Towards Workload-Balanced, Live Deep Learning Analytics for Confidentiality-Aware IoT Medical Platforms

Granados, J. M. del Castillo; Zou, Zhuo; Zheng, Lirong

doi:10.1109/aicas.2019.8771558

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…(𝑇𝑃+𝑇𝑁) 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 (5) In this section, our focus is on the results of the confusion matrix (Cm) as presented in (6). Additionally, we illustrate the accuracy (Acc) of LDA-DT for the addition of a new controller and KNN for assigning a new VM to a specific zone.…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

“…Consequently, when exchanging data between communicating devices in a medical environment, security measures must be strengthened. In this context, set up a real-time medical internet of thing (IoT) platform that processes information collected by the deep learning method on biological signals [6]. The result of their work is that the detection accuracy of the deep neural network is compared to that of a human expert on electrocardiogram (ECG) signals, which predicts possible future health damages.…”

Section: Introductionmentioning

confidence: 99%

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Automating cloud virtual machines allocation via machine learning

Kamoun-Abid,

Frikha,

Meddeb-Makhoulf

et al. 2024

IJEECS

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In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.

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Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automating cloud virtual machines allocation via machine learning

Kamoun-Abid,

Frikha,

Meddeb-Makhoulf

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…Therefore, security must be reinforced when exchanging data between communicating equipment within the medical environment. In this context, Granados et al [6] [9]. The previously presented works in [7,9] deploy the cloud in the medical field, without considering the security and access control of the stored medical data.…”

Section: Related Workmentioning

confidence: 99%

“…(𝑇𝑃 + 𝑇𝑁) 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁 (5) In this section, we focus on the results of the confusion matrix (Cm) presented by (6). In addition, we have illustrated accuracy (Acc) of DT (Decision Tree) for the addition of a new controller and of KNN (KNeighborsClassifier) for the membership of a new VM (i.e.…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

Allocation of virtual machine in a cloud environment based on machine learning

kamoun-abid¹,

Frikha²,

Meddeb-Makhoulf³

et al. 2023

Preprint

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Nowadays, the technics of health applications that use the cloud are being developed. However, the existing methods are static and cannot approve dynamic changes in the dynamic environment (for example, when the network and virtual machines (VMs) have a change in resources values) during the execution process. Since the cloud environment provides virtualized resources for computing and for storage (for example: health information) with used many virtual machines. Also, data applications require communication between these virtual nodes, placement of VMs and data location to achieve overall computation time. The majority of scientific researchers present in the current literature that the selection of physical nodes to place data and virtual machines as not separate problems. In addition, in the cloud environment, the major challenge is network security. So, there is no better solution than firewalls which are used to filter packets (detect spam packets). But the problem is that the cloud has a dynamic topology, for that these firewalls cannot examine the content from inside the packet and the network becomes vulnerable. This traditional firewall they only provide basic protection at the network layers and cannot work in complex topologies like Cloud Computing. For this, the risk that we will have unprotected areas. Regarding this challenge, in this article, we proposed to divide the cloud topology into zones, so that each zone is supervised by a controller. Thus, each virtual machine is supervised by a firewall. For remote network saturation with exchanged data between controllers and VMs, the number of controllers must be minimized and the addition of a new VM must be well placed in our new architecture (Divided-Cloud). For this, in this work, we used a learning method of Machine Learning (ML) "Decision Tree" at the level of the addition of a new controller. According to the affected result, the algorithm reaches its maximum accuracy which is equal to 83%. Furthermore, about the location of a new VM, we used a “KNeighborsClassifier” calcification method and it gives an accuracy is equal to 83%.

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“…At the application-layer, they adapt based on the protocol used at the edge to the one used by the cloud. For example, from constrained IoT devices employing CoAP to a cloud service that communicates through MQTT or AMQP [61,108,46]. They may also convert the lower-layer wireless protocols of the local, constrained devices for enabling Internet based transmission to the cloud [30].…”

Section: The Gatewaymentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

184

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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Towards Workload-Balanced, Live Deep Learning Analytics for Confidentiality-Aware IoT Medical Platforms

Cited by 6 publications

References 7 publications

Automating cloud virtual machines allocation via machine learning

Automating cloud virtual machines allocation via machine learning

Allocation of virtual machine in a cloud environment based on machine learning

Machine learning and data analytics for the IoT

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