Internet of Medical Things-Based Secure and Energy-Efficient Framework for Health Care

Rana, Arun Kumar; Chakraborty, Chinmay; Sharma, Satpal; Dhawan, Sanjeev; Pani, Subhendu Kumar; Ashraf, Imran

doi:10.1089/big.2021.0202

Cited by 27 publications

(16 citation statements)

References 36 publications

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“…Figure 10a-d illustrate the dependency between rising fault rate and falling the dependability of applications ( 1)-( 4) to verify the presented simulation results and Equation (13) efficiency in estimating the addressed parameter. Availability is defined as the application and service priority for the defined applications (other than application-2) because of the highlighted role of timely providing services to the requesters.…”

Section: Simulation Results and Observationsmentioning

confidence: 68%

“…Application dependability parameter depends on availability, security, and confidence with the different impact factors (α, β, and µ), which directly relate to service priority, as shown in Equation (13). To determine the mentioned impact factors, we define the weights that are demonstrated the role of service availability, security, and confidence based on application priorities.…”

Section: Servicesecurity Pi = ∑ T=t Max T=0mentioning

confidence: 99%

“…The protocols created an opportunity to protect the shared private records and recommendations for improving the security of IoMT applications. Researchers applied the cryptography techniques of symmetric and asymmetric algorithms to define different authentication keys, encryption, and decryption information to support the security of transferring data between the IoMT nodes and layers [9][10][11][12][13]. By providing trust-based services, the studies tried to solve the security and privacy problems via defining a hard limitation to provide or dedicate a server to a requester when correctness has a highlighter role than availability [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability

Rahmani

Mirmahaleh

2022

Sustainability

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The Internet of Medical Things (IoMT) has overcome the privacy challenges of E-healthcare-based Internet of Things (IoT) systems to protect the joined people’s private records to IoMT infrastructures and support their information security in different layers. By deploying various medical applications, security and privacy are challenging for the IoMT via rising communications between its layers and nodes. Some case studies aimed to solve the issues and provided various methods and protocols to identify the malicious data and information, which had almost overlooked application and service priority to targeting the research and satisfying security. We addressed the dependability and privacy problems of IoMT-based applications by presenting an intelligent algorithm for node mapping and flexible clustering (NFC) via defining a graph and employing a neural network (NN). This work proposes a flexible clustering method to categorize the healthcare service providers for timely detecting faults and identifying the proper servers to join the cluster by considering service and application priority. We improve the application dependability and privacy by about 77.3–83.2% via pruning the defective nodes and employing the neighbor components to support faulty devices’ role. By removing the failed or faulty nodes, the study reduces communication delay and energy consumption, approximately 19.3–21.7% and 10.3–11.8%, respectively.

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Section: Simulation Results and Observationsmentioning

confidence: 68%

Section: Servicesecurity Pi = ∑ T=t Max T=0mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability

Rahmani

Mirmahaleh

2022

Sustainability

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“…As classifiers are generally more suitable in the process of learning parameters using different algorithms, hence, improving the classification recognition should not be limited to gene expression. Our future plans include implementing the model with improved deep LSTM networks [ 51 ] and effective feature selection [ 52 ] for more accurate classification and using platforms such as Internet of Things (IoT) [ 53 , 54 ] for fast data processing.…”

Section: Discussionmentioning

confidence: 99%

Deep learning‐based microarray cancer classification and ensemble gene selection approach

Rezaee

Jeon

Khosravi

et al. 2022

IET Systems Biology

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Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k-nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis-related brain tissue lesions were examined to show the generalisability of the model method.

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“…Since the 21st century, the Internet of Things (IoT) technology has developed rapidly and has been used extensively in the medical field. Many advanced medical technologies rely on the Internet of Things technology, such as medical details, medical process digitisation, material management simulation, and disease diagnosis [ 1 , 2 , 3 ]. However, the condition of medical care has been increasingly unbalanced.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Mixed Attention Network for CT and MRI Image Fusion

Liu

Yan

Zhang

et al. 2022

Entropy

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Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this problem. Due to its outstanding feature extraction and representation capabilities, convolutional neural networks (CNNs) have been widely used in medical image fusion. However, most existing CNN-based medical image fusion methods calculate their weight maps by a simple weighted average strategy, which weakens the quality of fused images due to the effect of inessential information. In this paper, we propose a CNN-based CT and MRI image fusion method (MMAN), which adopts a visual saliency-based strategy to preserve more useful information. Firstly, a multi-scale mixed attention block is designed to extract features. This block can gather more helpful information and refine the extracted features both in the channel and spatial levels. Then, a visual saliency-based fusion strategy is used to fuse the feature maps. Finally, the fused image can be obtained via reconstruction blocks. The experimental results of our method preserve more textual details, clearer edge information and higher contrast when compared to other state-of-the-art methods.

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Internet of Medical Things-Based Secure and Energy-Efficient Framework for Health Care

Cited by 27 publications

References 36 publications

Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability

Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability

Deep learning‐based microarray cancer classification and ensemble gene selection approach

Multi-Scale Mixed Attention Network for CT and MRI Image Fusion

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