no.3
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• pp.551-559
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• 2016

In this paper, we propose a hybrid sensor calibration scheme for mobile crowdsensing applications. As the number of newly produced mobile devices containing embedded sensors continues to rise, the potential to use mobile devices as a sensor data source increases. However, because mobile device sensors are generally of a lower performance and cost than dedicated sensors, sensor calibration is crucial. To enable more accurate measurements of natural phenomena through the use of mobile device sensors, we propose a hybrid sensor calibration scheme for such sensors; the scheme makes use of mobile device sensors and existing sensing infrastructure, such as weather stations, to obtain dense data. Simulation results show that the proposed scheme supports low mean square errors. As a practical application of our proposed scheme, we built a temperature map of a city using six mobile phone sensors and six reference sensors. Thanks to the mobility of the sensors and the proposed scheme, our map presents more detailed information than infrastructure-based measurements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1426-1447
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• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.31-48
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• 2023

The popularization of smart devices and subsequent optimization of their sensing capacity has resulted in a novel mobile crowdsensing (MCS) pattern, which employs smart devices as sensing nodes by recruiting users to develop a sensing network for multiple-task performance. This technique has garnered much scholarly interest in terms of sensing range, cost, and integration. The MCS is prevalent in various fields, including environmental monitoring, noise monitoring, and road monitoring. A complete MCS life cycle entails task allocation, data collection, and data aggregation. Regardless, specific drawbacks remain unresolved in this study despite extensive research on this life cycle. This article mainly summarizes single-task, multi-task allocation, and space-time multi-task allocation at the task allocation stage. Meanwhile, the quality, safety, and efficiency of data collection are discussed at the data collection stage. Edge computing, which provides a novel development idea to derive data from the MCS system, is also highlighted. Furthermore, data aggregation security and quality are summarized at the data aggregation stage. The novel development of multi-modal data aggregation is also outlined following the diversity of data obtained from MCS. Overall, this article summarizes the three aspects of the MCS life cycle, analyzes the issues underlying this study, and offers developmental directions for future scholars' reference.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1036-1054
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• 2019

Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS-an intelligent parking system-and demonstrate the feasibility and efficiency of our mechanism through emulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.32-35
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• 2018

센서를 설치하는 대신 일반 대중들의 모바일 기기의 센서 정보를 이용하는 모바일 크라우드 센싱 기술을 활용해 적은 비용으로 주차장 포화도 정보를 제공하는 스마트 주차 시스템을 제안한다. 본 논문에서는 이러한 기술이 적용한 스마트 주차 시스템의 구조도를 제안하고, 특히 제공되는 정보에서 개인정보(장소, 시간, 차량번호 등의 연관관계)의 노출을 막기 위해 정보 제공에 사용되는 차량 이미지에서 번호판과 같은 개인정보를 효과적으로 제거하는 방법을 제시한다. 실험을 통해 그 가능성을 보인다.