IoT is becoming more common and popular due to its wide range of applications in various domains. They collect data from the real environment and transfer it over the networks. There are many challenges while deploying IoT in a real-world, varying from tiny sensors to servers. Security is considered as the number one challenge in IoT deployments, as most of the IoT devices are physically accessible in the real world and many of them are limited in resources (such as energy, memory, processing power and even physical space). In this paper, we are focusing on these resource-constrained IoT devices (such as RFID tags, sensors, smart cards, etc.) as securing them in such circumstances is a challenging task. The communication from such devices can be secured by a mean of lightweight cryptography, a lighter version of cryptography. More than fifty lightweight cryptography (plain encryption) algorithms are available in the market with a focus on a specific application(s), and another 57 algorithms have been submitted by the researchers to the NIST competition recently. To provide a holistic view of the area, in this paper, we have compared the existing algorithms in terms of implementation cost, hardware and software performances and attack resistance properties. Also, we have discussed the demand and a direction for new research in the area of lightweight cryptography to optimize balance amongst cost, performance and security.

Frozen gait (FG) is an increasingly prevalent concern in individuals with Parkinson’s disease (PD) that limits mobility and increases the risk of falls. Traditional FG detection and monitoring methods using clinical observations and wearable sensors face limitations, such as inflexibility, lack of portability, inaccessibility to individuals, and the inability to provide continuous monitoring in real-life environments. To address these challenges, this experimental study presents the development of a software-defined radio (SDR)-based radio frequency (RF) sensing platform for continuous FG monitoring. Data were collected through multiple experiments involving various physical activities, including FG episodes. The acquired data were processed using advanced signal-processing (ASP) techniques to extract relevant wireless channel state information (WCSI) patterns. The physical activities were classified using machine learning and deep learning models developed on the dataset prepared from the SDR-based RF sensing system. The results demonstrated that the deep learning models outperformed the machine learning models. The bidirectional gated recurrent unit (BiGRU) achieved the highest accuracy of 99.7%. This indicates that the developed system has the potential for accurate, real-time monitoring of FG and other PD symptoms. The proposed RF sensing platform using SDR technology and artificial intelligence (AI) offers an intelligent and continuous monitoring solution, addressing the limitations of traditional methods. This system provides portable, continuous detection of FG events, potentially improving patient care, safety, and early intervention.