MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems

Wang, Yuting; Han, Xiaofan; Jin, Shunfu

doi:10.1007/s11276-022-03099-2

Cited by 69 publications

(20 citation statements)

References 46 publications

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“…9 , it is observed that the increase in PSNR and the MAE value is decreased for the best detection of fake faces in the video image and the faces of the real video image. The proposed work obtains a minimum error rate ( Kong et al, 2020 ; Wu, Jin & Yue, 2022 ; Wang, Han & Jin, 2022 ). As a result, the value of PSNR is increased, and the MAE value is decreased compared to the existing models.…”

Section: Resultsmentioning

confidence: 99%

VIOLA jones algorithm with capsule graph network for deepfake detection

Venkatachalam

Trojovský

Hubálovský

2023

PeerJ Computer Science

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DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones’s (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.

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Section: Resultsmentioning

confidence: 99%

VIOLA jones algorithm with capsule graph network for deepfake detection

Venkatachalam

Trojovský

Hubálovský

2023

PeerJ Computer Science

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“…They then provided performance metrics for tasks from three perspectives: average delay, levels of energy consumption, and the availability of computational resources. In [23], a joint task offloading and resource allocation scheme in MEC, assisted by moving vehicles, was proposed. This method allocates tasks to edge servers at base stations or vehicles, while efficiently distributing the resources of base stations to minimize the overall weighted task processing delay for all devices.…”

Section: Computing Offloading and Resource Allocation In Mecmentioning

confidence: 99%

Collaborative Task Processing for Multiple Edge Nodes Based on Service Placement

Shi,

Feng,

et al. 2024

Preprint

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With the rapid development of the Internet of Things (IoT), Mobile Edge Com-puting (MEC) technology is shifting computational and storage capabilities fromcentralized clouds to the network edge to meet the low-latency demands ofnumerous emerging applications. However, ensuring quality of service (QoS) formobile users becomes challenging in dense, decentralized wireless communica-tion environments and with limited MEC server storage capacity. Against thisbackground, this paper proposes a collaborative task processing model for mul-tiple ENs based on service placement and formulates a MINLP optimizationproblem aimed at minimizing system latency and cost. To address this problem,we introduce an online optimization algorithm (OPDA) based on the Lyapunovframework which operates in real-time without the need to predict future infor-mation. Subsequently, we decompose the long-term optimization problem into aseries of one-time slot problems and design a two-stage one-time slot optimiza-tion algorithm to obtain an approximate optimal solution. Specifically, we usethe Lagrange multiplier approach to solve the resource allocation problem fortasks and the matching theory to solve the offloading decision and service place-ment problem for tasks. Simulation results show that our algorithm can achievenear-optimal latency performance while satisfying long-term cost constraints.

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“…Based on the aforementioned materials and methods, in this work, Normalized Hellinger Feature Selection based Soft Margin Boosting (NHFS‐SMB) based water quality prediction is proposed (Ge et al, 2019; Liu et al, 2022). An elaborate description of the proposed NHFS‐SMB method is presented in the forthcoming sections (Wang et al, 2022; Zheng & Jin, 2022).…”

Section: Related Workmentioning

confidence: 99%

Normalized hellinger feature selection and soft margin boosting classification for water quality prediction

Karuppannan

Jayagopal

2023

Expert Systems

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Several research works have been introduced using classification methods for predicting water quality. However, the accuracy level with which the prediction was made and the time consumed in the prediction was not found to be satisfactory. In order to solve these problems, water quality forecasting is done using machine learning and deep learning techniques. The study introduces an efficient water quality prediction called, Normalized Hellinger Feature Selection based Soft Margin Boosting (NHFS‐SMB) is developed based on pre‐processing, feature selection, and classification with higher accuracy and lesser time consumption. The Hellinger Similarity Feature selection model is used to select relevant features for water quality prediction. SMBC is an ensemble classifier that combines several weak classifiers (i.e., associative classifier) to provide the final prediction results as normal or contaminated conditions with lesser error rates. Experimental evaluation is determined by the factors such as accuracy, precision, recall, F1‐score, and ROC Curve with respect to the number of data points.

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MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems

Cited by 69 publications

References 46 publications

VIOLA jones algorithm with capsule graph network for deepfake detection

VIOLA jones algorithm with capsule graph network for deepfake detection

Collaborative Task Processing for Multiple Edge Nodes Based on Service Placement

Normalized hellinger feature selection and soft margin boosting classification for water quality prediction

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