Participant Service Quality Aware Data Collecting Mechanism With High Coverage for Mobile Crowdsensing

Jing, Yang; Fu, Lei; Yang, Boran; Xu, Jialiang

doi:10.1109/access.2020.2965734

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…Peng et al [13] extend the Expectation Maximization algorithm that combines maximum likelihood estimation and Bayesian inference to estimate the quality of sensing data. To ensure data integrity and real-time collection, Yang et al [14] propose a data-collecting mechanism based on the greedy algorithm. However, the above research work only pays attention to the implication from the signal aspect while neglecting the significance of the semantic factors.…”

Section: Related Workmentioning

confidence: 99%

Data-Quality Aware Incentive Mechanism Based on Stackelberg Game in Mobile Edge Computing

LUO,

WANG,

et al. 2024

IEICE Trans. Fundamentals

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Crowdsourcing becomes a popular data-collection method to relieve the burden of high cost and latency for data-gathering. Since the involved users in crowdsourcing are volunteers, need incentives to encourage them to provide data. However, the current incentive mechanisms mostly pay attention to the data quantity, while ignoring the data quality. In this paper, we design a Data-quality awaRe IncentiVe mEchanism (DRIVE) for collaborative tasks based on the Stackelberg game to motivate users with high quality, the highlight of which is the dynamic reward allocation scheme based on the proposed data quality evaluation method. In order to guarantee the data quality evaluation response in real-time, we introduce the mobile edge computing framework. Finally, one case study is given and its real-data experiments demonstrate the superior performance of DRIVE.

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Section: Related Workmentioning

confidence: 99%

Data-Quality Aware Incentive Mechanism Based on Stackelberg Game in Mobile Edge Computing

LUO,

WANG,

et al. 2024

IEICE Trans. Fundamentals

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“…Some studies aim to improve incentive mechanisms by finding the optimal solution. For example, Yang et al evaluated the coverage based on the number of target points covered by the participants [19]. This study selected the most-efficient sensing data under the limited platform budget so that the coverage of sensing data could be effectively optimized.…”

Section: Current Study Of Incentive Mechanisms For Crowdsensingmentioning

confidence: 99%

“…Then, we let dκ i ( bi) dω i = 0. By solving this equation, we can obtain the optimal share number of participants as shown in the following Formula (19):…”

Section: Optimal Decision-making Of Participants In the Maintenance M...mentioning

confidence: 99%

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Liu,

Xu,

Deng

et al. 2023

Mathematics

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Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expectation; (2) many incentives will affect the effectiveness because of the decision problem caused by asymmetric information. Inspired by ensemble learning and prospect theory, this paper proposes the Incentive Mechanism based on Ensemble Learning and Prospect Theory (IMELPT). First, we propose the Deep-Stacking-Generation algorithm based on Dropout (DSGD), to predict the participants and distinguish whether they are long-term or short-term participants. If the participants are short-term, we incentivize them through the Short-term Participant Incentive Mechanism based on Prospect Theory (SPIMPT). We increase the participation duration by transforming the change in reward into asymmetric information that aligns the participant’s goal with the platform. If the participants are long-term participants, we motivate them through the Long-term Participant Incentive Mechanism (LPIM), to maintain the participation rate of participants by maximizing their utility. Theoretical analysis and experiments on real datasets demonstrated that IMELPT can reliably improve the coverage of crowdsensing tasks.

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“…In [27], a mechanism was introduced to transmit sensed data to the data requester according to the time constraint. In [28], the researchers aimed to collect sensed data from workers that have high coverage properties of MCS fulfilled. The authors in [29] proposed a data-collection path-planning scheme that employed either cellular transmission or an opportunistic network.S.…”

Section: Task Allocationmentioning

confidence: 99%

Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm

Ipaye

Chen

Asim

et al. 2022

Sensors

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Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

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Participant Service Quality Aware Data Collecting Mechanism With High Coverage for Mobile Crowdsensing

Cited by 10 publications

References 42 publications

Data-Quality Aware Incentive Mechanism Based on Stackelberg Game in Mobile Edge Computing

Data-Quality Aware Incentive Mechanism Based on Stackelberg Game in Mobile Edge Computing

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm

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