BEiNG WISE

In this paper, we experimentally demonstrate and analyze the performance of software-defined radio (SDR)-based hybrid optical wireless communication (OWC) links for indoor Internet-of-Things (IoT) networks. We propose all-optical hybrid visible light communication (VLC) and optical camera communication (OCC) using a single light emitting diode (LED) as a transmitter and photodiode (PD) and image sensor (IS) based receivers for simultaneous high-and low-speed data transmission. We present a testbed using an SDR platform, i.e., the ADALM-PLUTO, to transmit and receive high-and low-speed signals in the very-high frequency band and a media converter as part of an OWC link. We evaluate the performance of the proposed hybrid system employing a chip LED modulated with quadrature phase shift keying signal in terms of the bit error rate and the reception success rate Rrs. We show that the proposed scheme can provide an independent link performance, irrespective of the significant differences between the operating data rates of VLC and OCC links. The results show that for a link span of 1 m, we achieve (i) error-free detection for high-speed PD-based VLC; and (ii) 100 % Rrs at varying modulation frequencies for low-speed IS-based OCC.

Wearable communication is one of the key drivers in the Internet of Things (IoT) and body area networks for transmitting vital sensory data. Wearable devices must be lightweight, flexible, and have low energy consumption. In this context, optical wireless communication offers several significant advantages, such as immunity to the radio frequency-induced electromagnetic interference, broad unlicensed spectrum, and enhanced physical layer security. Wearable devices employing light-emitting diodes coupled to side-emitting optical fibers form energy-efficient, lightweight, flexible, and omnidirectional distributed optical transmitters, which are ideal for IoT applications. In this paper, we propose an optical camera communication (OCC)–based wearable system with a unique image processing algorithm capable of detecting and recovering data from distributed transmitters of arbitrary shapes. Experimental results demonstrate the feasibility of the proposed system, where for distances up to 4 m, the bit error rate (BER) performance is well below the forward error correction BER limit with values as low as 5.5×10−5. The presented results demonstrate the potential of OCC-based systems using side-emitting fibers for IoT applications.

The rapid adoption of 5G networks, space networks, and Internet of Things (IoT) technologies in healthcare has significantly expanded the attack surface for cybersecurity threats. This evolving landscape demands robust defense mechanisms that can anticipate and neutralize sophisticated cyber-attacks. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies are pivotal in developing such advanced cybersecurity solutions. A critical factor influencing the effectiveness of these AI/ML models is the quality and diversity of the datasets used in their training. This paper presents a systematic review of various datasets used for AI/ML-based cybersecurity model training across multiple domains, with a focus on 5G networks, IoT healthcare, and space networks. By employing a structured Goal-Question-Metric (GQM) methodology and Quasi-Gold Standard (QGS) validation, we assessed the characteristics, applications, and limitations of real, synthetic, and hybrid datasets in enhancing cybersecurity measures. The review identifies key trends, gaps, and future research directions, highlighting the need for more diverse datasets, standardized benchmarks, and privacy-preserving techniques. Our findings offer insights into improving the resilience of AI/ML models for cybersecurity, guiding the development of more effective and adaptable defense strategies across emerging network technologies.

The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors’ previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors’ ongoing project.