The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Smart health care is an important aspect of connected living. Health care is one of the basic pillars of human need, and smart health care is projected to produce several billion dollars in revenue in the near future. There are several components of smart health care, including the Internet of Things (IoT), the Internet of Medical Things (IoMT), medical sensors, artificial intelligence (AI), edge computing, cloud computing, and next-generation wireless communication technology. Many papers in the literature deal with smart health care or health care in general. Here, we present a comprehensive survey of IoT- and IoMT-based edge-intelligent smart health care, mainly focusing on journal articles published between 2014 and 2020. We survey this literature by answering several research areas on IoT and IoMT, AI, edge and cloud computing, security, and medical signals fusion. We also address current research challenges and offer some future research directions.

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A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

Amalgamation of blockchain and IoT for smart cities underlying 6G communication: A comprehensive review

English teaching practice based on artificial intelligence technology

The Role of the Internet of Things in Healthcare: Future Trends and Challenges.

Day by day cities become more intelligence because governments move slowly to convert each thing to become smarter. These cities are built with the goal of increasing liveability, safety, revivification, and sustainability by building smart services like smart education, smart government, smart mobility, smart homes and e-health but it is important to build these services along with the method for securing and maintaining the privacy of citizen’s data. Citizens can build their own services that meet their requirements and needs. This book chapter discusses the internet of things and its applications in smart cities then discusses smart cities and challenge that faces smart cities and describes how to protect citizen data by securing the WiFi based data transmission system that encrypts and encodes data before transfer from source to destination where the data is finally decrypted and decoded. The proposed system is embedded with authentication method to help the authorized people to access the data. The proposed system first compresses data with run-length encoding technique then encrypt it using the AES method but with a rotated key then the source transfers the encoded and encrypted data to the destination where the data is decrypted then decoded to restore the original data then the original data is upload to the destination’s website.

Data Security and Challenges in Smart Cities

A secure real-time internet of medical smart things (IOMST)

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest.

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The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients

Nowadays, the Internet of Medical Things (IoMT) technology is growing and leading the revolution in the global healthcare field. Exchanged information through IoMT permits attackers to hack or modify the patient’s data. Hence, it is of critical importance to ensure the security and privacy of this information. The standard privacy techniques are not secured enough, so this paper introduces blockchain technology that is used for securing data. Blockchain is used with the smart contract to secure private patient records. This paper presents how a patient may send his vital signs to the physician through the Internet without meeting with the latter in person. These vital signs are collected from the IoMT system that we developed before. In the proposed method, each medical record is stored in the block and connected to the previous block by a hashing function. In order to secure the new block, the SHA256 algorithm is used. We modified the SHA256 algorithm by using run-length code in compressing data. If any hacker attempts to attack any medical record, he must change all previous blocks. In order to preserve the rights of the doctor and patient, a smart contract is built into the blockchain system. When the transaction begins, the smart contract withdraws the money from the patient’s wallet and stores it in the smart contract. When the physician sends the treatment to the patient, the smart contract transfers the money to the physician. This paper shows that all recent work implements Blockchain 2 into the security system. This paper also shows that our security system can create a new block with O (n + d) time complexity. As a result, our system can create one hundred blocks in two minutes. Additionally, our system can deposit the money from the patient’s wallet into the physician’s wallet promptly. This paper also shows that our method performs better than all subsequent versions of the original blockchain.

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Improving Healthcare Applications Security Using Blockchain

The Internet of Medical Things (IoMT) uses wireless networks to help patients to communicate with healthcare professionals. Therefore, IoMT devices suffer from a lack of security controls, just like many Internet of Things (IoT) gadgets. Thus, in this paper, we develop a system that uses a blockchain to secure medical data for each transaction between physicians and patients. This system also helps the physician to send the treatment to the blockchain. The blockchain creates a new block for the treatment and connects it with the previous block. This system also helps patients to access their treatment through the blockchain. SHA-256 is used to hash the new block using some information about the last block. We modify SHA-256 using the LZ4 algorithm to compress data. We also prevent a new block hash code starting with a specific number of zeros, which made the proposed system give a time complexity better than all related work. In this paper, we also develop a party-authentication technique that ensures the two parties of the transaction. The proposed system makes a transaction with O(n) time complexity. Thus, our system takes 1 s to create a block for the transaction. We also make a green computing algorithm comparison between our proposed system and the blockchain version. This comparison proves that our proposed method consumes less energy to create a new block. This paper proves that our method performs better than all previous blockchain versions.

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I. S. Farahat

Papers

Data Security and Challenges in Smart Cities

A secure real-time internet of medical smart things (IOMST)

The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients

Improving Healthcare Applications Security Using Blockchain

Secure Medical Blockchain Model