International Journal of Communication Systems最新文献

Cognitive radio (CR) is a new technology that aims to make better use of the radio spectrum. Its core component is spectrum sensing, which is difficult to accomplish precisely because many factors affect detection performance. Numerous spectrum sensing techniques, including the cyclic stationary detection algorithm, matching filter detection algorithm, energy detection algorithm, and others have been presented recently; these algorithms, though, depend on specific previous knowledge and are model-driven. Inaccurate model assumptions or difficult-to-obtain prior knowledge will impair the algorithms' detection performance. A novel method of achieving spectrum sensing is made possible by the advancement of completely learning alongside neural network capabilities. In this paper, spectrum sensing in cooperative cognitive radio networks utilizing binarized simplicial convolutional neural network performance in dynamic environments (SS-CCRN-BSCNN-DE) is proposed. Initially, the input signals are gathered from the GSM-900 spectrum dataset. Then, the data are preprocessed utilizing reverse lognormal Kalman filtering (RLKF) to normalize the gathered signals. Then, the preprocessed data are fed into feature extraction using quadratic phase S-transform (QPST) to extract the hidden spatial features. Then, the extracted features are fed into binarized simplicial convolutional neural network (BSCNN) for detecting the spectrum sensing in the cognitive radio system. The multiobjective thermal exchange optimization (MOTEO) is implemented to optimize the hyperparameters of BSCNN. The proposed method is implemented and its performance is evaluated under some metrics, like accuracy, signal-to-noise ratio (SNR), and root mean square error (RMSE), as well as computational time. The performance of the SS-CCRN-BSCNN-DE approach attains 16.21%, 17.18%, and 24.44% higher accuracy; 21.25%, 17.25%, and 13.32% lower RMSE; 11.17%, 19.15%, and 23.14% lower SNR when compared with existing methods: cooperative spectrum sensing depending on convolutional neural network (CNN) in cognitive radio scheme (CSS-CNN-CRS), improved spectrum prediction method for cognitive radio networks utilizing artificial neural network (ISP-CRN-ANN), and recurrent neural network based spectrum sensing cognitive radio (RNN-SS-CR), respectively.

Internet of Things (IoT) has been diversified smart networks of connected things consisting of users, smart devices, and interwoven equipment that facilitate communication using wired and wireless mechanisms. The devices in IoT networks transfer data among each other to provide amenities to mankind. These networks have been in hot demand and use in the recent past and have scaled up to a large extent. Fog-based IoTs have been maneuvered as a furtherance to cloud computing by accomplishing the processing of services faster and closer to end users. However, the rapid increase in the demand for services has raised various concerns like security and privacy in fog-based IoTs, and therefore, the identification and removal of malicious devices have become a prominent challenge. To conquer this, a secure method called TRUFOG is presented in this article. TRUFOG is a task-based dynamic trust mechanism. The proposed approach utilizes the following three major parameters: task success, task pending, and task failure ratio. Along with this recommendation credibility is used to estimate the trust of the fog node and average energy consumption is also tested. The security resistance of the TRUFOG mechanism is tested against black hole, gray hole, and energy consumption attacks. The proposed mechanism is extensively evaluated and compared with existing methods like TCF, TBMSC, and TRDTM in terms of accuracy, delivery ratio, energy consumption, and other metrics. The results have proven that TRUFOG has effectively recognized the malicious node in the network with a detection rate of 95.2%. The packet delivery ratio for TRUFOG under attack was found to be 4.5% better than TCF, 6.25% better than TBMSC, and 4.66% better than TRDTM. Furthermore, the average energy consumption of the TRUFOG method was found to be 13.6% lesser than TCF, 30.7% lesser than TBMSC, and 49.9% lesser than the TRDTM model.

DoS attacks pose significant threats to wireless sensor networks (WSNs) by disrupting regular network availability. The existing systems face limitations such as limited power, storage, bandwidth, and processing capabilities, making them particularly vulnerable to security risks. Despite these constraints, an effective intrusion detection system (IDS) is essential for detecting such attacks. As denial-of-service (DoS) attacks become more frequent and sophisticated, the traditional intrusion detection systems are losing their effectiveness. To overcome these complications, Efficient Detection of Denial-of-Service Attacks in Wireless Sensor Networks using Binarized Simplicial Convolutional Neural Networks for Enhanced Security (ED-DoS-WSN-BSCNN) is proposed. The input data are collected from the WSN-DS dataset. The gathered data are given to the preprocessing stage with the help of the adaptive two-stage unscented Kalman filter (ATSUKF) for data cleaning, data transformation, and normalization. Then the preprocessed data are given to the classification stage by using the binarized simplicial convolutional neural network (BSCNN) for classifying DoS attacks, such as normal, blackhole, grayhole, flooded, and TDMA. Finally, the Arctic tern optimizer (ATO) algorithm is employed to enhance the BSCNN that categorizes the types of DoS attacks accurately. The performance metrics like accuracy, precision, recall, specificity, F1-score, computational time, and RoC are taken into account. The performance of the proposed technique is compared with other existing methods. The ED-DoS-WSN-BSCNN technique is implemented in Python. The proposed technique attains 4.05%, 7.52%, and 2.91% higher accuracy, 4.10%, 7.61%, and 5.14% higher precision, 7.46%, 6.92%, and 2.88% higher recall, and 1.06%, 1.75%, and 2.31% higher specificity compared with existing methods: performance analysis of deep learning for DoS attacks identification in wireless sensor network (CNN-DoS-WSN), detection of DoS attack in wireless sensor networks: a lightweight machine learning approach (KNN-DoS-WSN), and extended evaluation on machine learning approach for DoS detection in Wireless Sensor Networks (RT-DoS-WSN), respectively.

One of the main benefits of the full-duplex (FD) technique is to enhance the spectrum efficiency and throughput of the communication systems. This can be obtained by enabling simultaneous transmission and reception over the same frequency channel. Digital self-interference (SI) cancellation becomes crucial for carrying out in-band FD communication. In this paper, we use multicarrier signals in conjunction with three adaptive filters to cancel the SI signals. These filters include the least mean square (LMS), normalized LMS (NLMS), and QR decomposition-based recursive least squares (QR-RLS). The LabVIEW software is used to prepare the transmitted signal and the Universal Software Radio Peripheral (USRP) platform. At the same time, a software-defined radio (SDR) technology is utilized to transmit and receive the signals. The experimental results demonstrate that the performance of using adaptive filters can achieve cancellation beyond the noise floor. In addition, SI signal reduction using the QR-RLS algorithm is 31 dB, which exceeds the noise floor level (30 dB) by 1 dB and outperforms the LMS and NLMS algorithms. The QR-RLS exhibits faster convergence and better stability in dynamic scenarios, making it a preferred choice for practical applications in real-time systems.

An equivalent circuit-based microstrip antenna of dual-band characteristics has been planned by cutting six vertical rectangular notches of different sizes in this article. The bandwidth of the presented antenna is achieved 23.77% (420 MHz) and 5.46% (137 MHz). The antenna is resonating in dual band at 1.903 and 2.489 GHz with return losses of −35.28 and −23.10 dB, respectively. The suggested antenna structure have frequency band between 1.557 and 1.977 GHz in the lower band and 2.441 and 2.578 GHz in the upper band. The gain of the suggested antenna is 3.760 dBi at 1.903 GHz and 3.803 dBi at 2.489 GHz. The efficiency of proposed antenna is 90% and 89% at both resonating frequencies 1.903 and 2.489 GHz, respectively. However, more than 81% antenna efficiency is observed in both operating bands. The suggested microstrip antenna is fabricated on FR-4 substrate of size 39.04 × 47.64 mm² (0.25 × 0.30 $��{\lambda }_0^2�$ at frequency 1.903 GHz) and excited by microstrip line feed of 50 Ω. The presented design of antenna is validated with measurement and equivalent circuit. The lower resonating band 1.557–1.977 GHz is suitable for GPS (1.575 GHz), GSM 2G (1.8 GHz), and PCS (1.90 GHz). However, upper resonating band 2.441–2.578 GHz is suitable for Bluetooth (2.45 GHz), ISM band (2.45 GHz), and 4G (2.5 GHz) applications.

A wireless body area network (WBAN) interconnects computer devices and sensing devices through wireless communication links. WBAN avails continuous communication and support in the fields of sports, entertainment, and remote health monitoring, due to its flexibility, real-time data support, and monitoring. Recent innovations in WBAN focus on improving the communication quality in terms of delay and energy. Even though a lot of clustering-based routing models have been established in WBAN research, identifying the quality nodes through optimization algorithms can be further improved considering multiple objectives. In this work, an efficient multihop routing inspired by an intelligent hybrid heuristic clustering algorithm is developed for achieving a high-quality transmission path in WBAN. Initially, the cluster groups are generated, where the cluster head (CH) selection is overlooked by the newly developed hybrid emperor penguins colony-based dolphin pod optimization algorithm (HEPC-DOA). An objective function considering the WBAN constraints including residual energy, inter- and intra-cluster distance, and delay is mainly considered for the optimal CH selection. Also, a multihop routing path is generated with the support of a developed HEPC-DOA. Again, the designed optimal path satisfies the objectives like distance, energy, number of hops, transmission rate, data flow, path loss, packet reception rate, throughput, and delay. As both the CH selection and the routing path are done under an intelligent optimization technique, the WBAN ensures achieving improved packet reception rate, maximum throughput ratio, minimal delay, and better network lifetime. The developed multihop routing protocol with the proposed HEPC-DOA is compared with conventional routing protocols to prove its efficacy and the comparative results indicate that the developed HEPC-DOA procedure has achieved optimal solutions through its strong exploration rate, and improved the overall system throughput at 94.1%.

This study introduces an innovative tree-shaped two- and four-port multiple-input and multiple-output (MIMO) antenna modeled to be used in unlicensed ultrawideband (UWB) systems suitable for 5G midband (4.8–4.9 GHz), extended WiFi-6-GHz band, and 7-GHz 6G Spectrum (7.025–7.125 GHz) mitigating interference with other useful wireless bands such as WiFi-5 frequency band (5.15–5.85 GHz), IEEE 802.16 band (3.3–3.7 GHz), and Satellite C-band extension (6.7–7.1 GHz). Wide bandwidth, low profile, small size, low mutual coupling, low cost, stable radiation pattern, good gain, efficient diversity parameters, minimum channel capacity loss, and mitigating interference with highly engaged bands are among the few design challenges faced by such band-notched UWB MIMO antennas. The low-profile, efficiently downsized two-port tapered transformer-based microstrip line-fed antenna makes use of an economical FR4 substrate that measures 0.33λ₀ × 0.43λ₀. The measured −10dB impedance bandwidth covers the frequency spectrum from 2.8 to 11 GHz, with a maximum isolation of 17.5 dB between both ports. A vertical rectangular notch on the ground plane and a grounded conducting stub are used to improve isolation. To prevent interference with WiMAX, WLAN, and weather satellite applications, it has three-stop bands with the aid of rectangular slot stubs on the ground, a C-slot on the feedline, and a J-slit on the patch. Surface electrical current analysis confirms the nonfunctionality of the antenna at these notch bands. Moreover, the investigated radiation behavior of the antenna confirms that the power level at notch frequencies is significantly lower than the −3dB value. In pursuit of better radiation performance, a four-port MIMO antenna is also suggested and evaluated. The four-port proposed MIMO antenna exhibits excellent pattern diversity and improved isolation in the band from 2.8 to 10.6 GHz. Time domain analysis and MIMO performance metrics for the suggested two- and four-port antenna are also studied. Results show good modeled and experimental agreement for both two- and four-port antennas, suggesting their potential use for 5G/6G/WiFi-6E IoT Mobile Devices.

This study presents an end-to-end performance evaluation of a hybrid radio frequency/free space optic–visible light communication (RF/FSO-VLC) communication link designed to transmit 10-Gbps on–off keying (OOK)–modulated data. The data are transmitted over outdoor RF/FSO channels to an indoor VLC channel, ultimately reaching the end users.

To evaluate the end-to-end performance of the RF/FSO-VLC hybrid communication system, the outage probability is first computed using distinct channel models for each segment: the Nakagami fading model for the RF link, a combined gamma–gamma atmospheric turbulence and pointing error model for the FSO link, and the Lambertian propagation model for the VLC link. The probability density function (PDF) and cumulative distribution function (CDF) of the signal-to-noise ratio (SNR) are derived and used in a Monte Carlo simulation implemented in MATLAB. Subsequently, an optical system simulation is conducted by varying input parameters such as laser power, link distances, atmospheric attenuation, field-of-view (FOV) angle, and RF carrier frequency. System performance is assessed in terms of bit error rate (BER), quality factor (Q factor), and SNR. Machine learning (ML) techniques—including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forests (RFs)—are employed for real-time performance monitoring and dynamic link switching within the hybrid communication system. These models are trained using data generated from optical simulations. The simulation outputs are processed by an artificial intelligence module comprising predictive models based on the aforementioned ML algorithms. This module enables intelligent switching between communication links to maintain the required quality of service (QoS), based on predicted performance metrics.

According to the obtained results, the outage probability analysis indicates that the RF-VLC and FSO-VLC hybrid links offer improved performance compared to standalone RF, FSO, and VLC links, with the FSO-VLC configuration demonstrating superior performance over the RF-VLC link. Additionally, the ML models, which enable hard switching based on optical system performance analysis, achieved prediction accuracies ranging from 95.64% to 98.75%, with an average switching accuracy of 97.5% (R² = 0.975).

The study employs a novel trust-aided routing approach that employs a stacked gated recurrent unit (SGRU) to estimate the node's trustworthiness in mobile ad-hoc networks (MANETs). In this designed model, an optimal path for effective routing is selected by the hybrid golf optimizer with walruses behavior-based optimization algorithm (HGO-WBOA), which is the combination of both golf optimization algorithm (GOA) and walruses behavior optimization algorithm (WBOA). The effectiveness of routing is enhanced by considering some multi-object constraints such as remaining energy, throughput, trust, path loss, packet energy cost, security, battery, routing overhead ratio, bandwidth, link cost, end-to-end delay, hop count, and packet delivery ratio (PDR) during the optimization process. This designed SGRU identifies security threats and provides an alternate node by removing the attack node to guarantee the integrity of the network. The numerical validations are performed with some traditional energy efficiency routing approaches in MANET. The throughput of the recommended HGO-WBOA-SGRU is 95%, which is greater than the previous approaches, such as COA-SGRU (88.5%), LOA-SGRU (94.5%), GOA-SGRU (84%), and WBO-SGRU (93%), respectively, for the 200th node value. The outcomes prove the effectiveness of the designed approach in recognizing and reducing security threats while also enhancing energy efficiency and routing performance. The novelty of the proposed approach lies in its integration of SGRU with trust-aided routing, enabling adaptive and accurate trust estimations in dynamic network conditions. This innovative framework allows the model to capture complex relations among nodes and adapt to dynamic network conditions, offering reliable and robust solutions for secure routing in MANETs.

Acoustic propagation delays, bandwidth limits, and node mobility are all big problems for underwater wireless sensor networks (UWSNs). We present a unified analytical framework that combines network function virtualization (NFV) and stochastic network calculus (SNC) to meet the need for reliable communication with low latency. Using VNFs made for underwater environments, our method simulates per-flow delay limits, queue prioritization, and dynamic service chaining. We test the framework by running many simulations in different UWSN scenarios using OMNeT++. The results show that the end-to-end delay, packet delivery ratio, and energy efficiency have all improved significantly. This proves that NFV-SNC integration can be used in real life for underwater applications like marine farming. To make sure the proposed method works, a comparison of the analytical model and simulation data is done. The goal of this study is to improve network architecture so that it can impose delay limits and improve overall performance. The problem is to achieve reliable and quick communication in UWSNs. People think that dynamic optimization using NFV will greatly improve the reliability and performance of UWSN communication systems.