Accurate detection of spectrum holes is a useful requirement for cognitive radios that improves the efficiency of spectrum usage. The authors propose three novel, simple, and entropy‐based detectors for spectrum sensing in cognitive radio. The authors evaluate the probability of detection of these three detectors: Vasicek's entropy detector, truncated Vasicek's entropy detector, and Van Es' entropy detector, over a predefined probability of false‐alarm. In particular, the authors provide the approximate and asymptotic test statistics for these detectors in the presence and absence of Nakagami‐m fading, noise variance uncertainty, and optimised detection threshold. Furthermore, the authors provide a detailed comparison study among all the detectors via Monte Carlo simulations and justify authors results through real‐world data. The authors’ experimental results establish a superior performance of truncated Vasicek's entropy detector over Vasicek's entropy detector, energy detector, differential entropy detector and Van Es' entropy detector in practically viable scenarios.

We introduce three novel, simple and robust entropy‐based detectors in the domain of spectrum sensing (SS) in cognitive radio. We evaluate the probability of detection of these three detectors: Vasicek's entropy detector (VED), truncated VED (TVED), and Van Es entropy detector (VEED), over a predefined probability of false‐alarm. Our experimental results establish a superior performance of TVED over VED, energy detector, differential entropy detector and VEED in practically viable scenarios.image

The efficiency improvement of healthcare systems is a major national goal across the world. However, delivering scalable and reliable healthcare services to people, while managing costs, is a challenging problem. The most promising methods to address this issue are based on smart healthcare (s‐health) technologies. Furthermore, the combination of edge computing and s‐health can yield additional benefits in terms of delay, bandwidth, power consumption, security, and privacy. However, the strategic placement of edge‐servers is crucial to achieve further cost and latency benefits. This article is divided into two parts: an AI‐based priority mechanism to identify urgent cases, aimed at improving quality of service and quality of experience is proposed. Then, an optimal edge‐servers placement (OESP) algorithm to obtain a cost‐efficient architecture with lower delay and complete coverage is presented. The results demonstrate that the proposed priority mechanism algorithms can reduce the latency for patients depending on their number and level of urgency, prioritising those with the greatest need. In addition, the OESP algorithm successfully selects the best sites to deploy edge‐servers to achieve a cost‐efficient system, with an improvement of more than 80%. In sum, the article introduces an improved healthcare system with commendable performance, enhanced cost‐effectiveness, and lower latency.

This paper discusses the challenges of delivering scalable and cost‐effective healthcare services and proposes the combination of smart healthcare technologies and edge computing as a promising solution. The paper proposes an AI‐based priority mechanism to identify urgent cases and an optimal edge‐servers placement algorithm to obtain a cost‐efficient system with lower latency. Results show significant improvements in system performance, cost‐effectiveness, and latency reduction.image

With the increasing rate and types of cyber attacks against information systems and communication infrastructures, many tools are needed to detect and mitigate against such attacks, for example, Intrusion Detection Systems (IDSs). Unfortunately, traditional Signature‐based IDSs (SIDSs) perform poorly against previously unseen adversarial attacks. Anomaly‐based IDSs (AIDSs) use Machine Learning (ML) and Deep Learning (DL) approaches to overcome these limitations. However, AIDS performance can be poor when trained on imbalanced datasets. To address the challenge of AIDS performance caused by these unbalanced training datasets, generative adversarial models are proposed to obtain adversarial attacks from one side and analyse their quality from another. According to extensive usage and reliability criteria for generative adversarial models in different disciplines, Generative Adversarial Networks (GANs), Bidirectional GAN (BiGAN), and Wasserstein GAN (WGAN) are employed to serve AIDS. The authors have extensively assessed their abilities and robustness to deliver high‐quality attacks for AIDS. AIDSs are constructed, trained, and tuned based on these models to measure their impacts. The authors have employed two datasets: NSL‐KDD and CICIDS‐2017 for generalisation purposes, where ML and DL approaches are utilised to implement AIDSs. Their results show that the WGAN model outperformed GANs and BiGAN models in binary and multiclass classifications for both datasets.

The quality of adversarial attacks produced by generative adversarial models (GANs, BiGAN, WGAN) and their impact on enhancing AIDS performance against unseen and rare attacks is assessed. The evaluation of generative adversarial models is covered by training on high diversity attacks and from real network traffic (NSL‐KDD and CICIDS‐2017). Also, AIDS built based on adversarial attacks are assessed based on different criteria, including classification types and learning approach (ML, DL), without bias to specific ones.image

With the support of the sixth‐generation mobile networks (6G) technology, the Internet‐of‐Vehicle (IoV) can realize the perception and monitoring of vehicle road information. However, due to the change of network topology and various environment, the reliable performance of the communication link is facing challenges. For the sake of improving communication quality, a cooperative vehicular network (CVN) system is established, which adopts cooperative communication and multiple input multiple output (MIMO) technology. According to the signal‐to‐noise ratio (SNR) threshold of relay vehicles, using hybrid decode‐amplify‐forward (HDAF) protocol and combining with antenna selection, the analytical expression of outage probability (OP) with Meijer‐G function is obtained. For predicting the OP accurately, the sparrow search algorithm based on back‐propagation neural network (SSA‐BPNN) is put forward. The simulation results show that the cascade order of the channels has a negative effect on the OP. Meanwhile, the prediction accuracy of SSA‐BPNN is 64.8% higher than that of BPNN, and 98.96% greater than that of general regression neural network, and the convergence rate is faster than ICS‐BPNN.

For the sake of improving communication quality, a cooperative vehicular network (CVN) system is established, which adopts cooperative communication and multiple input multiple output (MIMO) technology. According to the signal‐to‐noise ratio (SNR) threshold of relay vehicles, using hybrid decode‐amplify‐forward (HDAF) protocol and combining with antenna selection, the analytical expression of outage probability (OP) with Meijer‐G function is obtained. For predicting the OP accurately, the sparrow search algorithm based on back‐propagation neural network (SSA‐BPNN) is put forward.image

New cell phone services and apps consume more spectrum. Wireless spectrum allows services and apps to communicate with one another. Wi‐Fi quality is improved via smart spectrum usage and new CRT services. The use of spectrum is beneficial. Cross‐layer architecture improves the energy efficiency of wireless networks. System performance is improved by connecting protocol layers. Cross‐layer configuration does not introduce layer functionality into a network. By protecting networks, cross‐layer design increases communication. C‐LNRD uses self‐determined time slots to promote communication. Agents that collect information. At each level, the monitoring agent monitors traffic, time, and topology. Each layer of agents has its own database. Data is received by the network, MAC, and physical layers. Based on its measurements, each node grants trust. Routes were altered. PR ATTACK does not have RTS, CTS, or RREQ to reduce false positives. Spectrum allocation is improved via cognitive radio and learning technologies. Adaptive Cognitive Radio Networks are created using AI, GA, Fuzzy Logic, and Game Theory (ACRN). DSA creates high‐bandwidth MCRNs. This research looks at MCRNs in order to optimise spectrum usage, throughput, routing delay, and overhead. Multihop, the proposed approach by CRN takes into account spectrum awareness, quality route establishment, and route maintenance in the event that a connection fails due to spectrum or a node transfer. New strategies improve the cross‐layer network protocols of MCRN. Learners gain from spectrum models. Sensors and routers are linked by layers. The proposed routing improves both performance and spectrum use.

Cross Layered Neighbour Route Discovery (C‐LNRD) is the first method established in this study to improve the efficiency of communication in a WSN context. To begin, the sensor network is explored using MAC sub layers based on Dynamic Source Routing (DSR). The MAC layer also has a “dynamic route neighbour discovery tableˮ.image