As the Internet of Things (IoT) continues its rapid expansion, cloud computing has become integral to various smart healthcare applications. However, the proliferation of digital health services raises significant concerns regarding security and data privacy, making the protection of sensitive medical information paramount. To effectively tackle these challenges, it is crucial to establish resilient network infrastructure and data storage systems capable of defending against malicious entities and permitting access exclusively to authorized users. This requires the deployment of a robust authentication mechanism, wherein medical IoT devices, users (such as doctors or nurses), and servers undergo registration with a trusted authority. The process entails users retrieving data from the cloud server, while IoT devices collect patient data. Before granting access to data retrieval or storage, the cloud server verifies the authenticity of both the user and the IoT device, ensuring secure and authorized interactions within the system. With millions of interconnected smart medical IoT devices autonomously gathering and analyzing vital patient data, the importance of robust security measures becomes increasingly evident. Standard security protocols are fundamental in fortifying smart healthcare applications against potential threats. To confront these issues, this paper introduces a secure and resource-efficient cloud-enabled authentication mechanism. Through empirical analysis, it is demonstrated that our authentication mechanism effectively reduces computational and communication overheads, thereby improving overall system efficiency. Furthermore, both informal and formal analyses affirm the mechanism's resilience against potential cyberattacks, highlighting its effectiveness in safeguarding smart healthcare applications.

The Internet of Things (IoT) revolutionizes connectivity, as IoT devices grow exponentially, vulnerabilities emerge, ranging from data breaches to device hijacking. Thus there is the critical need for robust security measures, including encryption protocols and authentication mechanisms, to safeguard against cyber threats. Despite advancements in the authentication mechanism, still IoT security remains an ongoing concern. Because most of the authentications are vulnerable to diverse security attacks and other resource intensive. Thus protecting user data in this increasingly interconnected IoT-enabled world requires a secure and lightweight authentication mechanism. In this direction, in this paper, we propose a physically secure authentication framework for the IoT (PSAF-IoT). PSAF-IoT utilizes a combination of physical unclonable functions, secure hash algorithm, and elliptic curve cryptography to establish robust security measures. It guarantees the creation of a secure channel (session key) following user authentication at the gateway node, allowing the user to use the established secure channel for future communication. The secure channel establishment procedure is validated for security by employing formal methods such as the random oracle model and Scyther-based simulations. Additionally, PSAF-IoT undergoes informal validation to demonstrate resilience against node capture, replay attacks, impersonation, and other common security threats. Notably, PSAF-IoT demonstrates efficiency in terms of execution time, energy consumption, and communication costs, as evidenced by comparative analyses with related authentication frameworks, all while enhancing information security functionalities.

New applications like activity tracking and healthcare monitoring depend on secure and timely data transfer using wearable sensors. This work aims to reduce data loss and latency when transmitting wearable sensor information to analysis terminals by introducing an innovative probabilistic transfer learning approach. Using dynamic transmission slot allocation based on risk thresholds and time sensitivity, the suggested method intelligently arranges and prioritizes the transfer of aggregate sensor data across various sources. High-risk data is given preference when allocating slots in a two-step algorithm that divides the data into emergency and normal classifications to guarantee timely delivery without undue delays. Over time, for better activity identification, the transfer model of learning steadily learns and improves the slot assignment accuracy based on feedback. Comprehensive analysis of various queuing scenarios and transmission disruptions shows notable improvements over existing approaches, with waiting times, data loss, and transmission delays reduced by up to 10.49%, 2.42%, and 13.86%, respectively. Most importantly, 3.28% more accuracy is achieved in identifying distinct activities from the supplied wearable sensor data. This can be achieved by the dependable data supply by the probabilistic modelling approach. With its comprehensive architecture for efficiently managing limited communication resources, the suggested approach can provide real-time health surveillance, smart environment services, and other digital-physical systems requiring trustworthy data streaming. More interaction with statistical engines, improvements to security and privacy, and scalability validation on larger distributed platforms are possible areas for future work.