International Journal of Communication Systems最新文献

Recently, the rapid expansion of blockchain technology has sparked a transformative wave across various sectors, prominently impacting cybersecurity. This paper introduces a pioneering intrusion detection model, the deep logic sparse autoencoder–based kookaburra search (DLSA-KS) algorithm. This innovative approach amalgamates advanced deep learning capabilities with an efficient initial search strategy, significantly enhancing the identification and mitigation of malicious activities within digital environments. The initial phase involves gathering input data from diverse datasets, including the Malware Executable Detection dataset, KDD Cup 1999 dataset, NSL-KDD dataset, Bot-IoT dataset, and UNSW-NB15 dataset. These datasets serve as foundational resources for training and evaluating the DLSA-KS model, ensuring its efficacy across varied cyber threat scenarios. This integration not only bolsters security but also enhances scalability and real-time detection capabilities, crucial for managing the voluminous data dynamics inherent in blockchain ecosystems. Moreover, the DLSA-KS model exhibits remarkable flexibility and optimization abilities, adapting proficiently to diverse network conditions. This adaptability contributes significantly to its overall performance, enabling robust intrusion detection across a spectrum of operational environments. In addition to this, the proposed DLSA-KS approach is evaluated across multiple performance metrics, including accuracy rate, detection rate, error rate, precision, and F-measure. The findings unequivocally demonstrate the model's superiority over existing methodologies, achieving exceptional metrics such as an accuracy rate of 98.7%, detection rate of 99.2%, error rate of 3%, precision of 97.8%, and F-measure of 98.7%. Thus, the results underscore the efficacy of the DLSA-KS algorithm in effectively detecting and mitigating intrusions, thereby affirming its potential as a pivotal advancement in cybersecurity defenses.

A compact high-gain 16-port multiple-input-multiple-output (MIMO) antenna with a double spiral arm electromagnetic band gap (EBG) array is presented for 5G wireless networking applications. Each resonator of the proposed MIMO antenna consists of a microstrip line feeding, a fork-like monopole, and a partial ground plane. An array of EBG unit cells is positioned beneath the antenna elements to increase gain while decreasing surface wave effects, resulting in improved isolation among the resonating elements. The −10-dB impedance bandwidth of the trapezium-shaped monopole antenna element with EBG is 13.6 GHz (20.6–34.2 GHz) and isolation of > 54 dB. The peak gain of the double spiral arm EBG-based antenna is 24.4 dB. The presented trapezium-shaped mm-wave MIMO antenna offers decent diversity proficiency metrics like envelope correlation coefficient (< 0.26), directive gain (~10 dB), and total active reflection coefficient (< −27.5 dB). The overall size of the presented 16-port mm-wave MIMO antenna is 43.5 mm × 43.5 mm and can be used for n257/n258/n261 5G wireless systems.

The rapid expansion of the Internet of Things (IoT) and the increasing demand for wireless communication have intensified the need for efficient spectrum management in cognitive radio networks (CRNs). Traditional approaches to spectrum sensing and allocation often operate in isolation or rely on static methods, which fail to address the dynamic and evolving nature of modern wireless environments. As IoT devices proliferate and spectrum resources become increasingly congested, there is a pressing need for more adaptive and efficient spectrum management solutions. Our approach addresses this need by offering an adaptive framework that responds to the changing spectrum landscape, thereby optimizing spectrum usage and reducing interference. This research suggests improved NN joint spectrum sensing for CR-IoTNet (INJSS-CR). This approach leverages cognitive radio (CR) technology to enhance spectrum utilization and mitigate the impact of spectrum shortages. CR technology enables secondary users (SUs) to detect and access unused spectrum through spectrum sensing. Within the CR-IoTNet framework, joint spectrum sensing and allocation are performed to serve SU-IoT devices via an interference-free channel (IFC). The system comprises multiple primary user base stations (PU-BSs) and SU devices functioning as IoT smart objects. Additionally, we integrate an improved neural network (INN) to adapt to dynamic network conditions and monitor primary user (PU) spectrum utilization using a comprehensive multiclass (J × 8) − D feature set. This combination of advanced techniques and CR technology aims to optimize spectrum management and support the growing IoT ecosystem. In particular, the INJSS-CR obtained the greatest accuracy of 0.9492 at a training rate of 80%.

Millimeter-wave (mmWave) wireless communication has several hurdles, including the overhead associated with beam training, the limitations of low-power phased-array topologies, and the problems caused by phase-less power measurements due to oscillator phase noise. Accuracy in beamforming is impacted by traditional beam tracking’s difficulties with high mobility and large arrays. To solve these issues, a novel oversampled convolutional neural network bidirectional long short term memory (CNN-BiLSTM) model is proposed in this paper to train and track the beam. To normalize data and reduce overfitting, synthetic minority over sampling technique (SMOTE) is used. The CNN-BILSTM architecture presented uses batch normalization, max-pooling, ReLU activation, convolution, and normalization layers to extract spatiotemporal features from location and power metrics. This improves the effectiveness of data processing and assists in developing databases for predicting the angle of arrival/angle of departure (AoA/AoD). Lastly, a fully connected layer offers a reliable solution for accurate beam alignment in mmWave communications by predicting AoA/AoD. The results obtained show that the suggested technique achieves accuracy in AoA and AoD estimates while having reduced mean squared error (MSE) as compared to baseline methods. The future work to enhance mmWave beam tracking and training may focus on dynamic adaptation, deep reinforcement learning, multiobjective optimization, hardware optimization, robustness analysis, and integration with 5G and beyond technologies.

Dynamic burst size selection is a challenging process in the optical burst switching (OBS) networks for efficient burst assembly. In this manuscript, a dynamic burst-size assembly approach is proposed to standardize the data burst size in OBS networks. The proposed approach utilizes hysteresis properties in the burst size decider module (BSDM) to decide the data burst size. The inculcation of the dynamic burst assembly algorithm (DBAA) focuses on the nonlinear features to handle the blocking problem during the burst assembly process. DBAA involves a priority evaluator mechanism to determine the importance of each incoming packet at the ingress node. This provides a dynamic decision-making strategy to standardize the data burst size with change in transition count number (TCN). The performance of the proposed approach is evaluated on the self-similar traffic model with burstiness, ranging from H = 0.5–0.7. The experimental results show a decrease in the average queuing delay by 14.59% and an improved average burst utilization by 23.36% compared with the hybrid (time/length) approach. However, the proposed DBAA attains better burst utilization with a significant reduction in queuing delay. Furthermore, the consistency value of burst sizes indicates that DBAA performs better in terms of burst utilization than existing approaches.

Vehicular ad hoc networks (VANETs) are pivotal in intelligent transportation systems (ITSs), enabling swift vehicle communication. The study focuses on optimizing highway traffic flow, particularly discretionary lane changing (DLC) via vehicle-to-vehicle (V2V) communication. To address the dynamic highway environment, an intelligent DLC decision-making model integrating deep learning techniques is proposed. The model employs the enhanced competitive swarm optimization (ECSO) algorithm for traffic density and the improved locust search (ILS) algorithm for vehicle mobility prediction. The nonlinear autoregressive dynamic neural network (NAR-DNN) serves as the decision-making framework, offering choices such as free lane change, forced lane change, and no lane change. SUMO and NS2 simulations evaluate the model, demonstrating its efficacy in establishing efficient communication links. Results show significant improvements over traditional frameworks, with the NAR-DNN achieving superior performance in packet delivery rates (12%–18%), connectivity probability (10%–15%), message delay (15%–20%), and average lane-change duration (8%–12%), respectively. These findings highlight the NAR-DNN's effectiveness in enhancing traffic management and safety within VANETs, offering promising insights for future ITS advancements.

The Internet of Things (IoT) paradigm has recently opened up new research opportunities in many academic and industrial fields, particularly medicine. IoT-enabled technology has transformed healthcare from a centralized model to a personalized healthcare system driven by ubiquitous wearable devices and smartphones. The implementation of IoT in healthcare faces critical challenges, including energy efficiency, network reliability, task response time, and availability of services. An Adaptive Fox Optimizer (AFO) is proposed as a novel IoT-supported method for providing healthcare services. The zero-orientation nature of AFO is mitigated by quasi-oppositional learning. A reinitialization plan is also presented to improve exploration skills. Furthermore, an additional stage is implemented with two novel movement techniques to optimize search capabilities. In addition, a multi-best methodology is used to deviate from the local optimum and manage the population more efficiently. Ultimately, greedy selection accelerates convergence and exploitability. The proposed AFO was rigorously evaluated, demonstrating significant improvements across key performance metrics. Compared to conventional approaches, AFO enhances system availability by 83.33%, reliability by 11.32%, reduces energy consumption by 19.12%, and decreases task response times by 25.14%. These results highlight AFO's ability to optimize resource allocation, enhance fault tolerance, and prolong network lifespan in IoT healthcare environments. By addressing critical challenges, this research contributes to developing more efficient, reliable, and responsive IoT-enabled healthcare systems, paving the way for advancements in wearable health monitoring, telemedicine, and smart hospital management.

The sixth-generation (6G) communication technology is expected to provide the seamless and ubiquitous wireless access for future cellular networks. Due to the rise of network demands and the limited communication resource, the multiband network (MBN) system is needed to satisfy the stringent communication requirements. This trend triggers to utilize millimeter wave and THz frequency bands, in addition to traditional radio frequency band. In this paper, we divide the MBN control problem into two subproblems: the base station (BS) association problem and spectrum allocation problem. To jointly optimize these problems, we mainly focus on the cooperative game theory. According to the coalition formation game, each device considers the characteristics of different spectrum bands and forms its BS association for communication services. And then, a new control paradigm, called Max-min Multicriteria Bargaining Solution (MMBS), is introduced to solve the MBN spectrum allocation problem. By considering multiple criteria, the main concept of MMBS is the max–min–max optimization method based on the Nash product. By using the interactive relationship between BSs and individual devices, our joint control approach can effectively handle the MBN technology to ensure mutual advantages. Finally, simulation results show the efficiency and the effectiveness of our cooperative game approach. Compared with the existing MBN protocols, analytical results demonstrate the superior performance of the proposed solution.

In the rapidly evolving Internet of Things (IoT) ecosystem, the integration of software-defined networking (SDN) with edge computing is critical for optimizing performance in IoT applications. This paper introduces a novel framework, named D-RESIN, designed to dynamically orchestrate resources within IoT-enabled SDN at the edge, explicitly focusing on minimizing delays. The proposed framework employs evolutionary game theory to manage and optimize resource allocation across IoT devices, Open vSwitches, and Edge nodes. We implemented the proposed D-RESIN schemes using the Mininet network emulator with Ryu SDN controller and Open vSwitches. We found out that D-RESIN reduces average processing delay at the access tier by 52.43%–88.82% and 32.71%–87.91% compared to the existing scheme—T-RESIN and FlowMan, respectively. At the edge tier, D-RESIN decreases the average processing delay by 35.44-85.10% compared to T-RESIN. These simulation results highlight the effectiveness of D-RESIN in enhancing scalability and efficiency for delay-sensitive IoT applications.