A Multi-Objective Video Crowdsourcing Method in Mobile Environment

Yan, Chao; Chen, Yuhao; Wang, Fan; Wen, Yiping; Fu, Shucun; Huang, Wanli

doi:10.1109/access.2019.2940955

Cited by 4 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The TOPSIS method is developed for solving multicriteria decision making problems on the basis that the best alternative is the one with the shortest distance from the positive ideal solution and the farthest from the negative ideal solution [27]- [29]. However, the traditional TOPSIS method has the following disadvantages:…”

Section: E Improved Topsis Methodsmentioning

confidence: 99%

Energy Efficiency Evaluation and Optimization of Industrial Park Customers Based on PSR Model and Improved Grey-TOPSIS Method

Zhao

2021

IEEE Access

View full text Add to dashboard Cite

Energy efficiency evaluation of industrial park customers is carried out to help customers develop optimization strategies and improve the energy efficiency level. The evaluation indexes are proposed based on pressure-state-response (PSR) model and the redundant indexes are selected by combining principal component analysis (PCA) and correlation analysis. Thus the dynamic energy efficiency index system is constructed. The weight of indexes is calculated by the entropy weight method (EWM). An energy efficiency evaluation model based on improved Grey-technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which replaces the Euclidean distance by weighted grey correlation degree and can accurately evaluate customers' energy efficiency level. An energy efficiency optimization model is proposed, which can maximize optimization benefits while improving energy efficiency. The validity of the evaluation and optimization models is verified by the case study of an industrial park.INDEX TERMS Energy efficiency evaluation, industrial park customers, pressure-state-response model, Grey-technique for order preference by similarity to ideal solution, energy efficiency optimization This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

show abstract

Section: E Improved Topsis Methodsmentioning

confidence: 99%

Energy Efficiency Evaluation and Optimization of Industrial Park Customers Based on PSR Model and Improved Grey-TOPSIS Method

Zhao

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In this context, the authors of [14] have proposed a joint Bayesian learning and graph optimization framework achieving privacypreserving and fast convergence over a heterogeneous and sparse IoT graph without assumptions on the prior knowledge of the data distribution across the nodes. Yan et al [15] proposed an algorithm to enhance High Definition (HD) and Ultra High Definition (UHD) video services for mobile users through MCS participants that download and transmit video segments. Hamrouni et al [6] proposed a photo-based MCS architecture that monitors the quality of the photos submitted by reporters to cover ongoing events.…”

Section: Related Workmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

View full text Add to dashboard Cite

Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

show abstract

“…Xiong et al [9] estimated accuracy of a worker by evaluating her performance on the completed tasks, and predicting which tasks the worker is well acquainted with. Yan et al [10] proposed an algorithm to enhance HD and UHD video services for mobile users through crowdsourcing participants that download and transmit video segments. Mavridis et al [11] modeled tasks/workers using a hierarchical skill tree, which can map workers to tasks in a way that exploits the natural hierarchy among the skills.…”

Section: Related Workmentioning

confidence: 99%

“…In (10), the condition t w = 1 refers to the case of a fixed worker w. The term LoS w is its LoS and it represents its reach range R w . Also, the term P is a coefficient defined by the platform that converts the unit of traveled distance to a reward unit, e.g., monetary unit (MU).…”

Section: A Decision Variables and Constraintsmentioning

confidence: 99%

Many-to-Many Recruitment and Scheduling in Spatial Mobile Crowdsourcing

2020

View full text Add to dashboard Cite

Spatial mobile crowdsourcing (SMCS) enables a task requester to commission workers to physically travel to specific locations to perform a set of spatial assignments (i.e., tasks are related to a specific geographical location besides time). To efficiently perform such tasks and guarantee the best possible quality of returned results, optimizing the worker recruitment and task assignment processes must be conducted. Because both workers and task requesters impose certain criteria, this procedure is not obvious. To tackle this issue, we propose a novel formulation of the SMCS recruitment where task matching and worker scheduling are jointly optimized. A Mixed Integer Linear Program (MILP) is first developed to optimally maximize the quality of matching measured as a weighted score function of different recruitment metrics while determining the trajectory of each selected worker executing tasks. To cope with NP-hardness, we propose a heuristic SMCS recruitment approach allowing the achievement of sub-optimal matching and recruitment solution by iteratively solving a weighted bipartite graph problem. Simulation results illustrate the performance of the SMCS framework for selected scenarios and show that our proposed SMCS recruitment algorithm outperforms an existing greedy recruitment approach. Moreover, compared to the optimal MILP solution, the proposed SMCS recruitment approach achieves close results with significant computational time saving.

show abstract

A Multi-Objective Video Crowdsourcing Method in Mobile Environment

Cited by 4 publications

References 49 publications

Energy Efficiency Evaluation and Optimization of Industrial Park Customers Based on PSR Model and Improved Grey-TOPSIS Method

Energy Efficiency Evaluation and Optimization of Industrial Park Customers Based on PSR Model and Improved Grey-TOPSIS Method

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Many-to-Many Recruitment and Scheduling in Spatial Mobile Crowdsourcing

Contact Info

Product

Resources

About