Pervasive Healthcare Internet of Things: A Survey

Phung, Kim Anh; Kirbas, Cemil; Dereci, Leyla; Nguyên, Tam

doi:10.3390/info13080360

Cited by 6 publications

(3 citation statements)

References 98 publications

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“…A. I. Khan and A. Al-Badi, [41] Today's desktop-based technologies are going from niche to ubiquitous. There are billions of widely distributed software, however, application is difficult to validate because of diversity of pervasive hardware Minimum effort cloud computing is the future.…”

Section: Software As a Service (Saas)mentioning

confidence: 99%

Performance Analysis of Enterprise Cloud Computing: A Review

Malallah¹,

Qashi²,

Abdulrahman³

et al. 2023

JASTT

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Cloud computing has swiftly established itself as the norm in its field as a result of the advantages described above. In an attempt to reduce the amount of time spent on infrastructure upkeep, an increasing number of businesses are moving their operations to the cloud. As a result, maintaining a cloud environment has proved to be exceedingly difficult. It is necessary to have an efficient cloud monitoring system in order to reduce the workload associated with administration and improve cloud operation. The cloud monitoring service is beneficial since it has the potential to improve performance and make administration easier. The administration of Quality of Service (QoS) parameters for cloud-hosted, virtualized, and physical services and applications is one of the most important responsibilities of cloud monitoring. As a result, cloud management software retains a record of both actions and services, and it also conducts dynamic setups of the cloud in order to increase operational effectiveness. The performance of businesses and businesses as a whole was examined in this article, as was the influence that cloud-ready programs and tools have on that performance, as well as the advantages that may be obtained from adopting such programs and products.

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Section: Software As a Service (Saas)mentioning

confidence: 99%

Performance Analysis of Enterprise Cloud Computing: A Review

Malallah¹,

Qashi²,

Abdulrahman³

et al. 2023

JASTT

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“…Because of their widespread applications, location-based computing and services have received more attention as the Internet of Things (IoT) has improved. As a result, Information about the target location is critical in localization systems [1,2]. Localization methods are used in developing existing systems using various technologies and methods based on the application.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Indoor Localization Based on Deep Attention Learning Model

Abozeid¹,

Taloba²,

El-Aziz³

et al. 2023

Computer Systems Science and Engineering

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Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization's accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale Pyramid Module (SPM). It not only enhances accuracy but also ensures the model's real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25°for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03°for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods.

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“…The ransomware attacks are rapidly growing and are threats to individuals, businesses, and government and private sectors, where the attackers infect their deployed infrastructure and demand large amounts of ransom [ 8 , 9 , 10 ]. Due to an excess of such attacks, newer versions of the ransomware attacks appear frequently and have been responsible for millions of dollars of losses annually [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Tariq

Ullah

Uddin

et al. 2022

Sensors

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Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients’ critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.

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Pervasive Healthcare Internet of Things: A Survey

Cited by 6 publications

References 98 publications

Performance Analysis of Enterprise Cloud Computing: A Review

Performance Analysis of Enterprise Cloud Computing: A Review

An Efficient Indoor Localization Based on Deep Attention Learning Model

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

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