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Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS; however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS.

Due to the recent pandemic, the healthcare sector has been forced to incorporate new technologies into its systems, such as IoT and Fog Computing. However, being new technologies, they are prone to security breaches. From this context, it is identified that medical systems do not have a sufficient level of security, due to the use of new technologies such as IoT and the lack of controls to protect these new technologies. Therefore, a model for implementing an Internet of Medical Things (IoMT) Architecture with ISO/IEC 27001 security controls for remote patient monitoring is proposed. This model has 4 stages: Stage 1 selects an information security standard for the healthcare sector. Stage 2 selects the information security controls of the selected standard. Stage 3 selects and evaluates an IoMT architecture applicable to the healthcare sector. And Stage 4 designs the information security controls for each layer of the IoMT architecture. The IoMT architecture and information security controls are simulated and experimented with physicians (the productivity of the system) and with information security expert (the quality of the implemented controls). The results of the first experiment show that ""effectiveness"", ""productivity, and ""satisfaction"" regarding the use of the IoMT architecture have an average rating of 4.05 (high level). The results of the second experiment show that ""Information Security"", ""Awareness"" and ""Security Incident Management"" regarding the quality of the security controls implemented have an average rating of 4.05 (high level).

Abstract 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.

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]

In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features. [ABSTRACT FROM AUTHOR]

When it comes to improving people's health, various forms of smart city applications are put to use in the area of smart healthcare. Internet of Medical Things (IoMT) refers to an environment that consists of linked healthcare systems, computational capabilities, and health monitors. More and more health care providers are using this environment to enhance the quality of the services they provide (QoS). The transmission of data from intelligent medical equipment to IoMT necessitates the development of an effective solution. To enhance the quality of service (QoS) of the transmission of health data, it is necessary to develop and evaluate a novel multi-hop reinforced clustering-based routing protocol (MRCRP). This is necessitated by the importance of IoMT and 5G networks. The selection of cluster heads is done in a way that is more effective with energy in order to make better use of resources. The original data are transformed into meaningful data through the utilization of min-max normalization, which enables further research. It is possible to extract important characteristics by employing a technique known as principal component analysis (PCA). After that, the suggested routing method is put into action in order to successfully transfer the data. The proposed method's performance metrics, including delay, throughput, energy consumption, and packet drop ratio, as well as a comparative comparison with conventional routing protocols, are investigated. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to various cybersecurity attacks and threats, which lead to negative consequences. Cyberattacks can damage not just the IoMT devices in use but also human life. Currently, several security solutions have been proposed to enhance the security of the IoMT, employing machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. Furthermore, blockchain technology enables a decentralized approach to the healthcare system, eliminating some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects malicious data measured by medical sensors to find fraudulent data. As a result, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. Additionally, a blockchain network is used in the fog layer to ensure that the data is not altered, enhancing the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% based on the F1-score accuracy metric. [ABSTRACT FROM AUTHOR]

Osteoarthritis and rheumatoid are most common form of arthritis disorder, affecting millions of people worldwide. This article presents a computer aided detection system (CAD) for early knee osteoarthritis and rheumatoid detection using X‐ray images and machine learning classifiers. This work also proposed a novel feature extractor from X‐ray images of knee to assist in detection and classification, called explainable Renyi entropic segmentation with Internet of Things (IoT) framework. The proposed method later utilizes model agnostic algorithm using post hoc explainability for extracting relevant information from prediction of knee joint segmentation. CAD system is integrated with an IoT framework and can be used remotely to assist medical practitioners in treatments of knee arthritis. The presented results show commendable improvement over different existing feature extractors in combination with different classifiers. The best result of proposed extractor method was obtained when combined with random forest classifier having Euclidean hyperparameter that gave an accuracy of 95.23%, among all the evaluators. The obtained results show the effectiveness of proposed feature extractor model to determine relevant features from knee and describe the suitable knee disorders. [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 internet of medical things (IoMT) empowers patients to get adaptable, and virtualized gear over the internet. Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it. Thus, a proposition is being made for a distinct scheduling technique to suitably meet these solicitations. To manage the scheduling issue, an artificial intelligence (AI) method known as a hybrid genetic algorithm (HGA) is proposed. The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches. The CloudSim is utilized to quantify its effect on various parameters like time, resource utilization, cost, and throughput. The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of 40.16ms. Thus, the experimented outcomes show that the HGA reduces cost as well as time profoundly. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) plays an important role in strengthening sustainable healthcare systems. IoMT significantly influences our healthcare because it facilitates monitoring and checking patient medical information before transferring the data to a cloud network for future use. The IoMT is a big-data platform which is growing rapidly, so it is critical to maintain all data safely and securely. In this study, Blockchain-Assisted Cybersecurity (BCCS) for the IoMT in the healthcare industry is proposed. Blockchain is a decentralized digital ledger that allows end-to-end communication and provides interaction between untrustworthy persons. BCCS uses a conventional in-depth approach and blockchain to create a procedure for collecting medical information from the IoMT and integrated devices. The proposed system utilizes blockchain to record and extract the accumulated information in a secure and distributed manner within a closed environment suitable for healthcare professionals, such as nursing homes, hospitals, and the healthcare industry where data exchange is needed. The experimental outcomes show that the proposed system has a high security rate of 99.8% and the lowest latency rate of 4.3% compared to traditional approaches. In all, the reliability of the proposed system gives the highest rate of 99.4%. [ABSTRACT FROM AUTHOR]

The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT. [ABSTRACT FROM AUTHOR]

Road accidents occur everywhere in the world and the numbers of people dead or injured increase from time to time. People hope that emergency vehicles and medical staff will arrive as soon as possible at the scene of the accident. The development of recent technologies such as the Internet of Things (IoT) allows us to find solutions to ensure rapid movement by road in emergencies. Integrating the healthcare sector and smart vehicles, IoT ensures this objective. This integration gives rise to two paradigms: the Internet of Vehicles (IoV) and the Internet of Medical Things (IoMT), where smart devices collect medical data from patients and transmit them to medical staff in real time. These data are extremely sensitive and must be managed securely. This paper proposes a system design that brings together the three concepts of Blockchain technology (BC), IoMT and IoV to address the problem mentioned above. The designed system is composed of three main parts: a list of hospitals, patient electronic medical record (EMR) and a network of connected ambulances. It allows the road user in the case of an accident to report their position to the nearby health services and ambulances. [ABSTRACT FROM AUTHOR]

Currently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID‐19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource‐limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication [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]

Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have drawn significant attention over the last few years due to their significant contribution to every domain of life, but the major application of these devices has been witnessed in the healthcare sector. IoT devices have changed the complexion of healthcare set-up, however, the major limitation of such devices is susceptibility to many cyberattacks due to the use of embedded operating systems, the nature of communication, insufficient software updates, and the nature of backend resources. Similarly, they transfer a huge amount of sensitive data via sensors and actuators. Therefore, the security of Internet of Health Things (IoHT) devices remains a prime concern as these devices are prone to various cyberattacks, which can lead to compromising and violating the security of IoT devices. Therefore, IoT devices need to be authenticated before they join the network or communicate within a network, and the applied method of authentication must be robust and reliable. This authentication method has to be evaluated before being implemented for the authentication of IoT devices/equipment in a healthcare environment. In this study, an evaluation framework is introduced to provide a reliable and secure authentication mechanism based on authentication features. The proposed framework evaluates and selects the most appropriate authentication scheme/method based on evaluating authentication features using a hybrid multicriteria decision-making approach. It completes this in two steps: in the first step, the analytic hierarchy process (AHP) method is applied for assigning criteria weights; and in the second step, the technique for order preference by similarity to ideal solution (TOPSIS) approach selects the best authentication solution for IoHT devices based upon identified authentication features. This is the first attempt to present a features-based authentication model for selecting the improved authentication solution employed in IoHT devices. [ABSTRACT FROM AUTHOR]

Health care visualization through Internet of Things (IoT) over wireless sensor network (WSN) becomes a current research attention due to medical sensor evolution of devices. The digital technology-based communication system is widely used in all application. Internet of medical thing (IoMT) assisted healthcare application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation. These smart medical devices may consume with limited resources and also the data generated by these devices are large in size. These IoMT based applications suffer from the issues such as security, anonymity, privacy, and interoperability. To overcome these issues, data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption. This article proposed a well-organized data aggregation and secured transmission approach. The data generated by the sensor are collected and compressed. Aggregator nodes (AN) received the compressed data and concatenate it. The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptography-based encryption with Message Authentication code (MAC). Fog node extracts the forwarded data from AN using Fog message extractor method (FME) with decryption. The proposed system ensures data integrity, security and also protects from security threats. This proposed model is simulated in Network Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security, privacy and also reduces the communication cost. Fog node usages in between Aggregator and base station, will reduce the cloud server/base station computational overhead and storage cost. The proposed ideology is compared with existing data aggregation schemes in terms of computational cost, storage cost, communication cost and energy cost. Cost of communication takes 18.7 ms which is much lesser than existing schemes. [ABSTRACT FROM AUTHOR]

Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods. [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]

The Internet of Medical Things (IoMT) is a growing trend that has led to the use of connected devices, known as the Internet of Health. The healthcare domain has been a target of cyberattacks, especially with a large number of IoMT devices connected to hospital networks. This factor could allow attackers to access patients' personal health information (PHI). This research paper proposes Chidroid, an innovative mobile Android application that can retrieve, collect, and distribute logs from smart healthcare devices. The proposed approach enables the creation of datasets, allowing non-structured data to be parsed into semi-structured or structured data that can be used for machine learning and deep learning, and the proposed approach can serve as a universal policy-based tool to examine and analyse security issues in most recent Android versions by distributing logs for analysis. The validation tests demonstrated that the application could retrieve logs and system metrics from various assets and devices in an efficient manner. The collected logs can provide visibility into the device's activities and help to detect and mitigate potential security risks. This research introduces a way to perform a security analysis on Android devices that uses minimal system resources and reduces battery consumption by pushing the analysis stage to the edge. [ABSTRACT FROM AUTHOR]

The use of avatars in the Metaverse and virtual reality technology in education is a rapidly developing field that has the potential to revolutionize the way students learn. In the context of the Indian EdTech market, virtual reality could play a significant role in enhancing the learning experience for students, although more research is needed to explore its full potential.ICT has already made a significant impact on education, with academic programming, technical instruction, and theoretical concepts being taught through digital platforms. Virtual reality has the potential to take this a step further, allowing students to experience immersive and interactive learning environments that simulate real-world scenarios.However, as mentioned in the paragraph, there is still limited research on the use of virtual reality in education, particularly in the Indian context. Further studies are needed to assess the effectiveness of this technology and ensure that it can be integrated effectively into the educational system.Overall, the use of virtual reality in education is an exciting development in the ICT industry and has the potential to make a significant impact on the learning experience for students. Various non-governmental organizations (NGOs) seek to improve women's lives and provide them with self-sufficiency training. The government is likewise concerned about this issue and has established a number of programs to assist women. Information and communication technology (ICT) is significant in this context. ICT has improved the security, knowledge, education, employability, confidence, and popularity of women. The significance of ICT for the empowerment of women is highlighted in this study. To illuminate the various facets of ICT's impact on society, various examples have been addressed. Because of ICT, new generations of women all over the globe have defied all expectations and proven themselves in every aspect of life, even the most complicated and time-consuming realm of enterprise. In this regard, ICT may possibly become a magic wand for improving women's current situations. In this research, the eight factors that influence how ICT affects rural women's empowerment were found.

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.

The Internet of Medical Things (IoMT) has become a strategic priority for future e-healthcare because of its ability to improve patient care and its scope of providing more reliable clinical data, increasing efficiency, and reducing costs. It is no wonder that many healthcare institutions nowadays like to harness the benefits offered by the IoMT. In fact, it is an infrastructure with connected medical devices, software applications, and care systems and services. However, the accelerated adoption of connected devices also has a serious side effect: it obscures the broader need to meet the requirements of standard security for modern converged environments (even beyond connected medical devices). Adding up different types and numbers of devices risks creating significant security vulnerabilities. In this paper, we have undertaken a study of various security techniques dedicated to this environment during recent years. This study enables us to classify these techniques and to characterize them in order to benefit from their positive aspects.

Due to advancement in internet of medical things, the conventional health-care systems are transformed into smart health-care systems. The medical emergence services can be significantly enhanced by integration of IoMT and data analytic techniques. These technologies also examine the unexplored area of medical services that are still unseen and provide opportunity for investigation. Moreover, the concept of smart cities is not achievable without providing a smart connected healthcare scheme. Hence, the main purpose of this research is to come up with a smart healthcare system based on IoMT, Cloud and Fog computing and intelligent data analytic technique. The major objective of the proposed healthcare system is to develop a diagnostic model capable for earlier treatment of heart disease. The suggested scheme consists of distinct phases such as data acquisition, feature extraction, FogBus based edge/fog computing environment, classification, and evaluation. In data acquisition, different IoMT such as wearables and sensors devices are considered to acquire the data related to heart disease and the various features related to signal and data are extracted. Further, the deep forest technique is integrated into the proposed system for classification task and effective diagnosis capabilities of heart issues. The performance of the suggested scheme is evaluated through set of well-defined parameters. Comparison with other healthcare model was conducted for the purpose of performance evaluation. It is concluded that the proposed model has a superiority over other all other models in different aspects namely, the sensitivity measure, accuracy measure, and specificity.