User selection utilizing data properties in mobile crowdsensing

Wang, En; Yang, Yintang; Lou, Kaihao

doi:10.1016/j.ins.2019.03.071

Cited by 21 publications

(7 citation statements)

References 15 publications

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“…In the case of an opportunistic MCS system, the sensor data is acquired autonomously and reported to the cloud periodically without the user involvement [19]. In this method, mobile devices are involved in the process of decision making instead of the users as is the case of participatory crowdsensing [7].…”

Section: B Opportunistic Techniquementioning

confidence: 99%

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

et al. 2020

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With the rapid development of mobile technology and subsequent mass adoption of mobile devices, mobile crowdsensing (MCS) has gained a lot of research attention. In MCS systems, trust is a key focus in the overall improvement in the participant uptake of the sensing tasks. The trust-based scheme of MCS is studied to predict the damage level, the scores of quality-of-service (QoS), and the levels of qualityof-data (QoD) of MCS systems. Users can participate in MCS sensing based on trustworthy indicators that are related to user experience and system reputation, as well as the knowledge obtained about the MCS systems. This paper illustrates the establishment of user confidence during recruitment in MCS as it is very critical for the success of MCS systems and proposes a simulation trust-based mechanism (SiTBaM) approach. The level of MCS security is enhanced to protect the privacy of participants, so that participants can be assured that the MCS system they are working with during sensing moment is trustworthy. The application of SiTBaM in MCS is verified to yield better results as the simulations show that it offers higher QoS levels, QoD scores, as well as low damage levels in the presence of any task or many malicious users. These results were validated through comparisons with other schemes. INDEX TERMS Mobile crowdsensing, quality-of-data, quality-of-service, security, trust-based scheme.

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Section: B Opportunistic Techniquementioning

confidence: 99%

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

et al. 2020

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“…Then, they designed a greedy algorithm to select users to cover the maximum sampling points while the total reward does not exceed the limited budget. In addition to time and space factors, Wang et al [28] also included the data attributes into the mobile crowdsourcing, proposed a method of using the data attributes in the mobile crowdsourcing to select users, and then use the greedy algorithm to select the right users group. Finally, the effectiveness of the proposed method is proved by a large number of experiments on real data.…”

Section: Related Workmentioning

confidence: 99%

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

Yang

Ban

Xing

2019

Sensors

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With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.

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“…The incentive sharing mechanism is used to model the residual sharing process as a cooperative game, and the shapely value method is used to determine the payment for each user. Wang et al [13] proposed a user selection utilizing data properties in mobile crowdsensing (SPM), where a triple-layer structure considering not only the temporal and spatial probability, but also the data’s property is formulated. This method can finish the largest number of sensing tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Up to now, the research works on MCS user revenue mainly focused on the following aspects: revenue maximization [4,5,6,7], which aims at encouraging users to participate in the sensing activity, and data quality assessment [8,9,10,11,12,13,14], which determines how to select a suitable set of users to finish a sensing task. However, almost all the above works ignore two important factors: human bounded rationality and learning capacity.…”

Section: Introductionmentioning

confidence: 99%

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

Shao

Wang

Guang-sheng

2019

Sensors

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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 selection utilizing data properties in mobile crowdsensing

Cited by 21 publications

References 15 publications

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

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

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