Performance Evaluation of Fog-Computing Based on IoT Healthcare Application

2021 ◽  
Author(s):  
Amer Sallam ◽  
Akram A. Almohammedi ◽  
Abdulguddoos S. A. Gaid ◽  
Y. A. Shihab ◽  
Mahran Sadeq ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fog Computing ◽  
Healthcare Application

scholarly journals Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45706-45720
Author(s):  
P. G. Shynu ◽  
Varun G. Menon ◽  
R. Lakshmana Kumar ◽  
Seifedine Kadry ◽  
Yunyoung Nam
Keyword(s):  
Fog Computing ◽  
Disease Prediction ◽  
Healthcare Application

scholarly journals Impacto de LoRaWAN no Desempenho de Plataformas de IoT baseadas em Nuvem e Névoa Computacional

2019 ◽  
Author(s):  
Ivan Zyrianoff ◽  
Alexandre Heideker ◽  
Dener Ottolini Silva ◽  
João Henrique Kleinschmidt ◽  
Carlos Alberto Kamienski
Keyword(s):  
Performance Evaluation ◽  
System Performance ◽  
New Technology ◽  
Fog Computing ◽  
Data Communication ◽  
Evaluation Study ◽  
Low Energy ◽  
Energy Costs ◽  
Computational Aspects ◽  
Iot Platforms

LoRaWAN is a new technology that has been consolidating as a key data communication component to send data in IoT-based systems, due to its ability to send data over long distances with low energy costs. However, literature considers only wireless aspects, disregarding its computational aspects and its integration with IoT platforms, as well as ignoring the deployment possibilities that involve cloud and fog computing. In order to understand the computational impacts of the LoRa architecture we performed a careful performance evaluation study in a complex IoT scenario, exploring cloud and fog computing scenarios and integrating with the IoT FIWARE platform. The results show that the LoRaWAN architecture is scalable, but it has impacts on system performance.

scholarly journals Container Migration in the Fog: A Performance Evaluation

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1488 ◽  
Author(s):  
Carlo Puliafito ◽  
Carlo Vallati ◽  
Enzo Mingozzi ◽  
Giovanni Merlino ◽  
Francesco Longo ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Essential Feature ◽  
Fog Computing ◽  
Use Cases ◽  
Comprehensive Overview ◽  
Smart Transportation ◽  
Iot Devices ◽  
Smart Industry ◽  
The Internet Of Things ◽  
Migration Technique

The internet of things (IoT) is essential for the implementation of applications and services that require the ability to sense the surrounding environment through sensors and modify it through actuators. However, IoT devices usually have limited computing capabilities and hence are not always sufficient to directly host resource-intensive services. Fog computing, which extends and complements the cloud, can support the IoT with computing resources and services that are deployed close to where data are sensed and actions need to be performed. Virtualisation is an essential feature in the cloud as in the fog, and containers have been recently getting much popularity to encapsulate fog services. Besides, container migration among fog nodes may enable several emerging use cases in different IoT domains (e.g., smart transportation, smart industry). In this paper, we first report container migration use cases in the fog and discuss containerisation. We then provide a comprehensive overview of the state-of-the-art migration techniques for containers, i.e., cold, pre-copy, post-copy, and hybrid migrations. The main contribution of this work is the extensive performance evaluation of these techniques that we conducted over a real fog computing testbed. The obtained results shed light on container migration within fog computing environments by clarifying, in general, which migration technique might be the most appropriate under certain network and service conditions.

scholarly journals Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1633
Author(s):  
Belal Sudqi Khater ◽  
Ainuddin Wahid Abdul Abdul Wahab ◽  
Mohd Yamani Idna Idris ◽  
Mohammed Abdulla Hussain ◽  
Ashraf Ahmed Ibrahim ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Performance Evaluation ◽  
Sparse Matrix ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Space Representation ◽  
Extraction Stage ◽  
N Gram ◽  
Selection Stage

In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In addition, a Sparse Matrix is used to reduce the space by keeping only the weight of the features that appear in the trace, thus ignoring the zero weights. Subsequently, Linear Correlation Coefficient (LCC) is utilized to compensate for any missing N-gram in the test data. In the feature selection stage, the Mutual Information (MI) method and Principle Component Analysis (PCA) are utilized and then compared to reduce the number of input features. Following the feature selection stage, the modeling and performance evaluation of various Machine Learning classifiers are conducted using a Raspberry Pi IoT device. Further analysis of the effect of MLP parameters, such as the number of nodes, number of features, activation, solver, and regularization parameters, is also conducted. From the simulation, it can be seen that different parameters affect the accuracy and lightweight evaluation. By using a single hidden layer and four nodes, the proposed method with MI can achieve 96% accuracy, 97% recall, 96% F1-Measure, 5% False Positive Rate (FPR), highest curve of Receiver Operating Characteristic (ROC), and 96% Area Under the Curve (AUC). It also achieved low CPU time usage of 4.404 (ms) milliseconds and low energy consumption of 8.809 (mj) millijoules.

Performance Evaluation to Provide StaaS to IoT Devices in Fog Computing Environment

2018 ◽  
Author(s):  
Jose dos Santos Machado ◽  
Danilo Souza Silva ◽  
Raphael Silva Fontes ◽  
Adauto Cavalcante Menezes ◽  
Edward David Moreno ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fog Computing ◽  
Computing Environment ◽  
Iot Devices
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