International Journal of Communication Systems最新文献

Wireless sensor networks (WSNs) are important in real-time applications such as environmental monitoring, health, and automation in industries. Nevertheless, maintaining stable communication and energy efficiency during topology changes and node failures also comes as one of the major challenges. The majority of the currently existing frameworks, such as GSO, OEPO-FPA, and fuzzy-based clustering, specialize in either optimization of energy consumption or fault tolerance, yet many of them do not combine those two concepts effectively. Also, such approaches usually do not have adaptive intelligence to adapt to the evolving network conditions. In order to overcome these shortcomings, the present study is proposing a hybrid neuro-fuzzy optimization (NFO) framework, that is a synergistic combination of fuzzy inference to handle the uncertainty and multilayer perceptron (MLP) to learn fault patterns dynamically, and use particle swarm optimization (PSO) to optimize routing and duty cycles on a global scale. The implementation of the model took place with MATLAB R2023b and NS-3 and was tested on the WSN-DS dataset that includes the main network parameters of residual energy, PDR, and link quality. The proposed approach achieved 92.4% fault detection accuracy, 85% packet delivery ratio, 80% residual energy retention, and extended network lifetime up to 970 rounds, resulting in an improvement of over 15%–25% compared with existing methods. The inclusion of a dynamic feedback loop ensures continuous rule refinement and performance adaptation. This unified and lightweight solution offers a scalable, resilient, and intelligent architecture for self-healing WSNs, presenting a promising direction for future deployments in resource-constrained, mission-critical environments.

In the era of digital healthcare systems and cloud computing, securing the transmission and storage of sensitive medical data has become increasingly critical. Existing authentication protocols in cloud-based environments often suffer from significant limitations, such as high computational costs, vulnerability to insider or impersonation attacks, and insufficient guarantees of anonymity, traceability, and mutual authentication. In this work, we propose a lightweight and robust authentication scheme tailored for smart healthcare systems leveraging elliptic curve cryptography and secure hash functions. Our method ensures mutual authentication, anonymity, perfect forward secrecy, and strong resistance against various attack vectors including man-in-the-middle, replay, and insider attacks. Experimental evaluations demonstrate that our scheme outperforms existing approaches in terms of storage, communication, and computation efficiency, making it a promising solution for securing cloud-based healthcare infrastructures.

This article presents a novel compact printed monopole antenna with a gun-shaped design, developed for multiband operation targeting WLAN, WiMAX, and X-band applications. The antenna features a gun-shaped radiating patch integrated with two L-shaped structures and multiple stubs, which are optimized to support five distinct frequency bands. By introducing a rectangular cut into the patch and a rectangular slot, enhanced multiband performance with good impedance matching and radiation characteristics is achieved. The antenna is fabricated on a low-cost, low-profile FR4 substrate with a dielectric constant of 4.4, a loss tangent of 0.02, and a thickness of 0.8 mm. It has a compact overall size of 18 × 18 × 0.8 mm³ and operates across the following frequency bands: 2.3–2.84, 3.4–3.7, 4.8–5.2, 6.2–7.1, and 7.9–8.2 GHz, all with reflection coefficients (S₁₁) better than −10 dB. The measured peak gains at 2.4, 3.6, 5.0, 6.5, and 8.0 GHz are 2.4, 2.8, 2.9, 4.1, and 2.8 dBi, respectively. The antenna achieves impedance bandwidths of 22.5%, 8.33%, 8.00%, 13.84%, and 3.75% at the respective resonant frequencies of 2.4, 3.6, 5.0, 6.5, and 8.0 GHz. It also exhibits high radiation efficiency, exceeding 90% across all bands. A close agreement is observed between the simulated and measured results, confirming the antenna's suitability for multiband wireless communication systems.

Appropriate routing strategies are necessary for mobile ad hoc networks (MANETs) in order to facilitate effective data transfer. In order to counter the prevailing problems, the correct routing schemes will need to be selected as the default configurations are used. In this paper, a special optimal link state routing (OLSR) protocol is proposed to incorporate a deep learning methodology to facilitate efficient video streaming in MANETs. This study presents a new improved variant of the OLSR protocol, which is specially tailored to achieve efficient video streaming in MANETs. It is a radical approach that combines a deep-learning model with blockchain technology to overcome security and reliability issues. It starts with the gathering of video content that is available publicly. In order to detect black-hole nodes, a special twin-attention-based Elman spiking neural network model is applied. The reliability of the neighboring nodes is then measured by means of trust values. The pufferfish optimization algorithm, or the accuracy-aware energy-efficient multipath routing algorithm (AEMRAP), which takes into account node- and link-stability degrees, is used in making routing decisions. Interplanetary file system (IPFS) technology is used to store the data on blockchain and increase its security. The authentication of the blockchain architecture is conducted via the delegated proof-of-stake (DPoS) method that also delivers an extra protection of MANETs against unauthorized access. The study demonstrates superior performance in securing and optimizing video transmission, confirming that the extended OLSR protocol is highly effective for MANET video streaming applications. The proposed model exceeds the current approaches with a throughput of 2100 Kbps, an average end latency of 20.2 s, and a packet-delivery ratio of 92.3%.

The rapid increase in the integration of wireless sensor networks within the Internet of Things (IoT) ecosystem has led to crucial difficulties in providing reliable, energy-efficient, and secure communication. Most of the traditional intrusion detection models face few struggles in mitigating these challenges due to limited scalability and centralized processing. Therefore, this paper proposes a lightweight federated learning–based energy-aware (LF-LEA) model to overcome all the existing issues. The proposed model is an integration of federated learning, bio-inspired optimization, and energy-aware routing for decentralized environments. For local intrusion detection at edge nodes, the proposed model uses a lightweight convolutional neural network to transmit only the model updates rather than raw data, and this ensures the user's data privacy. The integration of the Levy flight and lotus effect mechanisms enhances the exploration and exploitation balance for improved intrusion detection accuracy. Furthermore, the S-LEACH–based routing protocol is incorporated to ensure secure and energy-efficient communication between nodes and base stations. Two benchmark datasets are used to validate the proposed model. The experimental results demonstrated that the proposed model achieves a higher accuracy of 98.60%, a precision of 98.32%, and a packet delivery ratio of 92.7%. In addition, the proposed model achieves a minimum communication delay and false alarm rate. Furthermore, the statistical Wilcoxon rank-sum test is conducted to confirm the effectiveness and consistency of the proposed model across diverse evaluation metrics. The overall result demonstrates that the proposed model ensures privacy preservation, scalability, and energy efficiency, making it a robust model for real-time intrusion detection in IoT-enabled applications, including smart cold storage monitoring systems, industrial automation, and environmental sensing networks.

Severe resource constraints, multi-hop connections, variable topology, and high sensitivity of transmitted data have raised the issue of quality of service (QoS) as one of the serious issues for wireless body area networks (WBANs). Therefore, many papers have been introduced in this field to improve the important aspects of the QoS field. However, studies indicate that some important issues have not been well considered, the most important of which are the lack of measures to consider the dynamic conditions of nodes and the effective support for varying traffic QoS. In addition, the performance of most methods leads to a sharp increase in control overhead. In this paper, a QoS-Temperature-Aware energy-efficient Adaptive Routing (QTAEEAR) based on the development of the Q-learning algorithm has been introduced. QTAEEAR is a three-step method. The routing table is created and updated in the first step. In the second step, routing and selecting the next-hop node are performed based on the Q learning. In the third step, learning is updated in response to new network conditions. Simulation results using NS-2 indicated the optimal performance and superiority of QTAEEAR compared to other similar methods.

software-defined networking (SDN) offers flexible traffic management but remains vulnerable to sophisticated cyberattacks, necessitating accurate and efficient network traffic detection. Existing SDN-based intrusion detection systems often suffer from high computational cost, poor scalability, and reduced accuracy in high-throughput or encrypted environments. To address these limitations, the NTD-SDN-RICCNN framework is proposed, which integrates fast robust iterative filtering (FRIF) for noise removal with spectral graph fast Fourier transform (SGFFT) for discriminative feature extraction. Rotation-invariant coordinate convolutional neural network (RICCNN) optimized with weighted velocity-guided gray wolf optimizer (WVGWO) for parameter tuning. The proposed method reduces redundant feature processing while improving detection accuracy and inference speed. Experiments on the SDN intrusion detection dataset show that NTD-SDN-RICCNN attains 99.7% accuracy, 99.6% precision, 99.5% recall, and reduces computational time by up to 32.5% compared to the state-of-the-art baselines. These results demonstrate the method's effectiveness and scalability for real-time SDN intrusion detection in diverse network conditions.

In wireless sensor networks (WSN), the sensor nodes are deployed randomly where the sensor nodes are not positioned away from each other. The intersection of sensing ranges creates an overlapping area. Every sharing node perceives the same event and generates redundant and associated data if it takes place inside the overlapping area. In this paper, a multimetric-based optimization of cluster selection and data redundancy elimination through copula variational LSTM in wireless sensor networks (CS-DRE-CVLSTM-WSN) is proposed. Initially, cluster formation using semantic invariant multiview clustering (SIMVC) for data aggregation in WSN by leveraging data characteristics and connectivity patterns is discussed. Then, the clusters formed are given to the Wader Hunt Optimization Algorithm (WHOA) for cluster head selection. For this, a novel method to enhance data redundancy elimination efficiency, considering factors, like trust degree computation, energy efficiency, link quality, path loss, distance from the target node to the base station, and aggregation delay is proposed. The selected clusters are fed into copula variational LSTM (CV-LSTM) to optimize cluster selection and predict temporal trends in key network metrics. The offline meta RL (OMRL) framework is proposed to suppress redundant data streams and decide which data to transmit or aggregate. The reward system assigns positive values for efficient aggregation and reduced communication costs, while penalizing data loss or unnecessary transmissions. The output is a set of policies for effective data aggregation and redundancy elimination. The performance of the proposed CS-DRE-CVLSTM-WSN method is evaluated with existing methods like the reliable cluster dependent data aggregation scheme for IoT with hybrid deep learning methods (RC-DAS-IoT-HDL), new machine learning-driven data aggregation for predicting data redundancy in IoT connected WSN (ML-PDR-IoT-WSN), and two vector data prediction techniques for energy-efficient data aggregation in WSN (TVDP-EEDA-WSN), respectively.

This paper investigates an energy-limited multi-user mixed radio frequency/free space optical (RF/FSO) system, where the RF links and FSO link undergo Rayleigh fading and Gamma-Gamma turbulence models incorporating the effect of pointing errors, respectively. Simultaneous wireless information and power transfer (SWIPT) based on the power splitting protocol is employed to maintain communication continuity of the system operating in an energy-limited scenario. Moreover, an energy-based user selection strategy is adopted to enhance the overall performance. Closed-form expressions for outage probability (OP) and approximate bit error rate (BER) are provided by using Meijer-G functions. The accuracy of theoretical derivations is validated by Monte-Carlo simulations. Results demonstrate that, under the user selection strategy, merely increasing the number of users cannot significantly boost system performance. Furthermore, influenced by multiple factors, the prudent design of the power splitting proportion enables system performance optimization.

The quick propagation of Internet of Things (IoT) devices in 6th Generation (6G) networks intensifies security challenges due to high-dimensional and diverse nature of IoT data, which complicates feature selection and increases computational overhead. Dynamic and evolving attack patterns, including various intrusion types, malware, denial-of-service attempts, and coordinated botnet attacks, further reduce detection reliability. To address these challenges, Optimized Periodic Implicit Generative Adversarial networks with advanced Transformer (OPIGAT) proposes an end-to-end framework for robust and scalable IoT security. The framework begins with a preprocessing stage that handles missing values, outliers, and normalization for clean and consistent data. Feature optimization occurs through a Hybrid Tuna Particle Swarm Optimization Algorithm (HTPSO), which Merges Particle Swarm Optimization (PSO) global search capability with Tuna Swarm Optimization (TSO) spiral foraging-inspired local refinement, enabling precise selection of compact and highly discriminative features while reducing dimensionality. OPIGAT classifier detects diverse attacks, with the generative component synthesizing realistic traffic patterns and the transformer module capturing contextual relationships, enhancing anomaly detection and reducing false positives. Finally, a lightweight blockchain integrated with InterPlanetary File System (IPFS) ensures secure and scalable data management, employing proof of authority, Elliptic Curve Cryptography (ECC) with Ring signature encryption, and smart contract–based revocation to maintain privacy, integrity, and efficiency. Extensive experiments on CICIoT-2023 and ROUT-4-2023 datasets demonstrate superior accuracy (98.78% and 97%), high detection efficiency (98.21%), and a low false alarm rate (20%), while IPFS-enabled storage supports seamless scalability. These results establish OPIGAT as a secure, efficient, and highly effective solution for 6G IoT intrusion detection.