Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient’s health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reducse the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.

Mobile computing and technology are becoming more common in many parts of private life and public services, and they are playing an increasingly important role in healthcare, not just for sensory devices but also for communication, recording, and display. They are used for more than only sensory devices but also for communications, recording, and display. Numerous medical indications and postoperative days must be monitored carefully. As a result, the most recent development in Internet of Things‐ (IoT‐) based healthcare communication has been embraced. The Internet of Things (IoT), which is employed in a wide range of applications, is a catalyst for the healthcare industry. Healthcare data is complicated, making it difficult to handle and evaluate in order to derive useful information for decision‐making. On the other hand, data security is a vital requirement in a healthcare data systems. Determining the need for a smart and secure IoT platform for healthcare applications, we create one in this study. Here, a cutting‐edge encryption algorithm is used to protect the health data. Normalization is first used to preprocess the data and remove any irrelevant information. Using principal component analysis and logistic regression, the data’s features are extracted (LR‐PCA). To choose the pertinent features, a feature selection process based on genetic algorithms is used. We have put out a brand‐new kernel homomorphism. To increase the security of the IoT network, use the two‐fish Encryption algorithm (KHTEA). EBSMO (exponential Boolean spider monkey optimization) is used to further boost the encryption process’ effectiveness. Utilizing the MATLAB simulation tool, the proposed system is assessed, and the metrics are contrasted with the accepted practices. Our suggested solution has been shown to be effective in protecting medical healthcare data. The effectiveness of the proposed and existing approaches is assessed using metrics for encryption time, execution time, and security level. The security precautions we suggested for healthcare data worked well.

This chapter explores the transformative role of Artificial Intelligence (AI) in enhancing the design, implementation, and management of Blue-Green Infrastructure (BGI), a sustainable urban planning approach that integrates natural and engineered systems to address environmental challenges. The convergence of AI with BGI offers unprecedented opportunities to improve urban resilience, optimize resource management, and mitigate the impacts of climate change. Through advanced data analytics, predictive modeling, and real-time monitoring, AI-driven solutions can enhance the efficiency and effectiveness of BGI projects. This chapter delves into the various AI applications in BGI, including smart water management, flood prediction and prevention, urban heat island mitigation, and biodiversity conservation. Case studies and examples from global cities illustrate how AI is being leveraged to create more adaptive, sustainable, and resilient urban environments. The chapter also discusses the challenges and ethical considerations associated with the integration of AI in BGI, emphasizing the need for interdisciplinary collaboration and responsible AI deployment to ensure equitable and long-term benefits.

The rise of the metaverse, a virtual shared space where users interact collectively, has generated significant interest in examining the dynamics of human-machine interaction within this digital environment. This study presents an extensive overview of current research on human-machine interaction (HMI) in the metaverse, encompassing a variety of technologies, methodologies, and consequences. By drawing upon a diverse array of academic references, this study delves into the fundamental aspects of HMI in the metaverse, such as gesture and motion tracking, voice recognition, virtual and augmented reality interfaces, haptic feedback, brain-computer interfaces, avatars, personalization, and collaborative engagement. Furthermore, this study addresses the obstacles and possibilities linked to HMI in the metaverse and provides a framework for swift integration of HMI in the metaverse.