Random Forest for Data Aggregation to Monitor and Predict COVID-19 Using Edge Networks

Adhikari, Mainak; Ambigavathi, M.; Menon, Varun G.; Hammoudeh, Mohammad

doi:10.1109/iotm.0001.2100052

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…The need for ultralow service requires to introduce tactile 5G [ 152 ]. For example, authors in [ 153 ] proposed a solution for ultralow latency based on machine learning and network slicing.…”

Section: Open Issues and Future Directionsmentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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Section: Open Issues and Future Directionsmentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The clinical reports are given 30% weightage, while the CT-scan report prediction results are given 70% weightage. In [ 112 ], the authors propose an online data monitoring framework that predicts the risk level of Covid patients. The framework aggregates data using edge computing, where local gateways and edge nodes are used for aggregating the monitored data.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

Krishna

Kumar

Kushwaha

2023

Wireless Pers Commun

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Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients’ health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients’ vitals and storing them securely have risen more than ever. Analyzing the stored patients’ data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients’ data, and processing/analyzing the stored patients’ data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

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“…To reduce hospital admission pressure related to COVID-19, AI-assisted edge computing systems use edge-centric ehealthcare models for monitoring patient symptom to predict the risk levels according to the monitored symptoms (Adhikari et al, 2021). Also, a variety of COVID-19 prediction models have been proposed, ranging from decision trees, Naive Bayes classifier, adaptive network-based fuzzy inference system, Multi-Layer perceptrons, and Support Vector Machines (Ardabili et al, 2020).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Also, a variety of COVID-19 prediction models have been proposed, ranging from decision trees, Naive Bayes classifier, adaptive network-based fuzzy inference system, Multi-Layer perceptrons, and Support Vector Machines (Ardabili et al, 2020). These models have been designed to run on edge devices (Adhikari et al, 2021) as well as on the cloud (Awal et al, 2021;Elbasi et al, 2021;Jing et al, 2021), with some models on the cloud utilizing stored data such as temperature data, audio data, and heart rate data (Kanmani et al, 2021) to make COVID-19 diagnoses. Deep learning methods also have been utilized to predict COVID-19 infection using the data gathered from wireless devices.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Shen

Ghatti

Levkov

et al. 2022

Front. Artif. Intell.

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Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

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Random Forest for Data Aggregation to Monitor and Predict COVID-19 Using Edge Networks

Cited by 10 publications

References 15 publications

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

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