Security is an important aspect of healthcare applications that employ Internet of Things (IoT) technology. More specifically, providing privacy and ensuring the confidentiality, integrity and authenticity of IoT-based designs are crucial in the health domain because the collected data are sensitive, and the continuous availability of the system is critical for the user’s wellbeing. However, the IoT consists of resource-constrained devices that increase the difficulty of implementing high-level-security schemes. Therefore, in the current paper, renowned lightweight cryptographic primitives and their most recent architecture, to the best of the authors’ knowledge, are investigated. Their security, architecture characteristics and overall hardware limitations are analyzed and collected in tables. Finally, all the algorithms are compared based on their effectiveness in securing healthcare applications, the utilized device and the overall implementation efficiency.
In recent years, systems that monitor and control home environments, based on non-vocal and non-manual interfaces, have been introduced to improve the quality of life of people with mobility difficulties. In this work, we present the reconfigurable implementation and optimization of such a novel system that utilizes a recurrent neural network (RNN). As demonstrated in the real-world results, FPGAs have proved to be very efficient when implementing RNNs. In particular, our reconfigurable implementation is more than 150× faster than a high-end Intel Xeon CPU executing the reference inference tasks. Moreover, the proposed system achieves more than 300× the improvements, in terms of energy efficiency, when compared with the server CPU, while, in terms of the reported achieved GFLOPS/W, it outperforms even a server-tailored GPU. An additional important contribution of the work discussed in this study is that the implementation and optimization process demonstrated can also act as a reference to anyone implementing the inference tasks of RNNs in reconfigurable hardware; this is further facilitated by the fact that our C++ code, which is tailored for a high-level-synthesis (HLS) tool, is distributed in open-source, and can easily be incorporated to existing HLS libraries.
Health 4.0 is a new promising addition to the healthcare industry that innovatively includes the Internet of Things (IoT) and its heterogeneous devices and sensors. The result is the creation of numerous smart health applications that can be more effective, reliable, scalable and cost-efficient while facilitating people with their everyday life and health conditions. Nevertheless, without proper guidance, the employment of IoT-based health systems can be complicated, especially with regard to security challenges such susceptible application displays. An appropriate comprehension of the structure and the security demands of IoT-based multi-sensor systems and healthcare infrastructures must first be achieved. Furthermore, new architectures that provide lightweight, easily implementable and efficient approaches must be introduced. In this paper, an overview of IoT integration within the healthcare domain as well as a methodical analysis of efficient smart health frameworks, which mainly employ multiple resource and energy-constrained devices and sensors, will be presented. An additional concern of this paper will be the security requirements of these key IoT components and especially of their wireless communications. As a solution, a lightweight-based security scheme, which utilizes the lightweight cryptographic primitive LEAIoT, will be introduced. The proposed hardware-based design displays exceptional results compared to the original CPU-based implementation, with a 99.9% increase in key generation speed and 96.2% increase in encryption/decryption speed. Finally, because of its lightweight and flexible implementation and high-speed keys’ setup, it can compete with other common hardware-based cryptography architectures, where it achieves lower hardware utilization up to 87.9% with the lowest frequency and average throughput.
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