Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Wang, Dapeng; Liu, Jia; Yang, Zhigang

doi:10.1109/access.2020.2978774

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…Ordinary users use their smart devices to obtain data, which are then submitted to the sink node for fusion; the sink node submits the final data to the sensing platform for publication. is method of obtaining information data by ordinary users using their own smart terminal devices is called MCS [5][6][7][8][9][10][11][12][13][14]. Currently, MCS is widely used in many fields, including environmental monitoring [15], traffic conditions [16], and medical health [17].…”

Section: Research Backgroundmentioning

confidence: 99%

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

Chen

Liu

et al. 2022

Journal of Electrical and Computer Engineering

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With the increased awareness of environmental protection, people have higher requirements for the accuracy of environmental information of surrounding life. The current monitoring of urban environmental information mainly comes from local environmental weather stations. Although the monitoring equipment of environmental weather stations is better than personal monitoring equipment, the monitoring equipment of weather monitoring stations is too expensive and only suitable for large-scale coarse-grained monitoring. Because the environmental information of a city is affected by factors such as landforms, buildings, rivers, factories, population density, and traffic flow, there are great differences in the environmental information of different areas in a city. Therefore, this study proposes a method that can be used for small-scale and fine-grained environmental information monitoring: the task grid-based urban environmental information release mechanism for mobile crowd sensing (MCS). Through this mechanism, the monitoring area is divided into different task grids according to the characteristics of the area, and the environmental information is sensed by mobile crowd sensing. For the sensing data, through an efficient data fusion algorithm designed in this study, the sensing information is fused to obtain the fine-grained environmental information of different task grids in the area. Through the use of this mechanism, differentiated environmental information can be provided to users in different areas of the city. In a simulation, this mechanism showed higher information accuracy than traditional information release methods. Thus, the mechanism is scientific and has good application value.

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Section: Research Backgroundmentioning

confidence: 99%

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

Chen

Liu

et al. 2022

Journal of Electrical and Computer Engineering

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“…For categorical data, d ist (•) is simply computed according to equation (4). For continuous data, the d ist (•) is calculated according to equation (3), which needs to first compute the std of the sensing data, which is standard deviation. Since the std calculation is performed only once in the entire algorithm, it is not included in the iterative process.…”

Section: Initializationmentioning

confidence: 99%

“…With the rapid popularization of portable mobile sensing devices (such as smart phones and smart watches), which carry many sensors (gravity sensors, GPS, acceleration sensors, fingerprint, etc. ), MCS has been extensively studied [1][2][3][4]. Participants with mobile sensing devices are encouraged to upload, analyze, and process their sensing data.…”

Section: Introductionmentioning

confidence: 99%

A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

Wang

et al. 2021

Security and Communication Networks

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With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

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“…The data layer is responsible for the management and processing of data. An MCS campaign can be promptly designed to deploy the functionality of the data layer in the cloud or closer to the network edge [52]. Most data processing, mining, and inference is done by the MCS system over the MCS participants' contributions.…”

Section: The Data Layermentioning

confidence: 99%

Quality Estimation for Scarce Scenarios Within Mobile Crowdsensing Systems

Azmy

Hassanein

2020

IEEE Internet Things J.

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Mobile Crowd Sensing (MCS) is a paradigm that exploits the presence of a crowd of moving human participants to acquire, or generate, data from their environment. As part of the Internet of Things (IoT) paradigm, MCS serves the quest for more efficient operation of a smart city. Big data techniques employed on this data produce inferences about the participants' environment, the smart city. However, sufficient amounts of data are not always available. Sometimes, the available data is scarce as it is obtained at different times, locations, and from different MCS participants who may not be present. As a consequence, the scale of data acquired maybe small and susceptible to errors. In such scenarios, the MCS system requires techniques that acquire reliable inferences from such limited data sets. To that end, we resort to small data techniques that are relevant for scarce and erroneous scenarios. In this thesis, we discuss small data and propose schemes to tackle the problems associated with such limited data sets, in context of the smart city. We propose two novel quality metrics, MAD-Q and MADBS-Q, to deal with small data, focusing on evaluating the quality of a data set within MCS. We also propose an MCS-specific coverage metric that combines the spatial dimension with MAD-Q and MADBS-Q. We show the performance of all the presented techniques through closed-form mathematical expressions, with which simulations results were found to be consistent.

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Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Cited by 6 publications

References 45 publications

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

Quality Estimation for Scarce Scenarios Within Mobile Crowdsensing Systems

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