scholarly journals End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1439 ◽  
Author(s):  
Nora El-Rashidy ◽  
Shaker El-Sappagh ◽  
S. M. Riazul Islam ◽  
Hazem M. El-Bakry ◽  
Samir Abdelrazek
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Fog Computing ◽  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Healthcare Systems ◽  
Mobile App ◽  
Cloud Layer ◽  
Area Network ◽  
End To End

Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.

scholarly journals A lightweight verifiable outsourced decryption of attribute-based encryption scheme for blockchain-enabled wireless body area network in fog computing

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090679
Author(s):  
Rui Guo ◽  
Chaoyuan Zhuang ◽  
Huixian Shi ◽  
Yinghui Zhang ◽  
Dong Zheng
Keyword(s):  
Fog Computing ◽  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Physiological Data ◽  
Body Area Network ◽  
Area Network ◽  
Encryption Scheme ◽  
Body Area ◽  
Attribute Based Encryption ◽  
Outsourced Decryption

Wireless body area network includes some tiny wearable sensors for monitoring the physiological data of user, which has been a promising method of promoting the quality and efficiency greatly in healthcare. The collected physical signs are aggregated into the medical documents and uploaded to cloud server for utilizing by the remote user. As these files are highly sensitive privacy data, there is a vital challenge that constructs a secure and efficient communication architecture in this application scenario. Based on this standpoint, we present a lightweight verifiability ciphertext-policy attribute-based encryption protocol with outsourced decryption in this article. Specifically, our construction enjoys the following six features: (1) Although the outsourced decryption enables to save the computation overhead of the data user sharply in an attribute-based encryption scheme, the ciphertext is out of control and the correctness cannot be guaranteed by the data owner. The proposal provides the verifiability of ciphertext that ensures the user to check the correctness efficiently. (2) The size of the ciphertext is constant that is not increased with the complexity of attribute and access structure. (3) For Internet of Things devices, it introduces the fog computing into our protocol for the purpose of low latency and relation interactions, which has virtually saved the bandwidth. (4) With the help of blockchain technique, we encapsulate the hash value of public parameter, original and transformed ciphertext and transformed key into a block, so that the tamper-resistance is facilitated against an adversary from inside and outside the system. (5) In the standard model, we prove that it is selectively chosen-plaintext attack-secure and verifiable provided that the computational bilinear Diffie–Hellman assumption holds. (6) It implements this protocol and shows the result of performance measurement, which indicates a significant reduction on communication and computation costs burden on every entity in wireless body area network.

Revelation of Body Behavior Based on Arm Motion Measurement in Wireless Body Area Network System

2016 ◽  
Vol 833 ◽  
pp. 179-184
Author(s):  
Nur Alia Athirah Mohtadzar ◽  
Shigeru Takayama
Keyword(s):  
Low Cost ◽  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Healthcare Systems ◽  
Body Area Network ◽  
Network System ◽  
Area Network ◽  
Body Area ◽  
Arm Motion ◽  
Fitness Training

Wireless Body Area Network or known as BAN, is a system consists of various kinds of wearable sensors to measure condition of human body. Wrist, waist and shoulder modules from BAN system can help to monitor, analyze and provide advice to the user in order to perform a moderate exercise. The availability of small, low-cost networked sensors combined with advanced signal processing and information extraction is driving a revolution in physiological monitoring and intervention. BAN system is enabling technologies for accurate measurements in healthcare systems, enhance sports and fitness training, life-style monitoring and individualized security.

scholarly journals Mitigation of packet loss with end-to-end delay in wireless body area network applications

2022 ◽  
Vol 12 (1) ◽  
pp. 460
Author(s):  
Suha Sahib Oleiwi ◽  
Ghassan N. Mohammed ◽  
Israa Al_Barazanchi
Keyword(s):  
Network Lifetime ◽  
Routing Protocol ◽  
Packet Loss ◽  
Wireless Body Area Network ◽  
Body Area Network ◽  
Area Network ◽  
Body Area ◽  
Cooperative Routing ◽  
End To End Delay ◽  
End To End

The wireless body area network (WBAN) has been proposed to offer a solution to the problem of population ageing, shortage in medical facilities and different chronic diseases. The development of this technology has been further fueled by the demand for real-time application for monitoring these cases in networks. The integrity of communication is constrained by the loss of packets during communication affecting the reliability of WBAN. Mitigating the loss of packets and ensuring the performance of the network is a challenging task that has sparked numerous studies over the years. The WBAN technology as a problem of reducing network lifetime; thus, in this paper, we utilize cooperative routing protocol (CRP) to improve package delivery via end-to-end latency and increase the length of the network lifetime. The end-to-end latency was used as a metric to determine the significance of CRP in WBAN routing protocols. The CRP increased the rate of transmission of packets to the sink and mitigate packet loss. The proposed solution has shown that the end-to-end delay in the WBAN is considerably reduced by applying the cooperative routing protocol. The CRP technique attained a delivery ratio of 0.8176 compared to 0.8118 when transmitting packets in WBAN.

scholarly journals Avoiding normalization uncertainties in deep learning architectures for end-to-end communication

2021 ◽  
Author(s):  
Simon Bos ◽  
Evgenii Vinogradov ◽  
Sofie Pollin
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Communication Systems ◽  
Digital Communication ◽  
Average Power ◽  
Communication Scheme ◽  
Batch Sizes ◽  
End To End ◽  
Improved Performance ◽  
Digital Communication Systems

Recently, deep learning is considered to optimize the end-to-end performance of digital communication systems. The promise of learning a digital communication scheme from data is attractive, since this makes the scheme adaptable and precisely tunable to many scenarios and channel models. In this paper, we analyse a widely used neural network architecture and show that the training of the end-to-end architecture suffers from normalization errors introduced by an average power constraint. To solve this issue, we propose a modified architecture: shifting the batch slicing after the normalization layer. This approach meets the normalization constraints better, especially in the case of small batch sizes. Finally, we experimentally demonstrate that our modified architecture leads to significantly improved performance of trained models, even for large batch sizes where normalization constraints are more easily met.<br>

A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN

2021 ◽  
Vol 10 (2) ◽  
pp. 44-65
Author(s):  
Koushik Karmakar ◽  
Sohail Saif ◽  
Suparna Biswas ◽  
Sarmistha Neogy
Keyword(s):  
Condition Monitoring ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Health Condition ◽  
Sensor Data ◽  
Area Network ◽  
Cardiac Condition ◽  
Artificial Neural Network Ann ◽  
Sensor Detection

Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

scholarly journals An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge

Computers ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Imran Zualkernan ◽  
Salam Dhou ◽  
Jacky Judas ◽  
Ali Reza Sajun ◽  
Brylle Ryan Gomez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Camera Trap ◽  
Mobile App ◽  
Camera Traps ◽  
Raspberry Pi ◽  
Adjusted Rand Index ◽  
Area Network ◽  
Wide Area Network ◽  
Neural Network Models ◽  
Average Current

Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. However, current camera traps suffer from two problems. First, the images are manually classified and counted, which is expensive. Second, due to manual coding, the results are often stale by the time they get to the ecologists. Using the Internet of Things (IoT) combined with deep learning represents a good solution for both these problems, as the images can be classified automatically, and the results immediately made available to ecologists. This paper proposes an IoT architecture that uses deep learning on edge devices to convey animal classification results to a mobile app using the LoRaWAN low-power, wide-area network. The primary goal of the proposed approach is to reduce the cost of the wildlife monitoring process for ecologists, and to provide real-time animal sightings data from the camera traps in the field. Camera trap image data consisting of 66,400 images were used to train the InceptionV3, MobileNetV2, ResNet18, EfficientNetB1, DenseNet121, and Xception neural network models. While performance of the trained models was statistically different (Kruskal–Wallis: Accuracy H(5) = 22.34, p < 0.05; F1-score H(5) = 13.82, p = 0.0168), there was only a 3% difference in the F1-score between the worst (MobileNet V2) and the best model (Xception). Moreover, the models made similar errors (Adjusted Rand Index (ARI) > 0.88 and Adjusted Mutual Information (AMU) > 0.82). Subsequently, the best model, Xception (Accuracy = 96.1%; F1-score = 0.87; F1-Score = 0.97 with oversampling), was optimized and deployed on the Raspberry Pi, Google Coral, and Nvidia Jetson edge devices using both TenorFlow Lite and TensorRT frameworks. Optimizing the models to run on edge devices reduced the average macro F1-Score to 0.7, and adversely affected the minority classes, reducing their F1-score to as low as 0.18. Upon stress testing, by processing 1000 images consecutively, Jetson Nano, running a TensorRT model, outperformed others with a latency of 0.276 s/image (s.d. = 0.002) while consuming an average current of 1665.21 mA. Raspberry Pi consumed the least average current (838.99 mA) with a ten times worse latency of 2.83 s/image (s.d. = 0.036). Nano was the only reasonable option as an edge device because it could capture most animals whose maximum speeds were below 80 km/h, including goats, lions, ostriches, etc. While the proposed architecture is viable, unbalanced data remain a challenge and the results can potentially be improved by using object detection to reduce imbalances and by exploring semi-supervised learning.

scholarly journals A Review on Security and Privacy Issues in Wireless Body Area Networks for Healthcare Applications

2019 ◽  
Vol 5 (11) ◽  
pp. 22-28
Author(s):  
Vineeta Shrivastava ◽  
Mayank Namdev
Keyword(s):  
Wearable Sensors ◽  
Wireless Body Area Network ◽  
Security And Privacy ◽  
Area Network ◽  
Security Measures ◽  
Body Area ◽  
Healthcare Applications ◽  
Communication Architecture ◽  
High Level ◽  
Privacy Issues

Wireless Body Area Network (WBAN) is a new trend in the technology that provides remote mechanism to monitor and collect patient’s health record data using wearable sensors. It is widely recognized that a high level of system security and privacy play a key role in protecting these data when being used by the healthcare professionals and during storage to ensure that patient’s records are kept safe from intruder’s danger. It is therefore of great interest to discuss security and privacy issues in WBANs. In this paper, we reviewed WBAN communication architecture, security and privacy requirements and security threats and the primary challenges in WBANs to these systems based on the latest standards and publications. This paper also covers the state-of-art security measures and research in WBAN.

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