Task recommendation method for fusion of multi-view social relationship learning and reasoning in the mobile crowd sensing system

Wang, Jian; Zhang, Zhe; Zhao, Guosheng

doi:10.1016/j.comcom.2023.04.028

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…In this paper's OCF game task allocation model, when a task is not eventually completed, the users who perform the task coalition are penalised by a reduction in their revenue. When the task t i is not completed, the failure penalty of user p j is calculated by Formula (10).…”

Section: Task Failure Punishmentmentioning

confidence: 99%

“…Compared to other sensing models, MCS can significantly reduce sensing and maintenance costs and leverage the "strength in numbers" feature to carry out tasks and collect data in a defined area with high accuracy [4,5]. As a result, the MCS service model has been increasingly utilized in various fields, including traffic detection [6,7], environmental monitoring [8,9], social networking [10,11], and healthcare [12,13].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Task Failure Punishmentmentioning

confidence: 99%