Multi-edge collaborative offloading and energy threshold-based task migration in mobile edge computing environment

Li, Chunlin; Cai, Qiqin; Luo, Yingwei

doi:10.1007/s11276-021-02776-y

Cited by 14 publications

(6 citation statements)

References 28 publications

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“… Find the best value for the strong team The process is similar to the previous steps. The fitness rate of gBest (the best in the world) for all is compared and the particle fitness rate is determined by whether the optimal particle position is adjusted [ 19 ]. Particle position and velocity update After the abovementioned comparison of the fitness values, the position and velocity of the particle are updated according to the expression of the correlation function.…”

Section: Research On Optimal Scheduling Algorithmmentioning

confidence: 99%

“…The process is similar to the previous steps. The fitness rate of gBest (the best in the world) for all is compared and the particle fitness rate is determined by whether the optimal particle position is adjusted [ 19 ].…”

Section: Research On Optimal Scheduling Algorithmmentioning

confidence: 99%

“…Formula one Formula two Formula three 0.4 compared and the particle fitness rate is determined by whether the optimal particle position is adjusted [19].…”

Section: Weighting Factormentioning

confidence: 99%

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[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.

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Section: Research On Optimal Scheduling Algorithmmentioning

confidence: 99%

Section: Research On Optimal Scheduling Algorithmmentioning

confidence: 99%

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[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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“…To solve this problem, they presented an iterative distributed method that converted the non‐convex problem into a two‐level optimization problem (higher and lower level). Li et al 102 proposed a multi‐edge collaborative CO model based on an improved GA to address the issue of under‐utilization of offsite edge clouds in the traditional offloading scenario, which was caused by the idle state in offsite edge clouds due to the existence of an empty window period for computing task arrival. Authors developed a service migration model with the sum of ECp and latency as the optimization target, implemented a dynamic selection of EdSrs, and designed a service migration policy that considered the service execution cost and data transmission cost of mobile servers.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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“…In order to facilitate learners to manage learning materials, the function of deleting learning materials is provided. Unnecessary learning materials can be deleted [20].…”

Section: Learning Materials Managementmentioning

confidence: 99%

Mobile Education System Based on Genetic Algorithm

Dang

2022

Mobile Information Systems

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In order to solve the problems existing in the current mobile education system, this paper proposes a method of mobile education system based on genetic algorithm. By analyzing the research status of mobile learning and the theory and application mode of mobile learning, this method explores the combination of mobile learning theory and application mode and China’s traditional primary and secondary education mode, then puts forward a mobile teaching model suitable for primary and secondary education, and builds a mobile teaching system on this basis. The results show that, through the analysis, it is found that most of them remain at about 10%. In practice, the occupation is slightly increased only when multiple people teach, but it also belongs to an acceptable normal level, which can meet more complex functions in the future. The function of mobile learning platform needs to be expanded. For example, mobile learning forum can provide learners with a platform for communication, discussion, and sharing learning materials.

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Multi-edge collaborative offloading and energy threshold-based task migration in mobile edge computing environment

Cited by 14 publications

References 28 publications

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Mobile Education System Based on Genetic Algorithm

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