Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Arif, Muhammad; Ajesh, F.; Shamsudheen, Shermin; Shahzad, Muhammad

doi:10.1155/2022/4937588

Cited by 5 publications

(3 citation statements)

References 36 publications

(36 reference statements)

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“…On the basis of these methods, a security mechanism was introduced in IoT networks for the healthcare applications in different works. These studies implemented closely related existing schemes in the simulation, for instance, delay optimal long short-term memory (LSTM) [ 21 ], workflow metaheuristic system (WFMS), [ 22 ] and workflow metaheuristic cloud (WMC) [ 23 ]. These studies are closely related to our work to execute workflow applications on heterogeneous nodes in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

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Section: Related Workmentioning

confidence: 99%

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

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“…Few research studies which have used the asynchronous learning method, have done so by using it as a regularization method [19], and they lack in detailed methodical research. Furthermore, few research studies [6,[20][21][22] has addressed this issue and explained different methodologies in an effective way.…”

Section: Related Workmentioning

confidence: 99%

Privacy Shield: A System for Edge Computing using Asynchronous Federated Learning

Khalid

Aziz

Fathi

2022

Scientific Programming

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Due to increase in IoT devices, the data produced every day are also increasing rapidly. The growth in data means more processing and more computations are required without delay. This introduced us to a new horizon of the computing infrastructure, i.e., edge computing. Edge computing gained prominence as a solution to the problem of delayed transmission, processing, and response by the cloud architecture. It was further augmented with the field of artificial intelligence. It has become a topic in research with preservation of data privacy as the focal point. This paper provides Privacy Shield, a system for edge computing using asynchronous federated learning, where multiple edge nodes perform federated learning while keeping their private data hidden from one another. Contrary to the pre-existing distributed learning, the suggested system reduces the calls between the edge nodes and the main server during the training process ensuring that there is no negative impact on the accuracy of the mode. We used different values of Ω that affect the accuracy and the compression ratio. In the interval of Ω = [0.2, 0.4], the C-ratio increases and the value of Ω and the compression ratio value are directly proportional regardless of the fluctuations. We analyzed the accuracy of the model which increases along with the increase in compression ratio. Compressing the gradient communications reduces the likelihood of the data of being attacked. As the nodes train asynchronously on limited and different kinds of samples, the binary weights adjustment method was used to handle the resulting “unbalanced” learning. The MNIST and Cifar10 dataset were used for testing in both tasks, respectively.

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“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%

Retracted: Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

and Communication Networks

2024

Security and Communication Networks

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Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Cited by 5 publications

References 36 publications

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Privacy Shield: A System for Edge Computing using Asynchronous Federated Learning

Retracted: Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

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