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With the rapid growth of the Internet of Things (IoT), the Internet of Medical Things (IoMT) has emerged as a critical sector that enhances convenience and plays a vital role in saving lives. IoMT devices facilitate remote access and control of various medical tools, significantly improving accessibility in the healthcare field. However, the connectivity of these devices to the internet makes them vulnerable to adversarial attacks. Safeguarding medical data becomes a paramount concern, particularly when precise biometric readings are required without compromising patient safety. This paper proposes a fast encryption mechanism to protect the color information in medical videos utilized within the IoMT environment. Our approach involves scrambling medical video frames using a rapid block-splitting method combined with simple operations. Subsequently, the scrambled frames are encrypted using different keys generated from the logistic map. To ensure the practicality of our proposed method in the IoMT setting, we implement the encryption mechanism on a cost-effective Raspberry Pi platform. To evaluate the effectiveness of our proposed mechanism, we conduct comprehensive simulations and security analyses. Notably, we investigate medical test videos during the evaluation process, further validating the applicability of our method. The results confirm our proposed mechanism's robustness by hiding patterns in original videos, achieving high entropy to increase randomness in encrypted videos, reducing the correlation between adjacent pixels in encrypted videos, and resisting various attacks. [ABSTRACT FROM AUTHOR]

The recent advancements in the Internet of Medical Things (IoMT) have significantly contributed to improving personalized medicine and patient diagnosis and monitoring. Nonetheless, the implementation of IoMT may encounter obstacles due to security and privacy concerns. Federated learning emerges as a promising solution, enabling multiple devices to collaborate on training rich, heterogeneous datasets while preserving privacy. Despite its potential, traditional federated learning methods exhibit vulnerabilities such as single points of attack or failure and performance degradation with heterogeneous data. To this end, this paper proposes a blockchain federated learning system to address these limitations. In the proposed blockchain, a Proof-of-Contribution-Earned (PoCE) consensus protocol is designed for block propagation and miners’ selection using an improved addition tic-tac-toe game. To overcome the challenge related to heterogeneous data, a reward system based on a cooperation strategy is proposed to ensure that high-quality data is shared among health institutions. We employ a Convolutional Neural Network (CNN) where we replace the fully connected layers with sparse ones to minimize the number of parameters using an exponential random graph while maintaining model accuracy. The experimental results on real-world heterogeneous data demonstrate that the proposed system outperforms existing state-of-the-art systems in terms of accuracy and convergence rate. Security analysis reveals that the proposed system is robust against existing security and privacy-related attacks. [ABSTRACT FROM AUTHOR]

The security of Internet of Medical Things (IoMT) is closely linked to patient safety, unauthorized access may result in grave consequences. This paper reports a privacy protection scheme in the IoMT. A 4D memristive map (4D-TLMM) combining trigonometric functions, Logistic map and memristor is first designed with Lyapnov exponent and chaotic range increasing with the parameters, and its high randomness of resulting chaotic sequence is verified using NIST and sample entropy. Furthermore, a digital hardware testbed is developed to demonstrate its feasibility for hardware implementation. Then we present and analyze the specific situation of IoMT, and an image encryption algorithm (TLMM-IEA) using novel reflection scrambling and split diffusion is developed accordingly, which effectively disrupts the pixel relationships and ensures that only correct key can access patient information. After conducting numerical simulations on medical datasets, it is determined that the proposed scheme achieves average NPCR and UACI values of 99.6086 % and 33.4644 % , and possesses comprehensive robustness. Additionally, the encryption of 224 × 224 image using it only takes 0.0306 s, demonstrating its exceptional reliability in protecting IoMT security. [ABSTRACT FROM AUTHOR]

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper. [ABSTRACT FROM AUTHOR]

One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies. [ABSTRACT FROM AUTHOR]

The research work proposed for the X-band microstrip line-based magnetic resonance imaging (MRI) coils has been accomplished with coplanar waveguide feeding and ha highlighted the design parameters to be employed in the internet of medical things (IoMT) features. The proposed research has focused on the wireless body area networks (WBAN) phenomenon in simulated human organs. It has been employed to study the electro-magnetic (EM) parameters of the simulated human organ and the functioning of wearable MRI coils on the human body. Therefore, these coils have been configured in triangle-shaped hierarchical structures, and each layer has been printed on both sides of the conductive strips. These proposed coils utilize a Bakelite substrate with a 1.6-mm thickness and an equivalent dielectric strength of 1.2. It has 69.9 × 85.2 × 1.6 mm3 dimensions and was fabricated using microwave integrated circuits (MIC). These coils have been generated at 8 GHz and this spectrum has been justified with the microwave X band (8–12 GHz) using the standard measured results. Hence, these coils have demonstrated 45.81-dB signal attenuation with a 1-dB standing wave ratio (SWR). Therefore, this research has extended to the different kinds of virtual simulation scenarios in diagnosis applications. Additionally, the research delves into the electromagnetic characteristics, encompassing electric and magnetic fields, the specific absorption ratio (SAR), and temperature. These characteristics are thoroughly analyzed using MRI phantom models within virtual environments. As a result of this comprehensive analysis, the suitability and efficacy of these MRI coils have met rigorous standards. These coils are highly demanded by complicated systems functioning in these bands for IoMT and MRI diagnosis applications. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of IoMT, federated learning (FL) is an excellent choice to manage machine learning (ML) algorithms to drive this analysis. This is because FL models can be trained in a distributed manner on local heterogeneous datasets that all contribute to the "collective wisdom". The model parameters can be regulated and shared without sharing the actual health data, ensuring confidentiality and security. This paper makes a case for the viability of FL-based analysis of data acquired via IoMT by presenting some use cases and recent work in this area and proposing a novel framework for data analysis using FL specifically in the context of mental stress detection. It shows that FL-based methods can significantly reduce the required communication overhead for each local device from 10.02MB/day up to only 754B/day as compared to non-FL techniques. [ABSTRACT FROM AUTHOR]

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption. [ABSTRACT FROM AUTHOR]

The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management. [ABSTRACT FROM AUTHOR]

The medical cyber-physical system utilizes various Internet of Medical Things (IoMT) devices that are connected to the network for real-time management and medication of patient. The resource constraint IoMT devices require energy-efficient lightweight security schemes to protect medical data. To the best of our knowledge, majority of the existing studies concentrate either on security or on energy efficiency issues. The main contribution of this research is to develop an energy-efficient lightweight authentication method without sacrificing the security level. The authentication is done using multiple factors, namely password, physical unclonable function (PUF), Chebyshev polynomial, smartcard and fuzzy extractor. Chebyshev polynomial is used for non-identification of the private key by any attacker, whereas PUF protects the smart card from cloning by generating unique challenge–response pairs. Apart from authentication, the proposed scheme includes Blockchain-enabled distributed trustable ecosystem among independent participants where miner is selected using lightweight fuzzy system. The proposed scheme carries out the formal security analysis using real or random model which is perseverance against different external attacks and the security verification has been done using AVISPA (Automate Validation of Internet Security Protocols and Applications) tool. As a major finding, the simulation result using NS-3 simulator confirms that the proposed study outperforms the existing studies in terms of packet loss rate, throughput, end-to-end delay, computational cost and communication cost. [ABSTRACT FROM AUTHOR]

Millennials have the advantage of accessing readily available modern scientific advancements, particularly in technology. One of these technologies that encompasses varied functionalities is the Internet of Things (IoT). In the midst of the Covid-19 pandemic, IoT, specifically Internet of Medical Things (IoMT), had pivotal significance in monitoring and tracking different health parameters. It autonomously manages an individual's health data and stores the same as Electronic Health Records (EHRs). However, the networking protocols used by IoMT are not adequate enough to ensure the security and privacy of EHRs. Consequently, such technology is susceptible to cyber-attacks, which have become more prevalent over time and have taken various forms, that generally the stakeholders are not aware of. This paper introduces machine learning-driven intrusion detection systems as a solution to tackle this issue. The focus of this study is on devising a Machine Learning (ML) oriented Intrusion Detection System (IDS) designed to identify cyber-attacks targeting IoMT based systems. Several classification based ML techniques such as Multinomial Naive Bayes, Logistic Regression, Logistic Regression with Stochastic Gradient Descent, Linear Support Vector Classification, Decision Tree, Ensemble Voting Classifier, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting and Extreme Gradient Boosting were used, whereupon the Adaptive Boosting was experimentally found to perform the best on performance metrics such as accuracy, precision, recall, F1-score, False Detection Rate (FDR) and False Positive Rate (FPR). Further, it was found that Adaptive boosting based IDS for IoMT performed comparatively better than the existing ToN_IoT based IDS models on performance metrics such as accuracy, F1-score, FPR and FDR. [ABSTRACT FROM AUTHOR]

The problems of data storage and sharing have been well solved with cloud storage. However, the disadvantage is that users' messages are stored in the cloud without encryption protection. Cloud storage managers can access or even obtain user data, which brings significant security risks to data owners. Therefore, some cloud storage centers adopt ciphertext storage, which can protect them from the risk of user privacy disclosure. However, there are also problems, that is, how to find the desired data achieved in the cloud server search and how to ensure information integrity in the search process. Moreover, the encryption operation destroys the value and size relation of the original plaintext data. It does not have semantic and statistical characteristics when retrieving. Combining the hidden access structure in an attribute-based file searchable encryption model with proxy Re-Encryption technology, we propose an attribute-based file retrieval model with a partially hidden access structure supporting Proxy Re-Encryption. The model solves the above problems effectively and supports keyword updating. Finally, the security of the model is proved based on the hypothesis of D-linear and decisional q-parallel bilinear Diffie-Hellman exponent under the random oracle model. [ABSTRACT FROM AUTHOR]

Mental health diagnosis often relies on subjective evaluations, which can be intrusive and lack objectivity. With the current global situation brought about by the COVID-19 pandemic, the need for real-time, on-demand healthcare services has become more apparent than ever. Fortunately, wearable Internet of Medical Things (IoMT) devices, such as wrist actigraphs and smartphones, offer a promising solution by generating objective data that can aid in early-stage mental health diagnosis. This paper presents a novel Deep Convolutional Neural Architecture (CNN) with a split attention mechanism, that outperforms the traditional methods of analyzing motor activity data in diagnosing schizophrenia. To address the unique characteristics of motor activity, the proposed method includes a novel imputation method, a sampling technique based on a sliding window for sample expansion, and the Synthetic Minority Over-sampling Technique (SMOTE) technique for class balancing. The results demonstrate the highest accuracy of 94% using 24-h actigraphy data. Overall, the application of this methodology can greatly contribute to the development of pervasive healthcare systems, providing non-invasive, objective, and real-time mental healthcare services. [ABSTRACT FROM AUTHOR]

One of the challenges that arise when utilizing real-time reaction services, such as constructing deep learning models within the Internet of Medical Things (IoMT) infrastructure, is effectively balancing the computation load between the cloud and fog computing layers. This paper proposes a unified framework of offline training and online response to the healthcare professional. The framework gathers medical images from various heterogeneous IoMT devices and then arranges them into homogeneous locations in the cloud, using a stage-one classification stage (or offline training). Furthermore, the stage-two classification (or online response) is employed to detect the type of cancer for each homogeneous location containing the same image type within the cloud. To evaluate the framework, we conducted extensive experiments on six well-known cancer datasets of multiple types. The stage-one classification shows superior results of the error rates for the InceptionResNetV2 and DenseNet201 pre-trained transfer learning models of 0.33% and 0.43% with accuracy values of 99.67% and 99.57% respectively. In the stage-two classification, the results show different performances on each dataset. The point is that each dataset is organized separately which helps in studying the influence of pre-trained transfer learning models and improving their performance in the absence of intervention and bias in datasets. [ABSTRACT FROM AUTHOR]

The introduction of IoT technologies, such as those used in remote health monitoring applications, has revolutionized conventional medical care. Furthermore, the approach utilized to obtain insights from the scrutiny of lifestyle elements and activities is crucial to the success of tailored healthcare and disease prevention services. Intelligent data retrieval and classification algorithms allow for the investigation of disease and the prediction of aberrant health states. The convolutional neural network (CNN) strategy is utilized to forecast such anomaly because it can successfully recognize the knowledge significant to disease anticipation from amorphous medical heath records. Conversely, if a fully coupled network-topology is used, CNN guzzles a huge memory. Furthermore, the complexity analysis of the model may rise as the number of layers grows. Therefore, we present a CNN target recognition and anticipation strategy based on the Pearson correlation coefficient (PCC) and standard pattern activities to address these shortcomings of the CNN model. It is built in this framework and used for classification purposes. In the initial hidden layer, the most crucial health-related factors are chosen, and in the next, a correlation-coefficient examination is performed to categorize the health factors into positively and negatively correlated groups. Mining the occurrence of regular patterns among the categorized health parameters also reveals the behavior of regular patterns. The model output is broken down into obesity, hypertension, and diabetes-related factors with known correlations. To lessen the impact of the CNN-typical knowledge discovery paradigm, we use two separate datasets. The experimental results reveal that the proposed model outperforms three other machine learning techniques while requiring less computational effort. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Security of Internet-of-Medical-Things (IoMT) networks has evolved as a vital issue in recent years. The IoMT networks are designed to link patients with caregivers. All reports, data, and medical signals are transferred over these networks. Hence, these networks require robust and secure access strategies for patients to send their vital data or reports. Hence, hacking of these networks may lead to harmful effects on patients. One of the vulnerable points to hacking in these networks is the access point. Access to these networks could be performed with biometrics. The popular biometric traits for this purpose are biomedical signals such as Electrocardiogram (ECG) signals, as they are continuously monitored and measured for patients. A common thread between all biometric authentication systems is the possibility of losing the biometric traits forever if hacking attempts manage to concur the biometric template storage. A new trend in the field of biometric authentication is to avoid the utilization of original biometrics in access control processes. A possible alternative is to use cancelable biometrics instead. Cancelable biometrics can be generated through encryption schemes or non-invertible transforms. This paper adopts both strategies in a unified framework for cancelable ECG signal recognition that can be used in the access step of IoMT networks. The proposed framework begins with applying a non-invertible transformation on the ECG signals through fuzzy logic to change the dynamic range of the signals. As this process is non-invertible in nature, it prevents the recovery of the original ECG signals from the processed versions, which is the main target of cancelable biometric systems. After that, lightweight encryption through XOR operation with user-specific patterns is implemented. Here, the high complexity of full encryption schemes that need a large processing burden is eliminated. The addition of the encryption stage enhances the security of cancelable biometric traits, allowing a hybrid nature of the proposed cancelable biometric framework through the merging of non-invertible transforms and encryption algorithms. Moreover, an FPGA hardware implementation is introduced for real implementation of the proposed ECG-based cancelable biometric recognition framework. This hardware can accompany the user to allow access of the IoMT network when requested. Experimental results show a promising performance of the proposed framework with a large Area under the Receiver Operating Characteristic curve (AROC) of 99.5% and an Equal Error Rate (EER) of 0.058%. [ABSTRACT FROM AUTHOR]

Security vulnerabilities associated with Internet of Medical Things (IoMT) may lead to health disasters. The researchers already have designed several lightweight authentication techniques to provide secure communication towards physical layer security in a heterogeneous IoMT environment. Blockchain technology is utilized to solve the existing security issues of IoMT ensuring the network layer security. This paper presents a lightweight two factor authentication scheme (TFAS) for a blockchain-enabled IoMT environment which focuses on both the physical and network layer security without involvement of any centralized third party. The proposed TFAS involves device, user and data authentication along with user authorization for improved access control of medical data. It is a two-phase authenticity verification method which involves the authentication of users and devices using the PUF and fuzzy extractor in the first phase and blockchain-based data authentication along with authorization of users in the second phase. It also improves storage capacity of blockchain-enabled IoMT network and ensures its scalability using a cluster of smart-contract enabled inter planetary file systems servers. The formal security analysis has been performed using the real-or-random model for evaluating security of session key and mutual authentication protocol. The informal security analysis provides strong evidences for resilience of TFAS from various known attacks. Moreover, the proposed scheme outperformed other existing schemes in terms of communication cost, computational cost and storage. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) are commonly employed to mitigate network security threats in various fields, including federated learning applications within the Internet of Medical Things (IoMT). However, IDSs face challenges owing to the sheer volume of network traffic, high-dimensional datasets and the necessity for real-time detection. Although machine learning integration assists IDSs in overcoming these challenges, modelling difficulties persist due to varied evaluation criteria and levels of conflict and importance. Multi-criteria decision-making (MCDM) solutions have been utilised in IoMT and IDS, yet they fall short in capturing the subjective judgements of experts and rely on normalisation approaches, which can impact results. This study seeks to address these issues through the integration of robust MCDM methodologies, namely fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM). Utilising rough Fermatean fuzzy sets (RFFSs), this integration produces precise solutions with reduced uncertainty. Our methodology involves adopting a decision matrix for IDS classifiers based on integrated evaluation criteria, followed by deriving new formulations and developments for RFFSs-based FDOSM and FWZIC for the modelling and weighting of criteria, respectively. Evaluations using datasets involving 125,973 records and 41 features across 17 evaluation criteria revealed that accuracy–security and training time–performance weights yielded the highest scores, whereas false negative rate–security and CPU time–performance criteria received the lowest weights. The random forest emerged as the optimal IDS classifier. Systematic modelling, sensitivity analysis and comparative studies confirmed the robustness of our results. [ABSTRACT FROM AUTHOR]

Internet of medical things (IoMT) enable physicians to provide precise care on the Internet for registered patients anywhere, bringing convenience to people's everyday life. Considering the importance of patient's privacy in IoMT, data security between patients and medical servers should be protected. Therefore, the authentication of identity and the agreement of a shared secret key are particularly important. In this work, we propose a lightweight anonymous authentication scheme between patients and medical servers in IoMT. We combine blockchain technology with biometric technology in order to form a shared session secret key. It can protect the privacy of patients through mutual authentication between patients and servers. Afterwards, the formal analysis of BAN logic shows that our scheme is secure. Non-formal analysis shows that our scheme achieves designed security objectives. Finally, comprehensive comparative experiments show that our proposed scheme achieves a better performance in both computation and communication efficiency. [ABSTRACT FROM AUTHOR]

The rapid development in medical devices and communication technologies led to the emergence of the Internet of Medical Things (IoMT), resulting in several new applications that connect to healthcare IT systems through online computer networks. A vast quantity of data produced by these applications will be received at the edge gateway periodically to transmit them to the remote cloud for further handling. However, sending this huge data to the cloud across the IoT network will place a significant burden on the IoT network. The long processing delays and exchanged data have a considerable influence on the real-time IoT applications response time. The responsiveness time of these applications will be decreased. Therefore, the IoT applications exploit the advantages of fog computing, which serves as a middle layer between the platform of cloud and IoT devices to minimize the transmitted data and enhance the response time. In this paper, we propose an efficient compression technique (ECoT) for reducing transmitted Electroencephalography (EEG) data without loss on the IoMT Networks based on Fog Computing. The ECoT combines three efficient data reduction techniques: DBSCAN clustering, Delta encoding, and Huffman encoding, to decrease the volume of data in the Fog node then sending it to the platform of cloud. First, the DBSCAN clusters the EEG data into clusters. Then, the Delta encoding is applied to the indices of EEG data in each cluster. Finally, the Huffman encoding encodes the vector of differences for each cluster. The encoded data from clusters is combined into a file to be sent to the platform of cloud. The results show that the ECoT technique introduced improved results in terms of compression ratio, sent data, compression power, and compression and decompression times compared with other methods. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT), with advances in wireless technologies, has boosted traditional healthcare into smart healthcare. Computer-aided diagnosis technology based on IoMT is thriving with the help of deep learning. However, fully supervised deep learning need to be trained with enough annotated samples, which is difficult in healthcare. Few-shot learning network can be trained with only a small number of annotated samples, which alleviates the difficulty of medical image collection and annotation. We proposed a few-shot prototype network to address the shortage of annotated samples based on IoMT. First, the capability of the feature extractor is enhanced by designing a contrast learning branch. Second, a novel strategy for constructing positive and negative sample pairs is proposed for the contrast learning, which avoids to specifically maintain a sample queue. Third, the contrast learning branch is also used to rectify the corruption samples and refine the category prototype. Finally, the hybrid loss, consisting of prototype loss and contrastive loss, is used to improve the classification accuracy and convergence speed. Our method achieved satisfactory performance on the mini-ISIC-2 i and mini-ImageNet datasets. [ABSTRACT FROM AUTHOR]

The creation of smart healthcare systems is a viable strategy to improve the quality and availability of healthcare services. Identity theft, data breaches, and denial-of-service attacks are just some of the security concerns that have arisen as a result of connecting wireless networks and smart medical equipment. A secure and trustworthy smart healthcare system that can protect patient data and preserve the confidentiality of private medical information is especially important in light of these vulnerabilities. Medical diagnosis assumes increasing importance as the amount of data created daily in the 6G-enabled Internet-of-Medical Things (IoMT) grows exponentially. To enhance the anticipation accuracy and supply a real-time medicinal diagnosis, this research presents an approach integrated into the 6G-enabled IoMT that requires less human intervention for healthcare applications. To do this, the proposed system combines deep learning with optimization methods. MobileNetV3 architecture is then used to learn the features taken from each image. In addition, we improved the performance of the HGS-based arithmetic optimization algorithm (AOA). The operators of the HGS are used in the new approach, dubbed AOAHG, to improve the AOA operation capacity as the viable province is divided up. We design a 6G-enabled IoMT approach that requires fewer humans in healthcare settings but yields faster diagnostic results. The new approach was developed to be used in systems with limited means. The created AOAHG prioritizes the most important features and guarantees an overall upgrade in model categorization. When compared to other methodologies in the literature, the framework's results were impressive. The created AOAHG also outperformed alternative FS methods in terms of the achieved accuracy, precision, recall, and F1-score. For instance, AOAHG had 92.12% accuracy with the ISIC dataset, 98.27% with the PH2 dataset, 95.24% with the WBC dataset, and 99.84% with the OCT dataset. [ABSTRACT FROM AUTHOR]

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient's end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques. [ABSTRACT FROM AUTHOR]

The COVID-19 outburst has encouraged the adoption of Internet of Medical Things (IoMT) network to empower the antiquated healthcare system and alleviate the health care costs. To realise the functionalities of the IoMT network, 5G heterogeneous networks emerged as an exemplary connectivity solution as it facilitates diversified service provisioning in the service delivery model at more convenient care. However, the crucial challenge for 5G heterogeneous wireless connectivity solution is to facilitate agile differentiated service provisioning. Lately, considerable research endeavour has been noted in this direction but multiservice consideration and battery optimisation have not been addressed. Motivated by the gaps in the existing literature, an intelligent radio access technology selection approach has been proposed to ensure Quality of Service provisioning in a multiservice scenario on the premise of battery optimisation. In particular, the proposed approach leverages the concept of Double Deep Reinforcement Learning to attain an optimal network selection policy. Eventually, the proposed approach corroborated by the rigorous simulations demonstrated a substantial improvement in the overall system utility. Subsequently, the performance evaluation underlines the efficacy of the proposed scheme in terms of convergence and complexity. [ABSTRACT FROM AUTHOR]

The internet of medical things is one of the popular application of internet of things (IoT) where various small medical devices are interconnected with each other to share sensitive data. But IoT device and network are vulnerable to several security attacks such as sniffing, spamming, flooding etc. initiated by Botnets which is a major problem for time critical application such as healthcare. Any kind of security attacks may lead to data breach, data alteration and non-availability of data may endanger the life of a patient in critical situation. To prevent these kinds of attacks machine learning and deep learning techniques can be applied to create an effective intelligent botnet attack detection engine (IBADE). This paper presents the performance analysis of 8 popular Machine Learning Models such as naïve bayes, decision tree, random forest, support vector machine, logistic regression, single-layer perceptron, convolution neural network and multi-layer perceptron to choose the most effective one for intelligent botnet attack detection engine (IBADE) modeling. N-BaIoT Dataset is used to train and test the above mentioned algorithms while principal component analysis (PCA) and linear discriminant analysis are used for feature reduction to get higher performance. Experimental result shows that the performance of Random Forest based ML model is best when used with PCA in terms of Accuracy = 0.99 and Precision = Recall = F1 score = 1. Hence Random Forest based model is the most effective to detect various Botnet attacks in an IoT network. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are among the major trends of nowadays. Billions of connected devices are creating great business profits and performing a multitude of automated tasks in many daily human activities. In healthcare service delivery, IoT capabilities are difficult to overestimate, they are progressively becoming entangled and commonly coined Internet of Medical Things (IoMT). The participating nodes in IoMT networks generate, collect and exchange huge amounts of extremely private and sensitive data. Numerous security vulnerabilities arise due to the complexity and the heterogeneity of the technology. New risks, born out of IoMT systems, cannot easily be supported by existing risk management frameworks. The existing cyber-security risk assessment methods and approaches, deployed in several organizations, will not address the IoMT inherent risks properly. This study includes a comprehensive review of IoMT systems. Popular risk assessment methods are discussed and their suitability to IoMT is dealt with in detail. Based on this study, we propose a framework to enhance trust and help with decision making in e-healthcare environments given its high-risk exposure. The proposal is based on a quantified risk assessment approach. Our aim is to define a novel approach/model for improving trust and risk management in an e-health context. [ABSTRACT FROM AUTHOR]

Internet of medical things (IoMT) plays an important role nowadays to support healthcare system. The hospital equipment's called as medical things are now connected to the cloud for getting many useful services. The data generated from the equipments are sent to the cloud for getting the desired service. In current scenario, most hospitals collect many images using equipments, but these equipments have less computational capability to process the huge generated data. In this work, one such equipment is considered which can take the human eye images and send the images to the cloud for detection of cherry red spot (CRS). CRS disease in eyes is considered as one of the very dangerous disease. The early diagnosis of CRS disease needs to be focused in order to avoid any adverse effect on human body. In this paper, a machine intelligence based model is proposed to detect the CRS disease areas in the human eyes by analyzing several CRS disease images using IoMT. The proposed approach is mainly focused on fuzzy rule-based mechanism to carry out the identification of such affected area in the eyes in cloud layer. From the results, it is observed that the CRS disease areas in the eyes are detected well with better detection accuracy and lower detection error than k-means algorithm. This approach will help the doctors to track the exact position of the affected areas in the eye for its diagnosis. The simulation is performed using socket programming written in Python 3 where a cloud server and a client device are created and images are sent from the client device to the server, and afterwards the detection of CRS is performed at the server using MATLAB R2015b. The proposed method is able to provide better performance in terms of detection accuracy, detection error and processing time as 94.67%, 5.33% and 1.1481% units respectively on an average case scenario. [ABSTRACT FROM AUTHOR]

One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things (IoMT), collaboration among institutes can help complex medical and clinical analysis of disease. Deep neural networks (DNN) require training models on large, diverse patients to achieve expert clinician-level performance. Clinical studies do not contain diverse patient populations for analysis due to limited availability and scale. DNN models trained on limited datasets are thereby constraining their clinical performance upon deployment at a new hospital. Therefore, there is significant value in increasing the availability of diverse training data. This research proposes institutional data collaboration alongside an adversarial evasion method to keep the data secure. The model uses a federated learning approach to share model weights and gradients. The local model first studies the unlabeled samples classifying them as adversarial or normal. The method then uses a centroid-based clustering technique to cluster the sample images. After that, the model predicts the output of the selected images, and active learning methods are implemented to choose the sub-sample of the human annotation task. The expert within the domain takes the input and confidence score and validates the samples for the model's training. The model re-trains on the new samples and sends the updated weights across the network for collaboration purposes. We use the InceptionV3 and VGG16 model under fabricated inputs for simulating Fast Gradient Signed Method (FGSM) attacks. The model was able to evade attacks and achieve a high accuracy rating of 95%. [ABSTRACT FROM AUTHOR]

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients' health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare industry by leveraging its capabilities to improve personalized healthcare services by enabling seamless communication of medical data. IoMT facilitates prompt emergency responses and provides improved quality of medical services with minimum cost. With the advancement of modern technology, progressively ubiquitous medical devices raise critical security and data privacy concerns through resource constraints and open connectivity. Vulnerabilities in IoMT devices allow unauthorized access for potential entry into healthcare and sensitive personal data. In addition, the patient may experience severe physical damage with the attack on IoMT devices. To provide security to IoMT devices and privacy to patient data, we have proposed a novel IoMT framework with the hybridization of Bayesian optimization and extreme learning machine (ELM). The proposed model derives encouraging performance with enhanced accuracy in decision-making process compared to similar state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Internet of Multimedia Things (IoMT) has gained popularity due to its immersive growth and real-time applications range from smart health to smart transportation systems. These systems always have various threats related to security breaches and privacy. With the passage of time, all types of cybercrimes and terrorism activities and its planning were initiated by using these networks and technologies. Terrorist attacks are unavoidable because of well-organized and well-planned attack planning. The all the data among these threat actors transmitted by using IoMT sensors and devices. Therefore, there is a need to analyze this type of data and predict the terrorist activities for in-time decision making. This paper presents a complete overview of existing models for such type of data and proposes a Terrorist Attacks Internet of Multimedia Things (TA-IoMT) model and a predictive model for public security in IoMT networks. The proposed model provides more effective data handling, cloud storage data management, and prediction to control and detect these kinds of activities. The results show an average of 89% accuracy, 0.73% sensitivity, and 0.92% specificity as compared to existing solutions. [ABSTRACT FROM AUTHOR]

Internet of Multimedia Things (IoMT), a special subset of Internet of Things (IoT), is a novel paradigm which is progressively increasing and gaining in popularity, allowing many multimedia content based applications such as Wireless Multimedia Sensor Networks (WMSNs). The standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), originally designed for transmitting scalar data traffic of IoT applications, should be customized in order to deal with IoMT applications which pose new challenges and requirements in terms of the Quality of Service (QoS) and the user's Quality of Experience (QoE). For this purpose and thanks to the flexibility and adaptability of RPL, we propose, in this paper, a multipath version of RPL, not defined in the RPL specification RFC 6550, named MP-RPL. MP-RPL leverages the multiparent feature offered by RPL in order to construct multiples end-to-end paths of different qualities based on radio link quality measurements. It is intended to improve the video traffic delivery for IoMT based WMSN applications by simultaneously using the built paths while taking into account the video traffic differentiation as per priority levels. Compared to the traditional single-path RPL, the results obtained from simulations when considering the influence of video characteristics, show that our proposal provides feasible and acceptable QoS and QoE performance metrics for multimedia applications, while maintaining RPL compliance. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage. Today, this large volume of IoMT generated medical data is stored in the centralized storage system. However, centralization of patient sensitive information leads to a single point of failure, privacy, and security concern. To address these issues, we propose a smart contracts enabled consortium blockchain network. We integrated interplanetary file systems (IPFS) cluster node where smart contracts are deployed at the initial stage for authentication of patient's and medical devices, the same cluster layer is also proposed as a distributed data storage layer for device-generated data after authentication and these data are securely transmitted over the consortium blockchain. The IPFS cluster node ensures the security and authentication of the devices and it also provides secure storage management in IoMT enabled healthcare system. The consortium network enables the privacy of data owing to hash-based storage in a block of IoMT enabled healthcare network. [ABSTRACT FROM AUTHOR]

Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a transformative concept in healthcare, leveraging the power of connected devices and technology to improve patient care and health outcomes. These devices are typically connected to the internet or a network and can communicate with each other to exchange data and provide insights to healthcare providers, patients, and other stakeholders. Advanced digital technologies like artificial intelligence, machine learning, and Blockchain integrated into the Internet of Medical Things (IoMT) can better mitigate the impact of pandemics and protect public health. IoMT utilizes medical sensors to capture real-time physiological data of patients and is available to a medical professional to diagnose, recognize, analyze, and make appropriate decisions. Data breaches or cyberattacks could compromise the security and integrity of IoMT devices and data, exposing sensitive information or causing malfunctions or disruptions. Consequently, to make IoMT systems reliable, data protection and secure communication must conform to security standards. Blockchain technology is being used in the healthcare industry to ensure the security of patient records and to streamline the sharing of information among healthcare providers, laboratories, pharmaceutical firms, and other healthcare providers. Overall, digital technologies have been instrumental in managing the COVID-19 pandemic, enabling more efficient surveillance, response, and care delivery. By leveraging these technologies, public health authorities and healthcare providers have been able to better mitigate the impact of the pandemic and protect public health. [ABSTRACT FROM AUTHOR]

Recognizing human activity from highly sparse body sensor data is becoming an important problem in Internet of Medical Things (IoMT) industry. As IoMT currently uses batteryless or passive wearable body sensors for activity recognition, the data signals of these sensors have a high sparsity, which means that the time intervals of sensors' readings are irregular and the number of sensors' readings in time unit is frequently limited. Therefore, learning activity recognition models from temporally sparse data is challenging. Traditional machine learning techniques cannot be applicable in this scenario due to their focus on a single sensing modality requiring regular sampling rate. In this work, we propose an effective end-to-end deep neural network (DNN) model to recognize human activities from temporally sparse data signals of passive wearable sensors that improve the accuracy rate of human activity recognition. A dropout technique is used in the developed model to deal with sparsity problem and avoiding overfitting problem. In addition, optimization of the proposed DNN model was performed by evaluating a different number of hidden layers. Various experiments were conducted on a public clinical room dataset of sparse data signals to compare the performance of the proposed DNN model with the conventional and other deep learning approaches. The experimental results demonstrate that the proposed DNN model outperforms the existing state-of-the-art methods in terms of lower inference delay and activity recognition accuracy. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) includes interconnected sensors, wearable devices, medical devices, and clinical systems. At the same time, skin cancer is a commonly available type of cancer that exists all over the globe. This study projects a new segmentation based classification model for skin lesion diagnosis by combining a GrabCut algorithm and Adaptive Neuro-Fuzzy classifier (ANFC) model. The proposed method involves four main steps: preprocessing, segmentation, feature extraction, and classification. Initially, the preprocessing step is carried out using a Top hat filter and inpainting technique. Then, the Grabcut algorithm is used to segment the preprocessed images. Next, the feature extraction process takes place by the use of a deep learning based Inception model. Finally, an adaptive neuro-fuzzy classifier (ANFC) system gets executed to classify the dermoscopic images into different classes. The proposed model is simulated using a benchmark International Skin Imaging Collaboration (ISIC) dataset and the results are examined interms of accuracy, sensitivity and specificity. The proposed model exhibits better identification and classification of skin cancer. For examining the effective outcome of the projected technique, an extensive comparison of the presented method with earlier models takes place. The experimental values indicated that the proposed method has offered a maximum sensitivity of 93.40%, specificity of 98.70% and accuracy of 97.91%. [ABSTRACT FROM AUTHOR]

Nowadays, the massive proliferation of real-world multimedia devices has paved the way to the emergence of a new paradigm called the Internet of Multimedia Things wherein Low-power and Lossy Networks (LLNs) are the main components of this new technology. RPL is an IPv6 routing protocol for LLNs designed by IETF to meet the requirements of a wide range of LLN applications such as Wireless Multimedia Sensor Networks where video traffic is expected to reach 6 times more than non-video traffic in 2025. The mono-instance version of RPL is far from satisfying the network's Quality of Service (QoS) and the user's Quality of Experience (QoE), as video in its compressed form is typically composed of various frames with different priorities requiring different QoS and QoE levels. In this paper, we exploit the multi-instance version of RPL for developing a new routing approach that improves the transport of a compressed video, composed of two types of frames with different priorities, by delivering each of them on the corresponding instance. The fundamental question we are addressing, is to find the best way to construct these instances: instances with Nodes Disjoint (ND) or Links Disjoint (LD). To do so, we designed a Multi-Instance routing protocol, named MI-RPL, and we performed extensive simulation experiments using the Cooja simulator. The comparison of MI-RPL-ND, MI-RPL-LD and RPL which regards to QoS and QoE performance metrics, confirms that multi-instance routing for video transmission in WMSN (and in particular MI-RPL-ND) is the best choice. [ABSTRACT FROM AUTHOR]

Most of the current research has been restricted to scalar sensor data based IoT applications. However, today's research and development activities rely on multimedia-based services and applications while this kind of applications has several requirements in terms of storage, bandwidth, latency, etc. Furthermore, protocols dedicated to IoT applications have proved their weakness in multimedia environments. Hence, a new paradigm called internet of multimedia things (IoMT) has been proposed to fulfill the requirements of multimedia applications. In this paradigm different multimedia things can interact and cooperate with each other over the Internet. Moreover, IETF ROLL working group standardized an IPv6 routing protocol for low-power and lossy networks (RPL for LLNs) for resource constrained devices. In this paper, we propose an enhanced version of RPL for IoMT called free bandwidth (FreeBW)-RPL in which the sensed data is essentially provided by multimedia devices. FreeBW-RPL protocol proposes a new objective function called FreeBW that takes the FreeBW calculation in the network layer. We set the QoS routing challenge as the amount of the bandwidth while selecting the routing path in order to measure the maximum FreeBW so as to deliver better performance of the multimedia applications. Simulations have been conducted over COOJA simulator. The obtained results proved that our proposal outperforms the basic ones in terms of end-to-end delay, throughput, packet delivery ratio and energy consumption and provides better performance than other protocols. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) visualizes a network of medical devices and society adopting wireless communications to enable interchange of healthcare data. IoMT is utilized to gather real-time data from medical equipment and sensors. This enables possibility for continuous health monitoring and prediction. There is concern related to potential privacy and safety hazards connected with the group and transmission of sensitive health data over the network. This study proposes a hybrid hierarchical deep learning (DL) model enhanced with features and a metaheuristic algorithm to achieve health monitoring and prediction based on IoMT. The information gained from the analysis helps to identify important features for prediction. The feature selection phase applies Self-regularized Quantum Coronavirus Optimization Algorithm (SQCOA) to prioritize important features for prediction. The prediction phase includes Optimized Long Short-Term Memory (OLSTM) and Hierarchical Convolutional Spiking Neural Network (HCSNN) for feature learning and performance prediction, respectively. The proposed model is simulated by adopting MATLAB. The model attains the highest accuracy of 98%. [ABSTRACT FROM AUTHOR]

The advent of advanced video communication technologies has enabled the new paradigm Internet of Multimedia Things (IoMT) wherein connected devices share multimedia contents across the internet. New approaches in compression techniques are constantly being evolved to circumvent the limitations in storing and transmitting uncompressed video. The Compressive Sensing (CS) technique that simultaneously senses and compresses an image is now considered as a better alternative for image / video compression. In Block Based Compressed Sensing (BBCS) technique, to reduce the computational complexity of CS, a large-sized image is divided into number of small non-overlapping blocks and each block is compressed at a uniform sampling rate. Instead an Adaptive Block Based Compressive Sensing (ABBCS) technique effectively make use of resources by sensing each block at a unique sampling rate adaptively estimated based on its image features. In this paper an efficient hybrid ABBCS (HABBCS) technique is proposed. HABBCS first adaptively estimates the block-wise sampling rate in a frame and the frame average is computed… The sampling rate for the entire video is then estimated from the frame averages the video is compressed by employing BBCS. The performance metrics Peak Signal to Noise Ratio (PSNR), Video Structural Similarity Index (VSSIM), Delta E and Computational Time (CT) are computed and tabulated. The results confirm that HABBCS is faster with a performance equal to ABBCS. [ABSTRACT FROM AUTHOR]