Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

Yang, Guisong; Wang, Buye; He, Xingyu; Wang, Jiangtao; Pervaiz, Haris

doi:10.1109/tnsm.2021.3072638

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…Therefore, they proposed a task allocation task method that took task information, such as time and location, into consideration. Authors in [40] investigated a task allocation problem by considering the competition of participants for tasks. They employed the congestion game theory to improve participant satisfaction by considering participant benefit, preference and designed a competition congestion metric.…”

Section: Task Allocation Methods For Mcsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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Mobile crowd sensing (MCS) with human participants has been proposed as an efficient way of collecting data for smart cities applications. However, there often exist situations where humans are not able or reluctant to reach the target areas, due to for example traffic jams or bad road conditions. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a task allocation method, called "UMA" (UAV-assisted Multi-task Allocation method) to optimize the sensing coverage and data quality. The method incentivizes human participants to contribute sensing data from nearby points of interest (PoIs), with a limited budget. Meanwhile, the method jointly considers the optimization of task assignment and trajectory scheduling. It schedules the trajectories of UAVs, considering the locations of human participants, other UAVs and PoIs which are rarely visited by human participants. In detail, UAVs take care of two tasks in our proposal. One is to calibrate the data collected by the human participants whom the UAVs come across along their trajectories. The other is to collect data from the PoIs which are not covered by other UAVs or human participants. We apply deep reinforcement learning to schedule UAVs moving trajectories and sensing activities in order to minimize the overall energy cost. We evaluate the proposed scheme via simulation using two real data sets. The results show that our proposal outperforms the compared methods, in terms of coverage completed ratio, calibrating ratio, energy efficiency, and task fairness.

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Section: Task Allocation Methods For Mcsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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“…Efficient crowdworker recruitment plays a pivotal role in the success of crowdsourcing campaigns [27]. Traditionally, crowdsourcing applications employ a direct approach, where the platform directly chooses and enlists suitable individuals for task execution [28].…”

Section: Crowdworker Recruitmentmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

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This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

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“…Study [24] proposes a multi-strategy repeated game task allocation mechanism that provides high-quality services to users and ensures the authenticity of user data without manipulating their bids. Another game-theoretic approach introduced in [25] is the worker decision game based on congestion game theory, considering the competitive behavior among users. This effectively improves user satisfaction and platform benefits.…”

Section: Related Workmentioning

confidence: 99%

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

Xiao

Han

et al. 2023

Electronics

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With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.

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Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

Cited by 13 publications

References 34 publications

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation

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