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This research focuses on the challenges of establishing a better medical system that can detect and diagnose diseases earlier. Using such cutting-edge health systems, healthcare practitioners may quickly and effectively manage patients’ medical issues by providing the appropriate data at the right time about the right people. The advancement of technology has increased the usefulness of devices that routinely analyse health measurements or monitoring time-sensitive health-related data. Medical professionals and patients alike are downloading health-related mobile apps to better track and manage their health. The research evidences how Internet of Things (IoT) technology may be used to support health care.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

This chapter proposes a new architecture to address challenges to security and the privacy in e-healthcare under Industry 5.0. With the growing needs for high-quality medical treatment and the continuously growing costs of treating global medical problems, systems and web-based medical care are regarded as innovative solutions. In particular, the new progress in Internet of Things (IoT) has led to the development of Internet of Medical Things (IoMT). The patient history data is handled and processed remotely in real-time rather than visiting any clinic and then having that data transferred for subsequent use to third parties, such as data that gets saved in the cloud. This patient data faced security threats and it is observed as major limitations of using such systems in Industry 5.0. This chapter analyzes the security and secrecy challenges, together with the necessities, the danger involved and proposed secured blockchain-based framework which is capable of future research scope in Industry 5.0. The study has described an Eye Hospital case study that stores the eye donors’ details. With such critical scenario, this study addresses healthcare scenario with poverty-led agenda and social developmental features.

The purpose of this paper is to conduct a dual-level (organizational and individual user) analysis of development related to the Internet of Medical Things (IoMT). It examines the organizational dynamics of IoMT and develops a conceptual model for quality of experience (QoE) in user acceptance.

This study uses the information systems success model of quality factor as an analytical framework and extends it beyond the individual user experience (UX) to include an organization-level perspective. Interviews are conducted with relevant stakeholders for sociotechnical inquiries; a survey identifies user factors in IoMT.

The sociotechnical analysis sheds light on how IoMT has been accepted and stabilized in the healthcare sector. It shows the complex interaction between the social and technical aspects of IoMT by highlighting the co-evolution, interaction, and interface of devices that constitute the next-generation network environment. The UX model conceptualizes QoE specific to medical informatics.

Given the sociotechnical nature of this investigation, another approach to adoption of IoMT innovations was worth investigating to determine effective integration.

IoMT needs to be meaningful if they are to be sustainable and they need to offer quality of services and QoE no matter the location or demographic in which they are used.

With a dual-level analysis, the study provides a comprehensive view of the IoMT development process by investigating the organizational dynamics, in addition to the UX, of IoMT. The results provide a basis for developing future IoMT services with QoE requirements, as well as for clarifying sociotechnical dynamics.

The aim of this paper is to identify some of the challenges that need to be addressed to accelerate the deployment and adoption of smart health technologies for ubiquitous healthcare access. The paper also explores how internet of things (IoT) and big data technologies can be combined with smart health to provide better healthcare solutions.

The authors reviewed the literature to identify the challenges which have slowed down the deployment and adoption of smart health.

The authors discussed how IoT and big data technologies can be integrated with smart health to address some of the challenges to improve health-care availability, access and costs.

The results of this paper will help health-care designers, professionals and researchers design better health-care information systems.

In line with the fast development of information technology, the Internet of Medical Robotic Things (IoMRT) is gaining more ground in health care. Sharing patients' information effectively and securely can improve sensing data usage and confidentiality. Nevertheless, current IoMRT data sharing schemes are lacking in terms of supporting efficient forward secrecy; when secret key for a robotic nurse as a data requester is compromised, all the historically shared data with this robotic nurse will be leaked.

The presented paper suggests an efficient puncturable attribute-based data sharing scheme enabling guaranteed firm security and versatile access control over health sensing data in IoMRT. This scheme integrates attribute-based and puncturable encryption to avail a shared secret key for data sharing that can be encrypted by an access structure over the Data Requester (DR) attributes. Additionally, the establishment of the shared key and the mutual authentication is simultaneously done between the cloud servers and DRs.

The proposed scheme can achieve forward secrecy by adopting the bloom filter technique that efficiently helps the updating of a private key with no need for the key distributor to reissue the key. The security proof illustrates that this scheme adheres to the security model. Besides, the performance evaluation expresses the feasibility of the suggested scheme.

The main goal of designing a puncture algorithm is to devise an updated key from the ciphertext and a secret key, allowing the decryption of all ciphertexts except the one that has been punctured on. This research illustrates the first effort to develop a puncturable attribute-based encryption scheme to achieve efficient finegrained data sharing in IoMRT.

A huge amount of diverse data is generated in the Internet of Things (IoT) because of heterogeneous devices like sensors, actuators, gateways and many more. Due to assorted nature of devices, interoperability remains a major challenge for IoT system developers. The purpose of this study is to use mapping techniques for converting relational database (RDB) to resource directory framework (RDF) for the development of ontology. Ontology helps in achieving semantic interoperability in application areas of IoT which results in shared/common understanding of the heterogeneous data generated by the diverse devices used in health-care domain.

To overcome the issue of semantic interoperability in healthcare domain, the authors developed an ontology for patients having cardio vascular diseases. Patients located at any place around the world can be diagnosed by Heart Experts located at another place by using this approach. This mechanism deals with the mapping of heterogeneous data into the RDF format in an integrated and interoperable manner. This approach is used to integrate the diverse data of heart patients needed for diagnosis with respect to cardio vascular diseases. This approach is also applicable in other fields where IoT is mostly used.

Experimental results showed that the RDF works better than the relational database for semantic interoperability in the IoT. This concept-based approach is better than key-based approach and reduces the computation time and storage of the data.

The proposed approach helps in overcoming the demerits of relational database like standardization, expressivity, provenance and supports SPARQL. Therefore, it helps to overcome the heterogeneity, thereby enabling the semantic interoperability in IoT.

Taking a business lens of telehealth, this article aims to review and provide a state-of-the-art overview of telehealth research.

This research conducts a systematic literature review using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) protocol and a collection of bibliometric analytical techniques (i.e. performance analysis, keyword co-occurrence, keyword clustering and content analysis).

Using performance analysis, this article unpacks the publication trend and the top contributing journals, authors, institutions and regions of telehealth research. Using keyword co-occurrence and keyword clustering, this article reveals 10 major themes underpinning the intellectual structure of telehealth research: design and development of personal health record systems, health information technology (HIT) for public health management, perceived service quality among mobile health (m-health) users, paradoxes of virtual care versus in-person visits, Internet of things (IoT) in healthcare, guidelines for e-health practices and services, telemonitoring of life-threatening diseases, change management strategy for telehealth adoption, knowledge management of innovations in telehealth and technology management of telemedicine services. The article proposes directions for future research that can enrich our understanding of telehealth services.

This article offers a seminal state-of-the-art overview of the performance and intellectual structure of telehealth research from a business perspective.

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.