Incentive Mechanism for Mobile Crowdsensing with Two-Stage Stackelberg Game

Hu, Chih‐Lin; Lin, Kun-Yu; Chang, Carl K.

doi:10.1109/tsc.2022.3198436

Cited by 19 publications

(7 citation statements)

References 36 publications

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“…There are many studies in the literature on the incentive mechanism [ 32 , 33 , 34 , 35 , 36 ]. An incentive mechanism for privacy protection and data quality awareness is proposed in [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…A personalized task recommendation system is proposed in [ 34 ], which recommends tasks to users based on a recommendation score that comprehensively considers each user’s preferences and reliability. The authors of [ 35 ] propose a game-based incentive mechanism, named Incentive-G, aiming at recruiting mobile users effectively and improving the reliability and quality of sensing data against malicious users. The Incentive-G mechanism consists of several design phases, including analyzing the sensing data, determining the reputations of mobile users, and ensuring data quality and reliability by voting in a task group.…”

Section: Related Workmentioning

confidence: 99%

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Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

He,

Wang,

Zhao

et al. 2024

Sensors

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Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user’s participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user’s profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user’s income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

He,

Wang,

Zhao

et al. 2024

Sensors

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show abstract

“…Ref. [30] motivated mobile users to maintain their reputation in task groups and ensure data quality and reliability by voting in a task group. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Firstly, most research on incentive mechanisms does not properly take into account the impact of uncertainty caused by user preferences on user decisions [28][29][30]. Additionally, few incentive mechanisms that introduce uncertainty take into account user preferences and their associated probabilities.…”

Section: Related Workmentioning

confidence: 99%

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Zhao,

Zeng,

Wang

et al. 2024

Electronics

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Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

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“…It increases the clearance rate but decreases bidders for task accomplishment. According to research [19], Incentive-G is a game-based incentive system designed to efficiently attract mobile users while also boosting the reliability and quality of sensing data against untrustworthy or malicious users. The Incentive-G system comprises numerous design steps, including sensing data analysis, defining mobile user reputations, and assuring data quality and reliability through task group voting.…”

Section: Detailed Literaturementioning

confidence: 99%

Reputation and Quality Aware Incentive Mechanism for Mobile Crowd Sensing Using Smart Devices

Shahzadi,

Ashraf,

Ihsan

et al. 2023

Preprint

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The Internet of Things (also known as IoT), a revolution, has enhanced the relevance of mobile employees' job efficiency in mobile crowd sensing (MCS). Incentives based on reputation for mobile workers (MW) play an important role in increasing service utilization, motivating mobile employees, and developing confidence in the service. The cell phone is the most exploited entity in mobile crowd-sensing MCS and cloud computing. In the underlying investigation, we observed that they had not evaluated the complexity level of a task, resulting in a strong reputation for executing multiple basic tasks. A person who completes a tough task may have a lower reputation score. Complexity levels of tasks (CLT) were developed for reputation evaluation on a crowd-sensing network, which would be utilized to evaluate the MW reputation. We offered simple and complex levels of challenges to assess reputation scores. Tasks receive incentives based on their reputation. By measuring reputation on (CLT) complexity level of task, this research will help the system to Maintain the reputation of the mobile worker so that entities of the system get the maximum benefit out of it, by hiring well-reputed mobile workers and MW receive an incentive on it. Furthermore, we conduct a comparative analysis between our scheme and various machine learning algorithms to identify the algorithm that best performs in evaluating task complexity. Considering both the simple and complex reputation scores, the comparison suggests that after analyzing the data, it is clear that the CLT (proposed) scheme and the Linear regression scheme not only outperformed the Neural Network scheme in terms of Complex Reputation Scores but also in terms of Simple Reputation Scores. We also apply statistical tests, such as p-tests and t-tests, to determine the significance of the results obtained from different algorithms. Considering MW 308, MW 345, and MW 1045's efforts and abilities, the MCS system may offer incentives and awards to further motivate and consider their continuing contributions. This reputation-based incentivization technique not only encourages strong competition among mobile workers, but also makes sure the delivery of high-quality services inside the MCS system.

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Incentive Mechanism for Mobile Crowdsensing with Two-Stage Stackelberg Game

Cited by 19 publications

References 36 publications

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Reputation and Quality Aware Incentive Mechanism for Mobile Crowd Sensing Using Smart Devices

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