Computation Offloading in Hierarchical Multi-Access Edge Computing Based on Contract Theory and Bayesian Matching Game

Su, Chunxia; Ye, Fang; Liu, Tingting; Tian, Yuan; Han, Zhu

doi:10.1109/tvt.2020.3022766

Cited by 29 publications

(14 citation statements)

References 41 publications

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“…Among the wide variety of applications of contract theory in wireless communications and networking (e.g., cognitive radio networks [10], Device-to-Device (D2D) communications [11], crowdsourcing [12], resource allocation [13]), some effort has been made in the direction of computation offloading. In [14], the problem of incentivization of potential temporary edge nodes from an edge computing operator is examined under a MEC paradigm. Similar problems are considered in [15], [16] under the concept of vehicular edge/fog computing offered by vehicles to other travelling vehicles or roadside users.…”

Section: A Related Workmentioning

confidence: 99%

“…Focusing on the practical application of contract theory models, most of the existing works in the literature, including the aforementioned ones in [10]- [12], [14]- [17], rely on one-dimensional user types that typically capture each user's level of willingness or ability to participate in the contract. Nevertheless, such an approach appears to be rather restrictive, since in most cases there are more than one distinguishing features for each user that should steer the contract modeling, especially when these features are conflicting.…”

Section: A Related Workmentioning

confidence: 99%

“…Hence, the multi-dimensional contract problem to be solved by the edge server can be formally written as: (13), (14).…”

Section: B Contract Formulationmentioning

confidence: 99%

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Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Diamanti

Charatsaris

Tsiropoulou

et al. 2022

IEEE Open J. Commun. Soc.

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The edge computing paradigm has become extremely popular over the past years, as a means of offloading computationally intensive tasks by users of resource and battery-constrained devices. Nevertheless, the edge networks' overexploitation by the ever-increasing number of task-offloading users, gradually leads to their performance degradation. In this paper, we leverage on the different levels of available computing capabilities across the network, and we design an incentive mechanism that aims to shift the selfish users' preference from the edge to the upper fog computing layer, accounting for their level of delay tolerance. To deal with the users' heterogeneity in terms of their applications' multidimensional distinctive features (including their delay tolerance/sensitivity), a multi-dimensional contract theory modeling is adopted, according to which the edge server determines the bundles of the users' provided efforts and corresponding offered rewards. In this respect, each user's effort represents the amount of its initially offloaded task at the edge that is allowed to be further forwarded and processed at the fog. Considering that the users-to-edge server offloading is performed under Non-Orthogonal Multiple Access (NOMA), the problem of joint computation task offloading and uplink transmission power allocation is subsequently addressed via a Stackelberg game, where the edge server and the users are treated as leader and followers, respectively. The aim of the game is to minimize the end-to-end network's energy consumption and increase its resource utilization efficiency. The incentive mechanism and resource allocation framework is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

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Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Diamanti

Charatsaris

Tsiropoulou

et al. 2022

IEEE Open J. Commun. Soc.

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“…Based on the above labels, we can predict the brand preference of cross-border e-commerce consumers. Bayesian personalized ranking method (BPR) obtains the correct ranking of brands based on Bayesian theory [20], as shown in formula (13):…”

Section: Regression Algorithm Of Brand Preference Predictionmentioning

confidence: 99%

“…e proposed method, literature [4] method, and literature [5] method are used to calculate the brand emotion tendency of the above consumers, and the calculation accuracy of different methods is compared, as shown in formula (20):…”

Section: Calculation Of Brand Emotional Tendencymentioning

confidence: 99%

Brand Preference Prediction Method of Cross-Border E-Commerce Consumers Based on Potential Tag Mining

Qin

2022

Mathematical Problems in Engineering

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There are many brands in cross-border e-commerce platforms. Obtaining consumers’ preference for brands will help promote the development of cross-border e-commerce industry. A brand preference prediction method of cross-border e-commerce consumers based on potential tag mining is proposed. Preprocess the cross-border e-commerce brand comment information obtained, build a HowNet emotion dictionary, and calculate consumers’ emotional tendency towards the brand on this basis. The projection pursuit regression model is optimized by differential evolution algorithm to reduce the dimension of the obtained consumer brand emotion information. Mining the potential labels of the information after dimensionality reduction, combined with Bayesian personalized sorting method and paired interaction tensor decomposition method, this paper constructs a brand preference’s prediction model to predict the brand preference of cross-border e-commerce consumers. The experimental results show that the proposed method has high accuracy of brand tendency calculation results, small average absolute error of prediction results, and high model accuracy.

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Research on Edge Computing Offloading Algorithm Based Contract-Design Theory

Peng

2024

Lecture Notes in Electrical Engineering

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Computation Offloading in Hierarchical Multi-Access Edge Computing Based on Contract Theory and Bayesian Matching Game

Cited by 29 publications

References 41 publications

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Brand Preference Prediction Method of Cross-Border E-Commerce Consumers Based on Potential Tag Mining

Research on Edge Computing Offloading Algorithm Based Contract-Design Theory

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