Data-Quality-Aware Participant Selection Mechanism for Mobile Crowdsensing

With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task-centric and user-centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform’s final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms.

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An Efficient and Secure Malicious User Detection Scheme Based on Reputation Mechanism for Mobile Crowdsensing VANET

Wireless Communications and Mobile Computing ◽

10.1155/2021/5302257 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Zhihua Wang ◽

Jiahao Liu ◽

Chaoqi Guo ◽

Shuailiang Hu ◽

Yongjian Wang ◽

...

Keyword(s):

Data Quality ◽

Traffic Safety ◽

Quality Evaluation ◽

Experimental Simulation ◽

Detection Scheme ◽

Mobile Crowdsensing ◽

Sensing Data ◽

Malicious User ◽

Sensing System ◽

Evaluation Phase

With the increasing development of wireless communication technology and Vehicular Ad hoc Network (VANET), as well as the continuous popularization of various sensors, Mobile Crowdsensing (MCS) paradigm has been widely concerned in the field of transportation. As a currently popular data sensing way, it mainly relies on wireless sensing devices to complete large-scale and complex sensing tasks. However, since vehicles are highly mobile in this scenario and the sensing system is open, that is, any vehicle equipped with sensing device can join the system, the credibility of all participating vehicles cannot be guaranteed. In addition, malicious users will upload false data in the sensing system, which makes the sensing data not meet the needs of the sensing tasks and will threaten traffic safety in some serious cases. There are many solutions to the above problems, such as cryptography, incentive mechanism, and reputation mechanisms. Unfortunately, although these schemes guaranteed the credibility of users, they did not give much thought to the reliability of data. In addition, some schemes brought a lot of overhead, some used a centralized server management architecture, and some were not suitable for the scenario of VANET. Therefore, this paper firstly proposes the MCS-VANET architecture-based blockchain, which consists of participating vehicles (PVs), road side units (RSUs), cloud server (CS), and the blockchain (BC), and then designs a malicious user detection scheme composed of three phases. In the data collecting phase, to reduce the data uploading overhead, data aggregation and machine learning technologies are combined by fully considering the historical reputation value of PVs, and the proportion of data uploading is determined based on the historical data quality evaluation result of PVs. In the data quality evaluation phase, a new reputation computational model is proposed to effectively evaluate the sensing data, which contains four indicators: the reputation history of PVs, the data unbiasedness, the leadership of PVs, and the spatial force of PVs. In the reputation updating phase, to achieve the effective change of reputation values, the logistic model function curve is introduced and the result of the reputation updating is stored in the blockchain for security publicity. Finally, on real datasets, the feasibility and effectiveness of our proposed scheme are demonstrated through the experimental simulation and security analysis. Compared with existing schemes, the proposed scheme not only reduces the cost of data uploading but also has better performance.

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PUEGM: A Method of User Revenue Selection Based on a Publisher-User Evolutionary Game Model for Mobile Crowdsensing

Sensors ◽

10.3390/s19132927 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2927

Author(s):

Zihao Shao ◽

Huiqiang Wang ◽

Guangsheng Feng

Keyword(s):

Data Quality ◽

Quality Assessment ◽

Low Cost ◽

Evolutionary Game ◽

Real Data ◽

Quality Data ◽

Game Model ◽

Mobile Crowdsensing ◽

Data Quality Assessment ◽

Selection Of

Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a low cost. Low-quality data will reduce the efficiency of MCS and lead to a loss of revenue. However, existing work lacks research on the selection of user revenue under the premise of ensuring data quality. In this paper, we propose a Publisher-User Evolutionary Game Model (PUEGM) and a revenue selection method to solve the evolutionary stable equilibrium problem based on non-cooperative evolutionary game theory. Firstly, the choice of user revenue is modeled as a Publisher-User Evolutionary Game Model. Secondly, based on the error-elimination decision theory, we combine a data quality assessment algorithm in the PUEGM, which aims to remove low-quality data and improve the overall quality of user data. Finally, the optimal user revenue strategy under different conditions is obtained from the evolutionary stability strategy (ESS) solution and stability analysis. In order to verify the efficiency of the proposed solutions, extensive experiments using some real data sets are conducted. The experimental results demonstrate that our proposed method has high accuracy of data quality assessment and a reasonable selection of user revenue.

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User Characteristic Aware Participant Selection for Mobile Crowdsensing

Sensors ◽

10.3390/s18113959 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3959 ◽

Cited By ~ 2

Author(s):

Dapeng Wu ◽

Haopeng Li ◽

Ruyan Wang

Keyword(s):

Urban Space ◽

High Heat ◽

Task Completion ◽

User Characteristics ◽

Mobile Crowdsensing ◽

Limited Budget ◽

Participant Selection ◽

History Of ◽

User Characteristic ◽

Multiple Sensing

Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS services. Participant selection should consider the limited budget, the different tasks locations, and deadlines. This selection becomes even more challenging when the MCS tries to efficiently accomplish tasks under different heat regions and collect high-credibility data. In this paper, we propose a user characteristics aware participant selection (UCPS) mechanism to improve the credibility of task data in the sparse user region acquired by the platform and to reduce the task failure rate. First, we estimate the regional heat according to the number of active users, average residence time of users and history of regional sensing tasks, and then we divide urban space into high-heat and low-heat regions. Second, the user state information and sensing task records are combined to calculate the willingness, reputation and activity of users. Finally, the above four factors are comprehensively considered to reasonably select the task participants for different heat regions. We also propose task queuing strategies and community assistance strategies to ensure task allocation rates and task completion rates. The evaluation results show that our mechanism can significantly improve the overall data quality and complete sensing tasks of low-heat regions in a timely and reliable manner.

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Revisiting Mobile Crowdsensing: An Open Challenge

10.5121/csit.2021.111509 ◽

2021 ◽

Author(s):

Vittalis Ayu

Keyword(s):

Data Collection ◽

Data Quality ◽

Context Awareness ◽

Data Exchange ◽

Data Availability ◽

Mobility Patterns ◽

Mobile Crowdsensing ◽

Collection Process ◽

Data Collection Process

Mobile crowdsensing has become a new paradigm that enables citizens to participate in the sensing process by voluntarily gathering data from their smartphones to accomplish some given task. However, performing the sensing task generate lots of data resulting in various quality of the sensed data and high sensing cost in term of resource consumption. This matter became a significant concern in mobile crowdsensing as the mobile nodes which act as crowd sensors have limited resources. Moreover, an opportunistic mobile crowdsensing mechanism does not require user involvement, so the data collection process must be autonomous and intelligent to sense the data in the proper context. That is why context-awareness is also essential in opportunistic crowdsensing to maintain the sensed data quality. In this mini-review, we revisit the possibility of enhancing the mobile crowdsensing mechanism. We argue that improving the data collection process, including context-awareness, can optimize in-node data availability and sensed data quality. Besides, we also argue that finding optimization on inter-node data exchange mechanisms will increase the quality of the in-node data. Furthermore, smartphones that are related to humans as their owners reflect humans' physical and social behavior. We believe that considering contexts such as human social relationships and human mobility patterns can benefit the optimization strategies.

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