Fairness task assignment strategy with distance constraint in Mobile CrowdSensing

Song, Xueting; Wang, En; Liu, Wenbin; Liu, Yujun; Dong, Yunmeng

doi:10.1007/s42486-022-00116-3

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…On scheduling fairness, Li et al [38] ensured that all users have opportunities to participate in performing tasks and earn rewards. And Song et al [39] took the fairness of users and tasks into consideration, while the fairness of the user refers to the full use of each user's ability to process the task, and the fairness of the task means that each task will have a certain frequency of assignment according to its characteristics. The commonality of the abovementioned studies is that the concept of fairness is based on empirical models, and there is a lack of accurate mathematical models to formulate and quantify it.…”

Section: Related Workmentioning

confidence: 99%

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Lu,

Liu,

Jia

et al. 2024

IEEE Trans. Serv. Comput.

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Section: Related Workmentioning

confidence: 99%

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Lu,

Liu,

Jia

et al. 2024

IEEE Trans. Serv. Comput.

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“…Recently, Refs. [7,8,22,23] have started to explore balancing objectives like driver income during the task allocation process. As future scenarios are unpredictable, algorithms allocate orders based only on historical data and current situations.…”

Section: Fairness Mechanism Designmentioning

confidence: 99%

Fairness-Aware Dynamic Ride-Hailing Matching Based on Reinforcement Learning

Liang

2024

Electronics

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The core issue in ridesharing is designing reasonable algorithms to match drivers and passengers. The ridesharing matching problem, influenced by various constraints such as weather, traffic, and supply–demand dynamics in real-world scenarios, requires optimization of multiple objectives like total platform revenue and passenger waiting time. Due to its complexity in terms of constraints and optimization goals, the ridesharing matching problem becomes a central issue in the field of mobile transportation. However, the existing research lacks exploration into the fairness of driver income, and some algorithms are not practically applicable in the industrial context. To address these shortcomings, we have developed a fairness-oriented dynamic matching algorithm for ridesharing, effectively optimizing overall platform efficiency (expected total driver income) and income fairness among drivers (entropy of weighted amortization fairness information between drivers). Firstly, we introduced a temporal dependency of matching outcomes on subsequent matches in the scenario setup and used reinforcement learning to predict these temporal dependencies, overcoming the limitation of traditional matching algorithms that rely solely on historical data and current circumstances for order allocation. Then, we implemented a series of optimization solutions, including the introduction of a time window matching model, pruning operations, and metric representation adjustments, to enhance the algorithm’s adaptability and scalability for large datasets. These solutions also ensure the algorithm’s efficiency. Finally, experiments conducted on real datasets demonstrate that our fairness-oriented algorithm based on reinforcement learning achieves improvements of 81.4%, 28.5%, and 79.7% over traditional algorithms in terms of fairness, platform utility, and matching efficiency, respectively.

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“…In Reference 63, fairness task assignment strategy with distance constraint in mobile crowd sensing is proposed. First model two fairness constraints simultaneously by converting them to user processing queue and task virtual queue.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

“…According to the above explanations, the classification of workflow scheduling methods has been shown in Figure 4. In the first part, the classification of methods is based on the nature of scheduling algorithms, which includes super-heuristic 9 Meta Heuristic methods [5], [16], [21], [35], [36], [37], [50], [51], [52], [54], [55], [56], [57], [58], [61], [62], [63], [64], [65], [66], [67] 9 Heuristic methods [1], [2], [13], [17], [18], [19], [22], [25], [26], [28], [29], [30], [31], [32], [33], [34], [42], [46], [47], [48], [49] 9 F I G U R E 4 Classification of workflow scheduling methods. 12 methods and heuristic methods.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

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The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

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Fairness task assignment strategy with distance constraint in Mobile CrowdSensing

Cited by 4 publications

References 34 publications

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Incentivizing Proportional Fairness for Multi-Task Allocation in Crowdsensing

Fairness-Aware Dynamic Ride-Hailing Matching Based on Reinforcement Learning

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

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