A survey of mobile edge computing in developing countries: challenges and prospects

Egwuche, O S; Ganiyu, Mutiu; Ibiyomi, M A

doi:10.1088/1742-6596/2034/1/012004

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Efficient computation offloading in MEC has become an effective technique to bring down the latency constraints that are usually associated with geo-dispersed cloud computing [29] [30]. In order to reduce transmission latency, a MEC server is placed near the terminals [31] [32]. Edge computing can effectively improve the security and reliability of cloud systems [33][34] [35].…”

Section: Introductionmentioning

confidence: 99%

An Energy and Latency Trade-off for Resources Allocation in a MEC System

El Ghmary,

Ziani,

Chanyour

et al. 2023

Int. J. Interact. Mob. Technol.

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This paper addresses the issue of efficient resource allocation in a Mobile Edge Computing (MEC) system, taking into account the trade-off between energy consumption and operation latency. The increasing deployment of connected devices and data-intensive services in the Internet of Things (IoT) poses significant challenges in terms of managing computational resources. In this study, we propose a MEC system model that considers energy constraints and the need to minimize latency to ensure optimal performance. We formulate the resource allocation problem in terms of a trade-off between energy consumption and latency, and explore solutions based on heuristic task offloading techniques. Our experiments demonstrate that our approach achieves improved latency performance while reducing energy consumption. We also evaluate the impact of various parameters, such as workload and resource availability, on the energy-latency trade-off.

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Section: Introductionmentioning

confidence: 99%

An Energy and Latency Trade-off for Resources Allocation in a MEC System

El Ghmary,

Ziani,

Chanyour

et al. 2023

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…On the other hand, edge computing can minimize the reaction time, increase the data processing capacity, ensure data security (since it is closer to end-users, it provides greater privacy) [30], be easy to design, and be cheap [31]. It has excellent application value and features such as high reliability, superior energy savings [32], low latency, and high real-time processing, increasing the overall data quality and utilization performance under the premise of efficient processing [33].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

Contreras-Luján

García-Guerrero

López-Bonilla

et al. 2022

MCA

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Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.

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A survey of mobile edge computing in developing countries: challenges and prospects

Cited by 2 publications

References 7 publications

An Energy and Latency Trade-off for Resources Allocation in a MEC System

An Energy and Latency Trade-off for Resources Allocation in a MEC System

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

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