Edge and Fog Computing in Critical Infrastructures: Analysis, Security Threats, and Research Challenges

Tedeschi, Pietro; Sciancalepore, Savio

doi:10.1109/eurospw.2019.00007

Cited by 26 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predictive approach here functions as a mapping between the object's characteristics and values [28]. A two phase support vector network was used which combined the twophase clustering approach with a probability based SVM that evaluated the Wisconsin Breast Cancer Diagnosis (WBCD) dataset [29]. It achieved a classification model accuracy of 99.10 percent.…”

Section: Breast Cancermentioning

confidence: 99%

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Siddegowda

Devi

2022

IJMTS

View full text Add to dashboard Cite

Background/ Purpose: Reviewing of the various work and literature in the proposed areas will help in developing a strong foundation of the domain on which the research is planned. The reason forth for the literature review is to become familiar in the health care domain. Since the area selected is the health care domain, the recent literature review is carried out as it is very important. Objective: A strong background on health care domain is developed and a new problem which is not addressed is discussed. The gaps in the research area are identified. A new solution for solving the problem is designed and developed. Design/Methodology: This work has adapted secondary source of data which is mainly journals, articles and review comments. The relevant literature is selected and a detailed study is conducted. This has helped in drafting the problem statement. Findings/Results: The finding and drawbacks of all the recent work are well studied. The reason for the gap is also well studied and the results of each work are also well analyzed. Research Limitations: A detailed study done on the chronic diseases and its impact has helped to open up the importance of studying about comorbid diseases. The limitations of various machine learning algorithms are also studied. Originality/Value: This paper aims at studying the relevant existing literature that includes research journals, conference papers, technical book chapter and few web sources. All the papers selected were relevant to the proposed work and all papers are recent and from well reputed publisher. The papers are cited by many authors. Paper Type: Literature review paper is carried out on scientific papers, especially from well indexed services.

show abstract

Section: Breast Cancermentioning

confidence: 99%

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Siddegowda

Devi

2022

IJMTS

View full text Add to dashboard Cite

show abstract

“…The accumulated time is captured by the cloud servers and it contributes towards the increase of a system’s latency. Furthermore, this motivates the appearance of drastic effects on power and energy consumption [ 10 ]. As a result of the caused high latency, there can be indications of degradation in the Quality-of-Service (QoS) and Quality-of-Experience (QoE).…”

Section: Background and Related Workmentioning

confidence: 99%

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Mijuskovic

Chiumento

Bemthuis

et al. 2021

Sensors

View full text Add to dashboard Cite

Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.

show abstract

“…Particularly, the significance of BLE-based sensors and machine learning algorithms is highlighted for self-monitoring of diabetes mellitus in healthcare. Machine learning plays an essential part in the healthcare industry by providing ease to healthcare professionals to analyze and diagnose medical data [8][9][10][11][12]. Moreover, intelligent healthcare systems are providing real-time clinical care to needy patients [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Edge computing utilizes sensors and mobile devices to process, compute, and store data locally rather than cloud computing. Besides, Fog computing places resources near data sources such as gateways to improve latency problems [9]. Apache Kafka will be used in real time as a delivery agent for messages in a platform that allows fault-tolerant, tall throughput, and low-latency publication.…”

Section: Tools and Technology For Implementation Of Hypothetical System For Healthcarementioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

104

View full text Add to dashboard Cite

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

show abstract

Edge and Fog Computing in Critical Infrastructures: Analysis, Security Threats, and Research Challenges

Cited by 26 publications

References 15 publications

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Contact Info

Product

Resources

About