A. S. M. Badrudduza

The uprising of deep learning methodology and practice in recent years has brought about a severe consequence of increasing carbon footprint due to the insatiable demand for computational resources and power. The field of text analytics also experienced a massive transformation in this trend of monopolizing methodology. In this paper, the original TF-IDF algorithm has been modified, and Clement Term Frequency-Inverse Document Frequency (CTF-IDF) has been proposed for data preprocessing. This paper primarily discusses the effectiveness of classical machine learning techniques in text analytics with CTF-IDF and a faster IRLBA algorithm for dimensionality reduction. The introduction of both of these techniques in the conventional text analytics pipeline ensures a more efficient, faster, and less computationally intensive application when compared with deep learning methodology regarding carbon footprint, with minor compromise in accuracy. The experimental results also exhibit a manifold of reduction in time complexity and improvement of model accuracy for the classical machine learning methods discussed further in this paper.

In the sixth-generation (6G) Internet of Things (IoT) networks, unmanned aerial vehicle (UAV)-mounted base stations and reconfigurable intelligent surfaces (RISs) have been considered key technologies to enhance coverage, flexibility, and security in nonterrestrial networks (NTNs). In addition to aerial networks enabled by NTN technologies, the integration of underwater networks with 6G IoT can be considered one of the most innovative challenges in future IoT. Along with such trends in IoT, this study investigates the secrecy performance of IoT networks that integrate radio frequency (RF) UAV-based NTNs and underwater optical wireless communication (UOWC) with RISs. Under three potential eavesdropping scenarios, i.e., overhearing of RF signal, UOWC signal, and both signals, we derive closed-form expressions for secrecy performance metrics, including average secrecy capacity, secrecy outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput. Extensive numerical analyses and Monte Carlo simulations elucidate the impact of system parameters, such as fading severity, the number of RIS reflecting elements, underwater turbulence, pointing errors, and detection techniques on system security. The findings offer comprehensive design guidelines for network deployment aiming to enhance secrecy performance and ensure secure communication in diverse and challenging environments.

In this study, physical layer security (PLS) in a dual-hop underwater optical wireless communication (UOWC)-radio frequency (RF) network under the intruding attempts of multiple eavesdroppers via RF links is considered. An intermediate decode-and-forward (DF) relay node between an underwater source and a destination transforms the optical signal into an electrical form and forwards it to the destination node with the help of harvested energy by the relay from an integrated power beacon within the system. The source-to-relay link, i.e., a UOWC link, is assumed to follow a mixture of exponential generalized Gamma turbulence with pointing error impairments whereas all the remaining links, i.e., RF links, are assumed to undergo κ-μ shadowed fading. Here, two eavesdropping scenarios are considered depending on the types of intruders, i.e., colluding (Scenario-I) and non-colluding (Scenario-II) eavesdropping operations. The analytical expressions of secrecy outage probability (SOP), probability of strictly positive secrecy capacity (SPSC), and effective secrecy throughput (EST) are derived for each scenario. Furthermore, the impacts of UOWC and RF channel parameters as well as detection techniques on secrecy capacity are demonstrated. A comparative study between two scenarios demonstrates that the collusion between the eavesdroppers imposes the most harmful threat on secrecy throughput but a better secrecy level can be attained by adopting diversity at the destination and power beacon nodes along with heterodyne detection rather than intensity modulation and direct detection technique. Finally, all the derived expressions are verified with the numerical results.

In the realm of the Internet of Things, reconfigurable intelligent surfaces (RISs) have emerged as a pivotal technology, offering unprecedented opportunities to enhance signal quality, coverage, and energy efficiency as part of the ongoing pursuit to overcome the limitations of conventional wireless communication systems. In this context, this paper focuses on the analysis of performance in an integrated air-to-underwater network under the amplified-and-forward relay with variable gain, specifically examining the impact of RISs on a mixed terahertz-underwater optical communication system. This study utilizes the α-μ distribution to characterize the fading effects and pointing error on the THz signal. On the other hand, the underwater turbulence on the optical signal is modeled using the mixture of Exponential Generalized Gamma distribution with pointing error impairments. To provide a basis for comparison, the heterodyne detection technique and the intensity modulation with the direct detection technique are also incorporated. Therefore, analytical expressions of outage probability, average bit error rate, and average channel capacity are demonstrated in terms of the Meijer-G function. To provide more insights, high signal-to-noise approximations of these metrics are also presented. Furthermore, the impact of various modulation schemes, fading severity, pointing errors, atmospheric turbulence conditions, and receiver detection techniques are inspected on the system performance. Finally, the analytical findings are validated through Monte-Carlo simulations, ensuring the robustness of the results.