Uma Choppali

Venkata Prasanth Yanambaka, Uma Choppali, Saraju P. Mohanty

The Internet of Things (IoT) was introduced almost two decades ago. In the past two decades, technology has seen huge advancements. Many devices have become powerful and have less power consumption. Many IoT architectures and environments were introduced to help make life easier, especially in wearable devices. The market for these wearable devices has constantly increased over the years and is expected to reach its maximum in the next couple of years. They also pose a threat to users' privacy and security because they constantly store and transmit personal information such as location, heart rate, and other sensitive data. Therefore, addressing the security vulnerabilities is a crucial aspect of this research. This paper presents a hardware-assisted, energy-efficient, low-overhead security solution for wearable devices. Specifically, two Physical Unclonable Function (PUF) architectures: Arbiter PUF and Hybrid Oscillator Arbiter (HOA) PUF are analyzed for integration in IoT systems. The result shows that Arbiter PUF consumes 25 $μ$W, whereas HOA PUF consumes only 2.7 $μ$W to generate keys for cryptographic purposes. These architectures introduce minimal power overhead while providing robust security, making them well suited for resource-constrained IoT ecosystems.

Sibi C. Sethuraman, Pranav Kompally, Saraju P. Mohanty et al.

Smart healthcare which is built as healthcare Cyber-Physical System (H-CPS) from Internet-of-Medical-Things (IoMT) is becoming more important than before. Medical devices and their connectivity through Internet with alongwith the electronics health record (EHR) and AI analytics making H-CPS possible. IoMT-end devices like wearables and implantables are key for H-CPS based smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. There are various smart garments that help users to monitor their body vitals in real-time. Many commercially available garments collect the vital data and transmit it to the mobile application for visualization. However, these don't perform real-time analysis for the user to comprehend their health conditions. Also, such garments are not included with an alert system to alert users and contacts in case of emergency. In MyWear, we propose a wearable body vital monitoring garment that captures physiological data and automatically analyses such heart rate, stress level, muscle activity to detect abnormalities. A copy of the physiological data is transmitted to the cloud for detecting any abnormalities in heart beats and predict any potential heart failure in future. We also propose a deep neural network (DNN) model that automatically classifies abnormal heart beat and potential heart failure. For immediate assistance in such a situation, we propose an alert system that sends an alert message to nearby medical officials. The proposed MyWear has an average accuracy of 96.9% and precision of 97.3% for detection of the abnormalities.