A new differential evolution algorithm for joint mining decision and resource allocation in a MEC-enabled wireless blockchain network

Wang, Yong; Chen, Chun Rong; Huang, Pei; Wang, Kezhi

doi:10.1016/j.cie.2021.107186

Cited by 21 publications

(7 citation statements)

References 33 publications

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“…DE has an advantage for multi-dimensional problems with a fast convergence rate and can be adapted for continuous and discrete problems. The DE algorithm has FIGURE 3: AI categories been widely adopted for optimization problems, related to MEC utilization, such as to determine UE fine-grained offloading decisions [74], to optimize resource allocation [75] and applied to context-aware offloading strategies [76].…”

Section: ) De Differential Evolutionary Algorithmmentioning

confidence: 99%

“…The authors of [74] proposed DE to determine the UE fine-grained offloading decisions by initialising the network and executing the DE algorithm, namely initialisation, mutation, crossover, and selection, to minimise the UE energy consumption. In [75] a proposed solution is provided for mining decisions. The approach adopts DE to optimise resource allocation.…”

Section: ) De Differential Evolutionary Algorithmmentioning

confidence: 99%

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A Review of Intelligent Computation Offloading in Multiaccess Edge Computing

Jin

Gregory

2022

IEEE Access

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Multi-Access Edge Computing (MEC) is a standardized architecture that enables cloud computing capabilities at the edge of heterogeneous networks. The concept is to reduce network congestion by running applications and network services closer to end-users. MEC is designed to be implemented at key locations on the network edge, including co-location with cellular base stations. MEC aims to facilitate computation intensive and delay sensitive applications, such as vehicular networks, face recognition, augmented reality and virtual reality. The service requirements for MEC are stochastic and time varying. Coupled with advances in artificial intelligence, a vast number of computation offloading approaches have been developed based on intelligent algorithms. This article provides a comprehensive review of intelligent computation offloading with critical issues, metrics and future directions.

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Section: ) De Differential Evolutionary Algorithmmentioning

confidence: 99%

Section: ) De Differential Evolutionary Algorithmmentioning

confidence: 99%

A Review of Intelligent Computation Offloading in Multiaccess Edge Computing

Jin

Gregory

2022

IEEE Access

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“…Mining criteria are the collective set of conditions that need to be met before broadcasting a new node [69]. It is an important decision for the efficiency of the overall network [69].…”

Section: A Theoretical Implicationmentioning

confidence: 99%

“…Mining criteria are the collective set of conditions that need to be met before broadcasting a new node [69]. It is an important decision for the efficiency of the overall network [69]. Once new data comes in and partially trains the model, the system decides whether to update the model or not (creating a new block).…”

Section: A Theoretical Implicationmentioning

confidence: 99%

Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach

et al. 2022

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Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other hand, data-driven technologies like machine learning need authentic data to perform precisely in real-world systems. This study proposes a blockchain and smart contract-based approach to achieve robust Machine Learning (ML) algorithm for e-commerce fraud detection by facilitating interorganizational collaboration. The proposed method uses blockchain to secure the privacy of the data. Smart contract deployed inside the network fully automates the system. An ML model is incrementally upgraded from collaborative data provided by the organizations connected to the blockchain. To incentivize the organizations, we have introduced an incentive mechanism that is adaptive to the difficulty level in updating a model. The organizations receive incentives based on the difficulty faced in updating the ML model. A mining criterion has been proposed to mine the block efficiently. And finally, the blockchain network is tested under different difficulty levels and under different volumes of data to test its efficiency. The model achieved 98.93% testing accuracy and 98.22% Fbeta score (recall-biased f measure) over eight incremental updates.Our experiment shows that both data volume and difficulty level of blockchain impacts the mining time. For difficulty level less than five, mining time and difficulty level has a positive correlation. For difficulty level two and three, less than a second is required to mine a block in our system. Difficulty level five poses much more difficulties to mine the blocks.INDEX TERMS blockchain, collaborative machine learning, incremental learning, privacy, smart contract Dr. RASHEDUR M. RAHMAN works as a Professor in the Electrical and Computer Engineering Department

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“…Due to the powerful optimization performance, DE has been successfully applied in many of fields [22][23][24]. Inspired by these successful applications, a novel DE algorithm is designed for optimization the allocation of WPT time in this work.…”

Section: The Allocation Of Wpt Time Based On De Differential Evolutio...mentioning

confidence: 99%

Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing

Dong

Wan

Deng

et al. 2022

Wireless Communications and Mobile Computing

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Wireless-powered mobile edge computing is a new network computing paradigm that combines with the advantages of wireless power transfer and mobile edge computing. When the harvest-then-offload protocol is adopted in this network, the time of wireless power transfer has a significant impact on system performance. If the time is too short, the user cannot harvest enough energy. If it is too long, the user will not have enough time to complete the task offloading. Both result in many of user tasks being discarded. To address this problem, DEWPT, a differential evolution-based optimization scheme for wireless power transfer time, is proposed in this paper. DEWPT is designed with a hybrid mutation operator and a perturbation-based binomial crossover operator. The hybrid mutation operator combines the benefits of two mutation operators with distinct characteristics, so that DEWPT not only has a strong exploration ability but also can quickly converge. Meanwhile, the perturbation-based binomial crossover operator improves DEWPT’s ability to exploit local space. These two improvements effectively enhance DEWPT’s optimization performance, which is beneficial to find the optimal time for wireless power transfer. Furthermore, to improve the optimization efficiency, micro-population is introduced into DEWPT. Finally, the computation completion ratio maximization model is used to validate the performance of DEWPT in the wireless-powered mobile edge computing network with multiple edge servers. Numerical results show that the computation offloading scheme integrating with DEWPT can achieve a higher computation completion rate than three benchmark schemes, and is competitive in complexity. This demonstrates that DEWPT is an effective time allocation scheme for wireless power transfer.

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A new differential evolution algorithm for joint mining decision and resource allocation in a MEC-enabled wireless blockchain network

Cited by 21 publications

References 33 publications

A Review of Intelligent Computation Offloading in Multiaccess Edge Computing

A Review of Intelligent Computation Offloading in Multiaccess Edge Computing

Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach

Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing

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