An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud

Lin, Yun; Liu, Tundong; Chen, Fufeng; Li, Kuan‐Ching; Xie, Yi

doi:10.1007/s11227-020-03470-0

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Recently, many solutions are advanced regarding the optimization of the offloading process in the edge networks. Offloading task to MCC/MEC platforms has been received lots of attention from the research community [43][44][45][46][47][48]. However, published studies have not considered the optimisation of execution latency subject to task precedence with task constraints and user mobility; this has also been observed in recent work [20,[49][50][51][52].…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

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Section: Metaheuristic Optimizationmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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“…The greater the concentration of disturbance, the more similar the disturbance, which is not conducive to the diversity of the population, and the smaller the selection probability of disturbance. By selecting the probability, the disturbance with high adaptability is selected, and the disturbance with high concentration is suppressed, thereby promoting and suppressing the population F. This selection operation improves the shortcoming of genetic algorithm which is easy to converge prematurely [23], and accelerates the evolution of the population to the optimal disturbance.…”

Section: E Select Operationmentioning

confidence: 99%

A Privacy-Preserving Method Based on Artificial Immune Computing in MCS

Long,

Hao,

Zhang

et al. 2023

IEEE Access

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Due to the widespread use of mobile intelligent terminal devices, Mobile Crowd Sensing (MCS) applications have gained significant research attention. However, ensuring users privacy remains a critical challenge, as it can hinder users' willingness to participate actively in tasks. To address the limitations of existing differential privacy protection methods, this paper proposes a novel privacy protection approach based on Artificial Immune Computing (AICppm). Specifically, private information is concealed within a masking carrier, and data scrambling is avoided. The proposed method involves two main steps: first, a carrier preprocessing approach based on a high-pass filter bank is designed to identify candidate positions for perturbation. Then, a carrier steganography algorithm based on multi-objective optimization is used, transforming the perturbation position into an antibody using the artificial immune algorithm. By iteratively searching for antibodies with higher fitness, the optimal perturbation of the offspring population is identified using the improved Strength Pareto Evolution Algorithm (SPEA2). The experimental results demonstrate that the proposed algorithm can withstand the attacks of malicious steganalysis tools, preserving the integrity of the sensing data and enabling real-time processing of private information.INDEX TERMS Mobile crowd sensing, privacy-preserving, edge computing, artificial immune computing, sensing data

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RETRACTED: Improving mobile cloud computing service efficiency using energy competent and federated learning process

Thirunavukkarasu

Shanmugapriya

2022

Trans Emerging Tel Tech

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Mobile cloud computing (MCC) helps to make effective communication that is used to transmit various information like voice, data, and video. The MCC environments support different real‐time applications, including gaming and virtual reality implications. During the data transmission, energy conservation is one of the major issues for retaining the application span incorporating distributed and personalized computing and resource sharing. Ability‐sustained energy competent method (ASECM) is introduced to overcome these issues and manage energy efficiency. The ASECM approach analyzes the session span and energy requirements for managing the applications' energy factor. In addition, the federated learning and computation offloading process is incorporated to retain the energy factor for ensuring reliable services. The effective utilization of the offloading process is used to meet the application deadline and energy‐sufficient operations, preventing failures. In the recommendation process, cloud service availability with energy management constraints is considered for reducing energy expenditures. This process is nonrecurrent as the user/application requirements rely on diverse resources over different energy management techniques. The performance is measured using energy efficacy, utilization, application span, and failures.

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An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud

Cited by 8 publications

References 23 publications

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

A Privacy-Preserving Method Based on Artificial Immune Computing in MCS

RETRACTED: Improving mobile cloud computing service efficiency using energy competent and federated learning process

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