Internet of Multimedia Things (IoMT)

Text classification is one of the severe challenges for categorizing large and high-dimensional text data accurately and efficiently. Many features confuse the classification process, and feature selection (FS) strategies should be used to deal with the problem of high dimensionality. This paper proposes a novel FS technique based on enhanced category-feature association measure (ECFAM). ECFAM utilizes the existence and elimination of terms and the complicated relationships among the terms across different sections. This one-of-a-kind approach emphasizes the key role of ancillary terms in classifying and differentiating categories. The comparison is done on two important datasets, Reuters-21578 and 20-Newsgroups, through two widely employed supervised machine learning classifiers and one deep learning algorithm. Throughout our experiments, we investigate the feature sizes in nine different feature sets, ranging from 50 to 4000. Experimental data show that ECFAM always performs better than other methods concerning accuracy and computational cost.

This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address...

Surgical Internet of Medical Things (IoMT) applications impose stringent Quality of Service (QoS) demands, requiring ultra-low latency and high reliability for critical data streams like control signals or high-definition video. Conventional resource orchestration methods often fall short, failing to guarantee performance for high-priority surgical tasks, particularly under dynamic network conditions, including varied wireless channel quality, and potential congestion. This paper introduces the Priority-Aware SDN Orchestration (PASO) framework, leveraging Software-Defined Networking (SDN) for centralized network visibility and control to address these critical challenges. PASO implements a novel joint optimization strategy focused primarily on maximizing the completion rate of critical surgical tasks that adhere strictly to their maximum delay constraints. A key distinguishing feature is its dynamic resource reallocation mechanism, which employs the preemption of lower-priority, non-medical traffic to proactively secure network resources and guarantee QoS for vital surgical flows, even during periods of high network load. A computationally efficient, priority-aware greedy algorithm operationalizes this strategy on the SDN controller. Comparative simulations against Random, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and a Deep Reinforcement Learning (DRL) agent as baselines demonstrate PASO’s substantial effectiveness. It significantly reduces end-to-end delay (e.g., up to 70% less than PSO under high load) and drastically improves the completion rate for critical surgical tasks (over 80% higher than PSO under high load), while also promoting balanced network utilization. PASO offers a robust solution for dependable, low-latency surgical IoMT operations through its targeted optimization and preemptive resource management capabilities.

The field of computer science known as machine learning explores algorithms that learn from examples. Classification requires machine learning algorithms that understand how to apply a class label to data from the problem area. An easy-to-understand example is categorizing emails as "spam" or "not spam." Binary classification predicts one of two classes, whereas multi-class classification predicts one of several classes. In most binary classification tasks, there is one type of normal state and another type of aberrant state. This Project involves how lung disease prediction using x-ray images will predict through the binary classification model implemented, and various python libraries like Tensor Flow, Keras, NumPy, etc. are used. This research project will observe the prediction of lung diseases by using x-ray images and further the output will be predicted with the detailed example and a detailed source code. The implementation will be shown step by step with possible screenshots.

The healthcare industry has been affected by the rise in technological innovation, and following COVID-19, it is easy to see how adopting contemporary technology shapes the relationship between caregivers and their patients. The paper describes how the modern healthcare system is defined by phrases such as Cyber-Physical System(CPS), Internet of Things(IoT), Electronic Medical Record(EMR), big data, and Internet of Medical Things(IoMT). The conclusions also describe the difficulties that may arise through or following the execution of knowledge-based systems(KBS) in the health care industry. We have closely followed the schedule for the documentation created in the last few years, primarily from 2023 to 2017. The selection of the papers is based on how modern technologies are used to create a brighter future, as well as the technologies that are employed and the difficulties encountered in putting them into practice. Articles on the subjects we have chosen are reviewed using the Scopus database. The changing healthcare system must overcome numerous obstacles as it enters the internet and big data world. Connecting, learning, and implementing the knowledge-based system for precise decision-making are some of these issues. It was observed that using new technologies and giving stakeholders modest incentives can help overcome obstacles. Although electronic medical records, or EMRs, are helpful in improving caretaker treatment, they also generate a lot of data, which raises concerns about handling, storage, and privacy. Technology advancements like 5G will open the door to a more sophisticated healthcare system that may be available to everyone, relieving pressure on the healthcare system, caregivers, and caregivers. These technology advancement support increase in quality of patient life and reduction in cost when compared to traditional healthcare system, also enabling the continuous monitoring of the patient health record using the IoMT and wearable devices.

Smart city" is a place where cutting-edge technology optimizes resource utilization, progress facilitation, environmental friendliness, and socially inclusive neighborhoods are established. Technology, economics, and social progress were discussed. Since 2000, political actors, business, administration, and urban planning have utilized the notion to define urban technological advancements and developments. The smart city is a planned, technologically advanced "city". In the face of economic, social, and political defeats in many nations after 2000, it uses all digital technology. Air pollution, overcrowding, and urban population growth are the main concerns. Along with technical advancements that make cities more habitable, financial constraints constantly present a challenge. Existing infrastructural inefficiency. Machine learning technology is essential to the IoT because it manages large amounts of data. This paper will explain how healthcare uses AI with IoT and WSN. This research underpins smart city Internet of Things duties and examines the IoT's impact on health facilities.

Background: Cloud computing (CC) and artificial intelligence (AI) are causing a rapid evolution in healthcare, meeting the requirement for accurate and effective disease diagnosis and management through wearable IoT devices and sophisticated algorithms. Objective: To develop a BBO-FLC and ABC-ANFIS system that works together for better disease prediction accuracy and real-time monitoring. Methods: Implemented on a scalable cloud architecture, the system combines IoT-enabled sensors for data gathering, ABC for feature optimization, BBO for fuzzy rule refining, and ANFIS for disease categorization. Results: The suggested solution outperformed conventional techniques with 96% accuracy, 98% sensitivity, and 95% specificity at a 60-second computation time reduction. Conclusion: The precision, scalability, and real-time healthcare applications for complicated disease prediction and monitoring could be greatly improved by this integrated system.

Using a convolutional neural network (CNN) in conjunction with Score-CAM for visual
explanations and integrated into Internet of Medical Things (IoMT) systems, the project aims to improve the explainability and accuracy of skin lesion identification. Through the use of methods such as Canny Edge Detection for border localisation and DF-U-Net for segmentation, the model allows for more accurate diagnosis. The model achieves higher accuracy and is highly effective for real-time skin lesion detection and diagnosis due to the incorporation of clinical metadata, which also improves interpretability.

This paper introduces a novel approach for predicting chronic kidney disease (CKD) by integrating advanced technologies, including the Internet of Medical Things (IoMT), robotic automation, and artificial intelligence (AI). The proposed system leverages Recurrent Neural Networks (RNN) to analyze temporal patterns in patient data and employs Type-2 fuzzy logic to handle uncertainties and imprecision in medical data. By incorporating robotic automation, the method enhances data processing efficiency, reducing human error and increasing productivity. The strategy enables real-time monitoring and provides individualized healthcare interventions, offering significant improvements in CKD prediction accuracy and effectiveness. Compared to traditional methods, this approach demonstrates substantial benefits, achieving 94.5% accuracy, 92.3% precision, and a rapid computing time of just 3.2 seconds. The proposed methodology addresses common challenges in medical diagnostics, such as data inconsistency, and holds the potential to transform CKD management by facilitating early detection and personalized treatment strategies.

Chronic Kidney Disease (CKD) is one of the most serious public health issues in global. This research work proposes a Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Neuro-Fuzzy Systems based hybrid model to generate real-time prediction from Internet of Medical Things (IoMT) data for CKD forecasting. Optimizing feature selection with the Aquila Optimization Algorithm (AOA) and using Edge AI for privacy and quick decisionmaking to increase accuracy up to 98.99%, which is compared with CKD management in resource limited settings beneficial. Objective: This paper aims to develop a real-time CKD early prediction model through the integration of CNN-LSTM and Neuro-Fuzzy using IoMT data, which provides high predictive accuracy in predicting CKD and classifying stages by ensuring privacy protection on the one hand and deployment in Edge AI environments with low latency. Methods: The model combines CNN for discovering spatial patterns, LSTM for spotting temporal dependencies and Neuro-Fuzzy systems for the classification of CKD stages. Both dimensionality reduction through Principal Component Analysis (PCA) and clustering with the use of DBSCAN

The appraisal of e-health care system adoption with the in-depth grounded knowledge of the internet of medical things infrastructure, established influential impact role toward cloud computing on the (IoMT)in Federal Teaching Hospital, Nigeria, is the focus of this research. In writing this project, the researcher has raised the need for understanding cloud computing in the public health care system and to get information about what the government is doing by bringing information to their doorstep. A further attempt has been made to examine the vibrancy of the adoption, such as IoMT in tackling the problem facing the public health care sector, providing a scheme to eradicate the challenges, making it viable for the commodity that everyone can buy, including the stakeholder which adopts the use of the new shift. However, the research work will critically examine the content of the E-health system with the adoption of cloud computing, helping the IoMT to achieve the result, as it opines that it will serve as reference material for future researchers.

The Internet of Things (IoT) improves our lives by facilitating real-time communication between people and objects. Predictive analytics could turn a reactive approach into a proactive one with the rise of artificial intelligence (AI) and machine learning in the healthcare sector. As a branch of machine learning, deep learning is able to deal with large amounts of data quickly, produce valuable insights, and solves complex problems. Accurate and timely diagnosis of diseases is essential for disease prevention and early treatment. The widespread adoption of electronic medical records necessitates the development of prediction models that are more accurate to effectively harness recurrent neural network variants of deep learning. This study investigates the integration of the Internet of Things and deep learning for disease prediction and diagnosis, which highlights key advancements in data collection, preprocessing techniques, and feature extraction strategies. The potentiality of convolutional and recurrent neural networks in increasing the accuracy of diagnosis and allowing early detection of diseases is analyzed. This review shows how IoT combined with deep learning can develop an investigative disruption in the prediction and diagnosis of diseases by expanding their validity, speed, and possibility of early detection thus opening the door to new applications and effective research.

Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signalto-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.

In Iraq, the fragmented systems within the healthcare sector struggle to protect patient-sensitive data, maintain data integrity, and enable interoperability, as highlighted. This review will focus on how blockchain technology can overcome these earlier challenges by improving the security and efficiency of information systems. Health Information Systems (HIS) are challenged with data breaches, cyber-attacks, and an interoperability gap in their healthcare information infrastructure due to blockchain decentralized, immutable transparency qualities. It creates a safe place for medical records storage and operation that can improve data integrity, security, privacy, and ease of use, eventually decreasing administrative inefficiency. This paper discusses the current situation of HIS in Iraq, which is faced with a lack of resources, poor standardization, and little technology uptake due to resistance. It analyses blockchain-enabled solutions to secure e-health records (EHR) and health information exchange HIE or clinical decision support systems (CDSS) to improve health delivery. Primary interventions implemented at the community level (e.g., identified key strengths of blockchain, including data security, patient outcomes, and process streamlining). The model of the blockchain-based EHR system in the Iraqi healthcare system is proposed and demonstrated, thus highlighting the need for technical development, regulatory basis, and stakeholder collaboration. We will discuss some challenges like scaling, regulatory burden, and technical specialty. The paper comes to an end by suggesting pragmatic solutions, which are implementation-phased, capability-building, and embracing standards. With blockchain, Iraqi medical establishments will enhance the quality of care by creating an encrypted, patient-centered, and efficient health system that secures information on sensitive medical matters. This review outlines the transformative capability of blockchain in Iraq's healthcare challenges and suggests a pathway towards practical usage.

signed tiger hashing algorithm (BS-THA) | Diasph principle component analysis (DPCA) | electrocardiogram (ECG) | internet of medical things (IoMT) | median filter (MF) | orthogonal Aranda convolutional neural network (OA-CNN) | phonocardiogram (PCG) | pooled variance empirical mode decomposition (PV-EMD

Background: Disease detection has grown increasingly effective with the quick development of artificial intelligence (AI) and cloud computing (CC), particularly through the real-time processing of large amounts of intricate medical data from Internet of Things (IoT) devices. Accurate and fast disease diagnosis is limited by traditional approaches' difficulties with handling high-dimensional data. Objective: Utilizing the advantages of fuzzy logic and evolutionary optimization, this study attempts establishing a hybrid model that combines the Fuzzy Aggregation Convolutional Neural Network (FA-CNN) and Differential Evolutionary-Extreme Learning Machine (DE-ELM) to improve disease detection accuracy, sensitivity, and computational efficiency in healthcare. Methods: In order to maximize classification accuracy, the suggested model combines DE-ELM with FA-CNN for processing ambiguous healthcare data. The system is more resilient to noisy IoT data if data preprocessing is used, such as feature extraction and normalization. Analyzed and contrasted with conventional techniques are performance parameters such computation time, sensitivity, specificity, and accuracy. Results: FA-CNN + DE-ELM outperformed current models by achieving superior outcomes with a computation time of 65 seconds, accuracy of 95%, sensitivity of 98%, and specificity of 95%. High efficacy in early disease identification and real-time healthcare monitoring is demonstrated by this hybrid technique. Conclusion: A reliable approach to disease identification that maximizes data processing and diagnostic precision is provided by the FA-CNN + DE-ELM hybrid model. The model is positioned as a viable tool for proactive, real-time healthcare diagnostics by combining fuzzy logic with evolutionary algorithms, that improves handling of inaccurate medical data.

This research offers a solution for AI in IoT-based health care systems that can help large scale implementation of AI-based cloud platforms for real-time data processing and analysis. The IoT sensors with the cloud-enabled environment supported by the CNNs and LSTM networks for health condition monitoring allows real-time management of patient health status. Based on the experimental context, the system was able to prove that the system can actually filter large data stream and work even through the fluctuating network environment. Specifically, the performance targets such as the time taken to process the data, AI output accuracy, the ability of the system to scale and the robustness of the system were assessed. The results demonstrate that the proposed solution has accuracy of up to 96% in health predictions and is easily scalable in terms of the number of IoT devices involved. Network latency was also well controlled, whereby the lowest network latency for networks was measured in 5G networks. Such conclusions prove that the application of cloud-based AI systems can enhance the conditions of the healthcare provisioning, in terms of timely identification of patients with certain health conditions.

The Internet of Medical Things (IoMT) offers real-time data insights, reduces energy costs, and enhances patient comfort, presenting transformative potential for healthcare systems. While existing research on IoMT in healthcare has primarily concentrated on data security concerns and corresponding solutions, it has often overlooked the broader role of IoMT technologies across diverse healthcare systems and the unique challenges of implementing them. This study aims to bridge these gaps by investigating how IoMT impacts healthcare systems, focusing on its technological roles, benefits, and challenges. The significance of this work lies in its comprehensive exploration of IoMT applications and their potential to transform healthcare delivery through personalized treatment, diagnostics, and enhanced quality of care. The findings indicate that combining IoMT with machine learning (ML) can achieve up to 99.84 % accuracy in predicting heart disease from medical images, while remote monitoring for elderly patients reaches an accuracy of 98.1 %. Additionally, a model utilizing edge-IoMT computations demonstrates a promising solution for real-time seizure detection. By facilitating continuous data collection and providing real-time insights, IoMT significantly enhances the operational effectiveness of ML algorithms, ultimately leading to improved health outcomes for these vulnerable populations. However, IoMT integration into smart healthcare systems raises security concerns, which can be mitigated by using strong encryption, authentication, and security updates, following privacy regulations, and educating healthcare professionals about cybersecurity. Future IoMT research must prioritize the implementation of artificial intelligence algorithms to improve the management of security and vulnerabilities in intelligent healthcare systems.

The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.

Among  the  different  tools  and  techniques  to  detect,  monitor  and analyze  the  behavior  of  cybercriminals  in  a  controlled environment is the use of Honeypots. This computer security technique consists of creating a trap or decoy that pretends to be a valuabletarget for cybercriminals, such as, for example, important data, applications or web servers, among others. Once the attackerenters this environment,  it records  all  the  activities  and behaviors  performed by  them,  this  in turn,  allows  the  detection  and  proactive  response  to future  attacks. Likewise,  they  allow  to  reduce  false  positives  and  identify vulnerabilities  of  the  systems.  On  the  other  hand,  the implementation of these tools implies a potential risk, because they can be used by attackers to obtain information about the defenses of the institution or organization and, even more, if it is not installed correctly, leaving the system and sensitive data exposed. In this sense, there are many high and low interaction Honeypots, such as, for example, Honeyd, Dioneda, Capture-HPC, KFSensor, INetSim. Cowrie, Honeytrap, Amun, Glastopf, Contop, among others. Finally, T-Pot Honeypot is considered as a set of computers (All in one) containing both high and low interaction Honeypots, with a unique ability to customize and manage honeypots, that is, it is a powerful and effective tool in the fight against attackers and computer security threats. In addition, it allows the obtaining of behavioral data ofcybercriminals to later determine patterns that allow improving Web Security

The mobile healthcare (mHealthcare) paradigm provides promise to enhance the delivery of healthcare services through remote diagnostics and medical data interchange. But problems like unapproved access and data leaks continue. Although there are still problems, attribute-based encryption (ABE) is a useful cryptographic method for protecting data exchange in mHealthcare. To address these problems, this study provides an effective data sharing strategy that also protects privacy. The mHealthcare service users activity has been monitored for malicious analysis in which the authentication of the users is analysed using convolutional reinforcement fuzzy neural network. the trained and classified output gives security analysis based on healthcare data modelling then the network privacy is enhanced. In order to protect user privacy, it hides a portion of the access policy, adds an offline method for generating keys and encrypting data in mHealthcare, and uses blockchain technology to provide decentralized, reliable verification of data access rights. The enhanced security and efficiency of the method are confirmed by security proofs and experimental outcomes. Proposed technique attained detection accuracy 96%, data privacy analysis of 94%, recall of 90%, RMSE of 60% based on mHealthcare dataset analysis.

This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.

The manual interpretation of medical images in healthcare facilities often delays patient diagnosis. This process is characterized by slow, time-consuming, costly, and laborintensive procedures. Pneumonia, a severe respiratory infection affecting the lungs, poses a significant threat, necessitating prompt detection and treatment to prevent fatalities. This study introduces an automated method utilizing deep learning to diagnose pneumonia. An approach employing a simplified CNN architecture has been devised, requiring less computational power. This approach can be particularly beneficial for healthcare facilities with limited resources. Experimentation conducted on a public image dataset yielded a model accuracy of 0.95. The study's findings demonstrate the proposed technique's efficacy, surpassing existing CNN models' accuracy and computational efficiency. This paper underscores the significance of deep learning in diagnosing and ensuring timely treatment for pneumonia.

Abstract: This research focuses on the analysis of
applying enhanced forms of artificial intelligence in
improving management information system in
healthcare. By analyzing various methods of machine
learning, deep learning, NLP, and IoT analytics,
carrying out their analysis, as well as their practical
application, the given work sheds light on their
revolutionary role in health care. Some of the identified
works involved the use of Random Forests and
Convolutional Neural Networks Clinicians, achieving
prediction accuracy of 85 % and 92 % respectively in two
clinical decision support and Medical image analysis
tasks. In this study, to analyze entity recognition, the
NER process was supported by CRF and achieved an 88
% F1-score for identifying medical entities in textual
data, increasing the speed of clinical documentation.
Moreover, IoT based anomaly detection systems
obtained a 95% of the detection rate contributing to the
improvement of real time observation and urgent
response into the healthcare environments.

Keywords: Artificial Intelligence, Healthcare
Management

Twenty-four percent of all sectors’ energy use is in the residential sector. Intelligent sensors and smart appliances have sped up the development of energy management systems for private residences. Finding a happy medium between user convenience and energy efficiency is the goal. When calculating the efficiency of a smart house, heating, ventilation, and air conditioning (HVAC) systems may account for as much as 65% of total energy consumption. Research has shown that reducing energy usage is the major objective of combining a fuzzy logic system with other tactics. However, these strategies almost always compromise user experience. In this research, propose a fuzzy inference system (FIS) that takes humidity into account as another input to ensure that the thermostat settings are always optimal for user comfort. The temperature changes within the house as input to the proposed FIS in an effort to reduce energy use. As the number of laws increases, so does the time spent drafting new rules in FIS, which in turn increases the likelihood of human mistake. A combinatorial method is recommended for automatic rule generation. The plans are assessed using Surgeons FIS and Mamdani FIS. The sophisticated FIS system requires a small storage space and processing power, which is well-suited for usage with an Internet of Things (IoT) operating method. The suggested method reduces energy usage by 25%.

Medical healthcare units and diagnostics devices are designed with standard modules installation and almost every sensory device-working mode is similar with differences in mode of data fetching. However, even though the working operations are standard researchers are developing different devices for different diagnostics. Typically, sensors are false in digital or analogue modes only hence developing a generic device supporting multiple types of sensors is feasible and acceptable. For areas with limitations of resources, a multiple functional single device is in high demand. Major issues with such a device development are software and sensor value calibration or calculation as per the requirement. Body statistics include multiple parameters where some are direct values calculated and detected by the sensor itself in real-time, and few parameters are calculated based on the standard procedure of averaging concerning time or the differential value between different processes or values. The proposed system is designed to overcome this problem and implement a single device with multiple functional algorithms execution as and when required and demand based. The research study presented the method to handle different processes on demand and stated the multiple procedures of operations in healthcare devices.

With rapid development and growth in connected world technologies like IoT or the edge computing medical industries and support system gaining greater advantages through tele-healthcare and remote healthcare monitoring system. Even after the connected world infrastructure like Wi-Fi, mobile technology and even the satellite communication still the real-field deployment of these technologies are at very lower rate. Deployment of any medical grade technology or the products needs multiple level of testing, certifications and the finally the user acceptance of the devices. Data transfer to the concern entity with guaranteed delivery is the major concern for tele-healthcare implementors. Keeping the patient treatment and diagnosis data privacy concerns, even the medical sensor devices are in control of manufacturers its data access needs to keep under the radar and safe from different level of users of the data and the device statistics. This paper study focuses on understanding and finding out the multiple layers of testing and the deployment for connectivity for tele-healthcare system. This study also covers reaching the highest depth of data captured for analysis with its multiple possible combinational reporting and result generations. With proposed solution study conclude the study result and the solution to the real-world problem in tele-healthcare system.

Developing wireless technology and the exploitation of automated systems has demanded an innovative method of handling the objects that share multimedia information. The monitoring and automation system has progressed towards the Multimedia Internet of Things (M-IoT) whereas circumstance is entirely transformed to meet the requirements of the fast-moving world. Internet of Things (IoT) engrosses abundant objects and makes those objects think intelligently, which decides on their own by perceiving the situation of the circumstance. The incorporation of IoT into the real-time application facilitates the information exchange and utilizes the information for monitoring as well as prevention of complicated situations. The M-IoT-based conceptual framework and integration of smart objects in real-time applications have enriched the standard of living. M-IoT is widely used in health care, industry, and monitoring event. In this article, the specialized architecture of M-IoT is discussed alo...

In this paper, we propose an agent-based architecture for remote collaboration support systems that enables the exchange of synchronous and asynchronous multimedia streams at remote sites using an Internet of Multimedia Things (IoMT) approach. First, we design and implement Internet of Things (IoT) applications that contain simple sensors and actuators. These applications are modularized into agentbased subsystems that can be incorporated into an IoT application with agent operations. Our aim is to develop remote collaboration support applications composed of video, data, and document channels. Because the applications will exchange enormous multimedia streams between remote sites, we propose a novel IoMT system architecture composed of several channel types that consist of various resource and network components. Users can dynamically incorporate these channels into applications to update the IoMT system's effects. Finally, we demonstrate and discuss the experimental results of our application to validate its ability to rapidly supply multimedia resources via multi-agent collaboration. Adding to its novelty, the developed system is in practical use for collaboration between France and Japan. INDEX TERMS Agent-based architecture, channel, Internet of multimedia things, multimedia resource, remote collaboration support.

Cloud computing aims to provide reliable, customized, and quality of service (QoS) guaranteed dynamic computing environments for end-users. However, there are applications such as e-health and emergency response monitoring that require quick response and low latency. Delays caused by transferring data over the cloud can seriously affect the performance and reliability of real-time applications. Before outsourcing e-health care data to the cloud, the user needs to perform encryption on these sensitive data to ensure its confidentiality. Conventionally, any modification to the user data requires encrypting the entire data and calculating the hash of the data from scratch. This data modification mechanism increases communication and computation costs over the cloud. The distributed environment of fog computing is used to overcome the limitations of cloud computing. This paper proposed a certificate-based incremental proxy re-encryption scheme (CB-PReS) for e-health data sharing in fog ...

The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delaysensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT. INDEX TERMS Multimedia Internet of Things (M-IoT), multimedia communication, Internet of Multimedia Things (IoMT), multimedia computing, Quality-of-Experience (QoE), Quality-of-Service (QoS), multimedia routing, medium access control (MAC).

The Healthcare sector is experiencing a change in thinking with the advent of Healthcare 5.0, bringing forth improved patient care and system efficiency. However, this transformation poses significant challenges. The growing digitization of healthcare systems raises concerns about the security and privacy of patient data, making seamless data sharing and collaboration increasingly complex tasks. Additionally, as the volume of healthcare data expands exponentially, efficient handling and analysis become vital for optimizing healthcare delivery and patient outcomes. Addressing these multifaceted issues is crucial for healthcare professionals, IT experts, data scientists, and researchers seeking to fully harness the potential of Healthcare 5.0. Federated Learning and AI for Healthcare 5.0 presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry; it dives into the principles of Healthcare 5.0 and explores practical implementation through cloud computing, data analytics, and federated learning. Readers will gain profound insights into the role of cloud computing in managing vast amounts of healthcare data, such as electronic health records and real-time analytics. Cloud-based frameworks, architectures, and relevant use cases are explored to optimize healthcare delivery and improve patient outcomes. Federated Learning and AI for Healthcare 5.0 encourages readers to take initiative and address the security and privacy concerns of cloud-based healthcare systems. It offers invaluable strategies, including security primitives, trust-based architectures, privacy models, and compliance standards, ensuring the protection of sensitive patient data while enabling secure data sharing and collaboration within the healthcare ecosystem. In-depth exploration of federated learning in healthcare empowers professionals with a comprehensive understanding of this distributed machine learning approach, preserving data privacy during analysis. Through practical case studies and simulations, readers gain actionable insights to implement federated learning models and frameworks, bringing tangible improvements to real-world healthcare 5.0 scenarios. The book explores emerging technologies like quantum computing, blockchain-based FL cloud services, and intelligent SaaS APIs, envisioning a future where these innovations redefine healthcare 5.0 and lead to groundbreaking advancements. Federated Learning and AI for Healthcare 5.0 serves as an indispensable resource, empowering healthcare professionals, IT experts, data scientists, and academicians to navigate the complexities of modern healthcare, leveraging innovative technologies to revolutionize patient care and system efficiency. With its comprehensive approach and practical insights, this book stands at the forefront of advancing Healthcare 5.0 into a more secure, efficient, and patient-centric era.

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

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, differ...

Public Safety is nowadays a priority, cornerstone and major concern for governments, majors and policy makers in current (and future) smart cities. Notwithstanding the foregoing, large advances in ICT technologies are foretold to revolutionize our society and enhance our feeling of safety (and hopefully, wellbeing). This chapter presents an introduction to three of the most promising technological pillars considered to be spearheads in this transformation: Internet of things, understood as the data capillarity through billions of sensors, Intelligent Video Analytics and Data Mining Intelligence, the latter two enabling smarter contextual awareness and prediction of potential threats leading to proactive prevention of them. The associated horizontal economic implications of this evolution and its impact into the societal and economic fabric are also tackled. Part of the results and analysis produced in this chapter are the outcome of the work carried out in the FP7 EU project SafeCity, one of the eight Use Cases of the FI Programme. The Safety Transformation in the Future Internet Domain 191 to reduce emergency response time and urban crime: for example, digital surveillance cameras have been placed in many critical areas and buildings throughout cities and call dispatchers have been created to distribute the emergency calls. Moreover, advanced technological capabilities facilitate urban public safety systems to become not just more interconnected and efficient, but also smarter and self-adaptive. Instead of merely responding to crimes and emergencies after a critical situation, novel smart systems emerge to analyse, anticipate and, actually, contribute to preventing them before occurring. After the terrorist attacks of March 2004 in Madrid, the city developed a new fully integrated Emergency Response Centre which, after an incoming emergency call, simultaneously alerts the required emergency agency (police, ambulance and/or fire brigade). The system can recognize if alerts relate to a single or multiple incidents, and assign the right resources based on the requirements coming from the ground. Furthermore, specialized video analytics systems are successfully installed for traffic surveillance purposes. These are CCTV-based systems capable of automatically detect illegal vehicles behaviour (e.g. cars stopped in forbidden areas, going in the opposite direction), restricted entries behaviour (e.g. bike entering in a forbidden road), stolen vehicles, etc. In addition, M2M communications, that is, intelligent communications by enabled devices without human intervention, are nowadays present in home and industrial security monitoring systems and alarms. Several Public Safety organizations and Public Administrations are using sensor networks to monitor environmental conditions or to be temporally deployed driven by an emergency situation. Other advanced technologies are focused on enhancing emergency notification mechanisms, fire and enforcement records management, surveillance, etc. As presented, outstanding capabilities offered by advanced technologies are currently in use for safety purposes. However, there is still a wide list of non-satisfied safety capabilities requested by Public Safety agencies. Several ongoing initiatives research upon how Future Internet can assist these entities in their daily work and during emergency response phases. That is the case of SafeCity (Future Internet Applied to Public Safety in Smart Cities) [1], an EU-funded project under the FP7 FI-PPP programme which proposes to enhance the role of Future Internet by developing smart Public Safety applications of high value. SafeCity aims at significantly improving the implementation and up-taking of Future Internet services in this safety field by 2015, leveraging the internet infrastructure as the bases of Public Safety centred open innovation schemes. It is focused in situational awareness (i.e. surveillance of public facilities, transport stations, energy facilities, roads, citizens in the streets; environmental monitoring), decision-making tools in C2 centres, seamless usage of ad-hoc communication networks temporarily deployed to support additional demand communication capacity (e.g. due to a major plan event) and alerting population mechanisms. This paper presents the state-of-the-art and on going advances in these three vital technological fields (Internet of things, Intelligent Video Analytics and Data Mining intelligence) that are envisaged as fundamental pillars of the FI infrastructure in the Public Safety domain. It further continues discussing and concluding on what the economic implications of such technological advances for Safety purposes are.

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength.

In recent years India is suffering from various natural disaster which have great effect on social life of people and large burden on disaster management authority. The high frequency disaster causes a serious loss of property, life and present more complex damage. Disaster management involves the detailed process of disaster response. A High-Performance Intelligent Disaster Management System can realize the complete disaster avoidance and reduction from satellite mission planning, data production, data acquisition, with the application of remote sensing and managing integrated rapid service. The main objective of proposed work is to overcome the limitations of disaster management with a novel design and development of IoT based platform for the application of disaster management system.

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementation AI-based cloud computing complexities, especially when we tackle real-time environments of AI technologies. In this paper, we propose a new mobile AI smart hospital platform architecture for stroke prediction and emergencies. In addition, this research is focused on developing and testing different modules of integrated AI software based on XAI architecture, this is for the ...

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniq...

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniq...

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

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.

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospital management systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating an attributebased encryption scheme. Furthermore, an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network. The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner. The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency. Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations. The average latency is 61 ms and 16 ms for read and write operations, respectively.

Cloud, edge, and fog computing enable Internet of Things (IoT) applications, offering high connectivity, scalability, and high availability. Smart cities, smart agriculture, and e-health systems are examples of IoT applications that can exploit the opportunities generated by these technologies. However, due to the scale and complexity of the IoT and the heterogeneity of the devices and service level expectations, resource management is not a trivial task. While facilitating storage and processing at the end device (the edge), at the intermediary layer (the fog), or centrally (the cloud), new points of failure are introduced at and between each layer. In some use cases, such as e-health, device availability also has high criticality. Any downtime impacting one or more components in the architecture can result in adverse effects and/or additional logistical effort and cost. This chapter discusses extant research on how cloud, fog, and edge computing is being used in smart city, smart ...