Internet Of Medical Things Environment Research Articles

Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment.

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.

Achieving Privacy-Preserving Online Diagnosis With Outsourced SVM in Internet of Medical Things Environment

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

LMNet: Lightweight multi-scale convolutional neural network architecture for COVID-19 detection in IoMT environment

The COVID-19 disease, initially known as SARS-CoV-2, was first reported in early December 2019 and has caused immense damage to humans globally. The most widely used clinical screening method for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). RT-PCR uses respiratory samples for testing, because of which, this manual technique becomes complicated, laborious and time-consuming. Even though it has a low sensitivity, it carries a considerable risk for the testing medical staff. Hence, there is a need for an automated diagnosis system that can provide quick and efficient diagnosis results. This research proposed a multi-scale lightweight CNN (LMNet) architecture for COVID-19 detection. The proposed model is computationally less expensive than previously available models and requires less memory space. The performance of the proposed LMNet model ensemble with DenseNet169 and MobileNetV2 is higher than the other state-of-the-art models. The ensemble model can be integrated at the backend of the smart devices; hence it is useful for the Internet of Medical Things (IoMT) environment.

Anonymous Dynamic Group Authenticated Key Agreements Using Physical Unclonable Functions for Internet of Medical Things

Group authenticated key agreements (GAKAs) for an Internet of Medical Things (IoMT) enable medical sensor devices to authenticate each other and agree upon a common session key. These medical sensor devices can then establish a secure channel using this session key and exchange information securely. Owing to hardware limitations, sensors do not have sufficient computing power, energy, and storage resources to implement burdensome protocols. Moreover, sensors are easily damaged, causing the number of members of the group to change. Therefore, a group key agreement (GKA) protocol is required to consider dynamic groups and reduce the computational cost of an IoMT. The physical unclonable function (PUF) uses the uniqueness and randomness of its circuit to perform calculations. It has message fingerprints so that the user is not necessary to store a private key. The PUF is less computationally burdensome than the more common modular exponentiation and can be applied in an IoMT environment with limited resources. This article develops lightweight anonymous dynamic GAKA (DGAKA) that uses the PUF to solve the problems of data storage, transmission security, and computing efficiency that are faced by the IoMT. The proposed protocol has higher security and efficiency in computation and storage than those previously developed.

K-nearest neighbour-based smart contract for internet of medical things security using blockchain

Due to the fast-pacing development of technology in the healthcare domain, many problems arise surrounding the security and privacy preservation of medical data. Secure authentication on the Internet of Medical Things (IoMT) is essential. The lack of security in critical and sensitive information of IoMT may lead to high-risk issues in patient privacy. When new data is transmitted from the sensor node, it cannot be assured as authenticated data. Therefore, a blockchain-based system is needed. Such a system allows healthcare providers to access the health records of patients in a more secured authentication-based approach across various network connections. In this paper, a new secure authentication approach using machine learning is proposed. To identify the dynamic time attack detection and authentication in an IoMT environment, this work implements K-Nearest neighbour (KNN) and machine learning using smart contract (KNN-MLSC). It improves security, reduces latency, and maintains health data privacy for both physicians and patients. The accuracy of KNN-MLSC got 0.96 compared with KNN using a smart contract. Also, the results showed that KNN-MLSC has the minimum computation time.

Security in IoMT‐driven smart healthcare: A comprehensive review and open challenges

AbstractThe Internet of Medical Things (IoMT) is a kind of communication environment, which deals with communication that occurs through the Internet of Things (IoT)‐enabled smart medical (healthcare) devices. The potential security threats of IoMT affect the confidentiality, integrity, authenticity and availability of its data and the associated resources. In this review article, we explain various architectures of IoMT communication along with their applications. We highlight the security requirements of IoMT communication environment along with some potential security attacks and threats of IoMT. Furthermore, the network model and adversary model of IoMT communication are provided. A taxonomy of security protocols of IoMT communication environment is additionally added, which contains various security protocols, such as key management, user/device authentication, access control and intrusion detection protocols. Moreover, we provide a detailed comprehensive comparative analysis of several security protocols with respect to various parameters, like computation cost, communication cost, security features and functionality features, accuracy and F1‐score. Additionally, we also provide some future research directions.

On the design of an AI-driven secure communication scheme for internet of medical things environment

The Internet of Medical Things (IoMT) is a collection of smart healthcare devices, hardware infrastructure, and related software applications, that facilitate the connection of healthcare information technology system via the Internet. It is also called IoT in healthcare, facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data. In other words, IoMT is an amalgam of medical devices and applications, which improves overall healthcare outcomes. However, this system is prone to security- and privacy-related attacks on healthcare data. Therefore, providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential. To mitigate this, we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT. The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT. Furthermore, we provide the security analysis of the proposed scheme to show its security against different possible attacks. Moreover, a comparative study of the proposed scheme with other similar schemes is presented. Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs, and security and functionality attributes. Finally, we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Fusion-based learning for stress recognition in smart home: An IoMT framework

Today, in order to prevent chronic stress from causing irreparable damage, it is imperative to diagnose and treat it in its early stages. Using the Internet of Things (IoT) and automated learning methods in homes, creating an intelligent environment can help identify stress-related emotions. An approach to stress detection based on metaheuristic fuzzy inference system-based learning (fMFiS-L) and emotion recognition is presented in this paper. Accordingly, our study focuses on the use of fusion learning to diagnose stress using the healthcare system and the Internet of Medical Things (IoMT) for smart homes. Music videos were shown to participants in the first stage to arouse emotional states such as anger, anxiety, and depression. Volunteers were divided into two groups, with one group practicing Reiki meditation for two weeks. The EEG signals were recorded before and after meditation, stress levels were assessed using the Likert scale, and emotions were classified using the modified fusion fuzzy inference system. In addition, a method is presented for determining the optimal parameters in the fMFiS-L structure by optimizing the innovative gunner algorithm (AIG). We conclude that Reiki meditation can significantly reduce negative emotions and stress levels in the IoMT environment of smart homes. Furthermore, the fMFiS-L architecture was evaluated for generalization of emotion recognition based on unseen EEG data. Generally, the classification of emotions produced satisfactory results, with a 92% accuracy rate.

Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things

With the application of the Internet of Medical Things (IoMT) in minimally invasive surgery (MIS), surgeons now have a better chance at hard-to-treat cases by carrying out more complicated MIS workflows. However, a scheduled surgical workflow is often required to be updated based on the patient’s internal tissue states. Perioperative complications could occur if in-time adjustments are lacking in the operating rooms when needed. To help manage the uncertainty of live surgical workflows in the IoMT environment, we propose an MIS safety improvement framework. It helps surgeons in predicting surgical workflows with limited MIS video frames by embedding our proposed model GuidedNet. To predict future surgical activities, we first build three isomorphic neural networks to capture the spatio-temporal information. Then we establish a guidance fusion module to handle the contextual information. It guides the GuidedNet to recognize the surgical stage. Moreover, we build a novel joint loss function to train the GuidedNet to predict the future surgical stage. We evaluate the approach on a large dataset that contains 80 cholecystectomy videos (Cholec-80) and compare it with the state-of-art. Experiments show that the GuidedNet can assist surgeons in carrying out MIS as well as guide the next stage of surgery for improving surgical safety. Comparing to the state-of-the-art, our approach can obtain better predict accuracy (up to 79%) with less computing resource consumption. The result also shows that our approach has a high application prospect in video classification in other Internet-of-Things scenarios.

Variance Approximation and Probabilistic Decomposition Noise Removal Framework for Arrhythmia Detection and Classification on Internet of Medical Things Environment

Variance Approximation and Probabilistic Decomposition Noise Removal Framework for Arrhythmia Detection and Classification on Internet of Medical Things Environment

Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things

In recent times, Internet of Medical Things (IoMT) gained much attention in medical services and healthcare management domain. Since healthcare sector generates massive volumes of data like personal details, historical medical data, hospitalization records, and discharging records, IoMT devices too evolved with potentials to handle such high quantities of data. Privacy and security of the data, gathered by IoMT gadgets, are major issues while transmitting or saving it in cloud. The advancements made in Artificial Intelligence (AI) and encryption techniques find a way to handle massive quantities of medical data and achieve security. In this view, the current study presents a new Optimal Privacy Preserving and Deep Learning (DL)-based Disease Diagnosis (OPPDL-DD) in IoMT environment. Initially, the proposed model enables IoMT devices to collect patient data which is then preprocessed to optimize quality. In order to decrease the computational difficulty during diagnosis, Radix Tree structure is employed. In addition, ElGamal public key cryptosystem with Rat Swarm Optimizer (EIG-RSO) is applied to encrypt the data. Upon the transmission of encrypted data to cloud, respective decryption process occurs and the actual data gets reconstructed. Finally, a hybridized methodology combining Gated Recurrent Unit (GRU) with Convolution Neural Network (CNN) is exploited as a classification model to diagnose the disease. Extensive sets of simulations were conducted to highlight the performance of the proposed model on benchmark dataset. The experimental outcomes ensure that the proposed model is superior to existing methods under different measures.

Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

The Internet of Medical Things (IoMT) plays a vital role in healthcare systems to increase electronic devices’ accuracy, reliability, and productivity. This paper presents fast multiple zero-watermarking methods based on Multi-channel Fractional Legendre Fourier moments (MFrLFMs) for medical image security and copyright protection in IoMT applications without deforming the original medical images. The MFrLFMs are utilized due to their high accuracy, numerical stability, geometric invariances, and high resistance to various attacks. Based on the most significant features generated from MFrLFMs, after scrambling using a two-dimensional Discrete Henon Map, then XORed with binary scrambled watermark to construct owner share. The proposed watermarking method is implemented using a low-cost Raspberry Pi Linux microprocessor, which ensures the suitability of medical devices in the IoMT environment. We evaluated the robustness of the proposed algorithm against different geometric and common signal processing attacks using various medical images. The proposed method gives better BER, NC, and SSIM values than existing methods.

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies–machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others–to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier detection and classification of BC using digital mammograms can decrease the mortality rate. This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm (DL-MOMFO) for BC diagnosis and classification in the IoMT environment. The goal of this paper is to integrate deep learning (DL) and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC. The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter (AWMF)-based noise removal and contrast-limited adaptive histogram equalisation (CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms. In addition, a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms. Moreover, a SqueezeNet-based feature extraction and a fuzzy support vector machine (FSVM) classifier were used in the presented technique. To enhance the diagnostic performance of the presented method, the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques. The DL-MOMFO technique was tested on the MIAS database, and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bps under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM.

Malware Detection Approaches and Analysis for the Internet of Medical Things Enabled Healthcare Systems

The advancement of information and communications technology has changed an IoMT-enabled healthcare system. The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) that focuses on smart healthcare (medical) device connectivity. While the Internet of Medical Things (IoMT) communication environment facilitates and supports our daily health activities, it also has drawbacks such as password guessing, replay, impersonation, remote hijacking, privileged insider, denial of service (DoS), and man-in-the-middle attacks, as well as malware attacks. Malware botnets cause assaults on the system's data and other resources, compromising its authenticity, availability, confidentiality and, integrity. In the event of such an attack, crucial IoMT communication data may be exposed, altered, or even unavailable to authorised users. As a result, malware protection for the IoMT environment becomes critical. In this paper, we provide several forms of malware attacks and their consequences. We also go through security, privacy, and different IoMT malware detection schemes

Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors’ performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems.

A Two-Echelon Responsive Health Analytic Model for Triggering Care Plan Revision in Geriatric Care Management

Due to the increasing ageing population, how can caregivers effectively provide long-term care services to meet the older adults’ needs with finite resources is emerging. In addressing this issue, nursing homes are striving to adopt smart health with the internet of things and artificial intelligence to improve the efficiency and sustainability of healthcare. This study proposed a two-echelon responsive health analytic model (EHAM) to deliver appropriate healthcare services in nursing homes under the Internet of Medical Things environment. A novel care plan revision index is developed using a dual fuzzy logic approach for multidimensional health assessments, followed by care plan modification using case-based reasoning. The findings reveal that EHAM can generate patient-centred long-term care solutions of high quality to maximise the satisfaction of nursing home residents and their families. Ultimately, sustainable healthcare services can be within the communities.

A Study On The Construction Of Virus Infection Area Information System Using Iomt Environment

With the recent emergence of novel virus, the importance has been empathized on the epidemiologic investigation of the infection route and control of infected patients. Control system on the viral infections is required since the new virus expression cycle becomes faster which was not previously identified with the spread to other regions and countries. Viral infections cause multiple types of problems such as public health, economic and social ones. In addition, viruses can be spread fast beyond the regions and countries all over the world according to the movements of population. Although national quarantine systems have been established to solve these problems by countries, earlier actions on the novel virus were not satisfactory. IoT environment-based healthcare industry and IT convergence medical service industry have been continuously growing. Healthcare IT, which is the area to deal with information on the personal health and medical cares, devices, systems, and platforms, provides with personal healthcare services. In this article, information system on the virus infected territories is proposed using Internet of Medical Things (IoMT) environment. The proposed study is to establish the information system on the virus infected territories by IoMT-based monitoring of infection symptoms for early reactions against the viral infections. The system can be reacted proactively by detecting the risk factors of community spread from early detections of infected patients under the fast spread situation of viral diseases. The information system on the virus infected territories using IoMT environment can react to the spread of infectious diseases actively upon providing the information of infectious disease symptoms using user information. The proposed system can establish the integrative control system of viral diseases by the prediction module of community outbreak and spread of viral diseases.

Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things Environment

In recent days, the Internet of Medical Things (IoMT) is commonly employed in different aspects of healthcare applications. Owing to the increasing necessitates of IoT, a huge amount of sensing data is collected from distinct IoT gadgets. To investigate the generated data, artificial intelligence (AI) models plays an important role to achieve scalability and accurate examination in real-time environment. However, the characteristics of IoMT result in certain design challenges, namely, security and privacy, resource limitation, and inadequate training data. At the same time, blockchain, an upcoming technology, has offered a decentralized architecture, which gives secured data transmission and resources to distinct nodes of the IoT environment and is stimulated for eliminating centralized management and eliminates the challenges involved in it. This paper designs deep learning (DL) with blockchain-assisted secure image transmission and diagnosis model for the IoMT environment. The presented model comprises a few processes namely data collection, secure transaction, hash value encryption, and data classification. Primarily, elliptic curve cryptography (ECC) is applied, and the optimal key generation of ECC takes place using hybridization of grasshopper with fruit fly optimization (GO-FFO) algorithm. Then, the neighborhood indexing sequence (NIS) with burrow wheeler transform (BWT), called NIS-BWT, is employed to encrypt the hash values. At last, a deep belief network (DBN) is utilized for the classification process to diagnose the existence of disease. An extensive experimental validation takes place to determine the analysis of the optimal results of the presented model, and the results are investigated under diverse aspects.

Secure Data Sharing With Flexible User Access Privilege Update in Cloud-Assisted IoMT

Cloud-assisted Internet of Medical Things (IoMT) is becoming an emerging paradigm in the healthcare domain, which involves collection, storage and usage of the medical data. Considering the confidentiality and accessibility of the outsourced data, secure and fine-grained data sharing is a crucial requirement for the patients. Attribute-based encryption (ABE) is a promising solution to deal with this issue, but considering its property of each attribute sharing with multiple users, how to flexibly and efficiently update access privileges of certain users without affecting others is still a serious challenge. In this article, we propose a secure and fine-grained data sharing scheme with flexible user access privilege update in cloud-assisted IoMT environment. Specifically, we take ABE as the basic building block, and utilize proxy re-encryption and key blinding techniques to empower the cloud server to re-encrypt the ciphertext affected by revocation and update keys for unrevoked users. In addition, adding attributes for users to extend their access rights is realized only based on few key components stored in cloud without entirely re-computing and re-issuing keys for them. As a result, the patients are able to flexibly and efficiently share their data and manage users’ privileges. Formal proof and detailed performance evaluation demonstrate the security and efficiency of the proposed scheme.