BRAKE: Bilateral Privacy-Preserving and Accurate Task Assignment in Fog-Assisted Mobile Crowdsensing

Zeng, Biao; Yan, Xingfu; Zhang, Xinglin; Zhao, Bowen

doi:10.1109/jsyst.2020.3009278

Cited by 12 publications

(2 citation statements)

References 38 publications

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“…In literature [53], according to the problem that sensing tasks are location-dependent and have time features, Yang et al proposed a heterogeneous task allocation algorithm to ensure sensing quality. In literature [54], Zeng et al proposed a task allocation framework based on the multisecret sharing method to preserve location privacy in task allocation. In the process of task allocation, tasks and participants are required to provide secret sharing of their real location information to fog nodes.…”

Section: Task Allocation Methods Considering Time and Spacementioning

confidence: 99%

A Survey of Application and Key Techniques for Mobile Crowdsensing

Wang

2022

Wireless Communications and Mobile Computing

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Mobile crowdsensing (MCS) uses ubiquitous smart devices and network access technology to migrate sensing tasks from a centralized platform to a distributed computing terminal across time and space and provides new approaches for solving large-scale, diversified, and complex sensing problems. MCS research is of great importance and has attracted wide attention from researchers. Accordingly, this article summarizes the current research status of MCS. First, the typical applications of MCS are summarized. Then, the research status of key technologies, such as task allocation, incentivizing, data transmission, quality assurance, and evaluation methods, is considered. Finally, future research directions of MCS are given to provide a reference for in-depth MCS research.

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Section: Task Allocation Methods Considering Time and Spacementioning

confidence: 99%

A Survey of Application and Key Techniques for Mobile Crowdsensing

Wang

2022

Wireless Communications and Mobile Computing

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“…The proposed greedy task selection algorithm can help users choose the appropriate task to maximize the total revenue and realize task assignment. Zeng et al [18] took the execution time of workers as the optimization goal, and proposed an adaptive Top-k worker selection algorithm to select the most appropriate workers and achieve efficient task assignment. Huang et al [19] investigated and formulated the time-dependent task allocation problem, and characterized the cost of performing a sensing task for each mobile user.…”

Section: Related Workmentioning

confidence: 99%

A Task Assignment Method Based on User-Union Clustering and Individual Preferences in Mobile Crowdsensing

Shao

Wang

2022

Wireless Communications and Mobile Computing

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Mobile crowdsensing (MCS) offers a novel paradigm for large-scale sensing with the proliferation of smartphones. Task assignment is a critical problem in mobile crowdsensing (MCS), where service providers attempt to recruit a group of brilliant users to complete the sensing task at a limited cost. However, selecting an appropriate set of users with high quality and low cost is challenging. Existing works of task assignment ignore the data redundancy of large-scale users and the individual preference of service providers, resulting in a significant workload on the sensing platform and inaccurate assignment results. To tackle this issue, we propose a task assignment method based on user-union clustering and individual preferences, which considers the influence of clustering data quality and preference-based sensing cost. Firstly, we design a user-union clustering algorithm (UCA) by defining user similarity and setting user scale, which aims to balance user distribution, reduce data redundancy, and improve the accuracy of high-quality user aggregation. Then, we consider individual preferences of service providers and construct a preference-based task assignment algorithm (PTA) to achieve the diversified sensing cost control needs. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm, which realizes the individual preference-based task assignment under the premise of ensuring high-quality data.

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