Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Abstract

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

“…may find useful results for predicting maritime accidents. With the amount of data increases, deep learning techniques, such as smart sustainable intelligent transportation systems [ 51 ], cognitive-radio-based internet of things networks [ 52 ], and visual perception and environment mapping algorithms [ 53 ] could be introduced for maritime accident prediction in further studies. Additionally, SHAP also has drawbacks, and other post-hoc explanation methods could be used for the same object.…”

Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing

“…The MV-rule uses a threshold τ = (S + 1)/2 with S odd, and this complicates the inversion of the cumulative binomial functions (12) and (14). To perform the inversion, we make use of the suitable approximations of the binomial distribution, most of which have existed for decades.…”

“…We now use (12) with ( 14) and p FA < 1/2 and p D > 1/2, jointly with (40) and (41). Assuming a probability of FA π FA < 1/2 at the FC that is no greater than the target probability πFA and a probability of detection π D > 1/2 at the FC that is no lower than the target probability πD , we obtain…”

“…A second major factor of CSS is the sensing algorithm of the SUs. The choice of this algorithm mostly depends on the available information about the structure of the PU signal [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Matched filtering and feature extraction presuppose that the SUs have some knowledge about the PU signal properties.…”

Joint Beamforming and Phase Shifts Design for RIS-Aided Multi-User Full-Duplex Systems in Smart Cities

“…With the recent progress in requirement definitions and developing potential technologies of the sixth generation (6G) wireless communication, the capacity of the 6G network will increase by nearly 1000 times compared with the fifth generation (5G) to support the ubiquitous Internet of Things (IoT) devices [ 1 , 2 , 3 ]. Full-duplex (FD) technology (i.e., in-band co-time co-frequency) can double the spectral efficiency (SE) at most compared with the traditional time division duplex (TDD) or frequency division duplex (FDD).…”