International Journal of Communication Systems最新文献

Ad hoc wireless network has been one of the significant research topics, as its uses are spread over various applications. The energy-aware routing (EAR) protocols must create a routing environment with both the security and energy factors, such that the ad hoc network can be sustained for a longer period. Thus, this work proposes an EAR model by considering various factors of the ad hoc network and thus aims to improve the overall network lifetime. At first, the necessary attributes for this process are gathered from standard data sources. After that, an energy-efficient optimal routing process is performed with the support of the proposed Hybrid Dark Forest-based Gazelle Optimization Algorithm (HDFGOA) for minimizing energy consumption and maximizing the network lifespan. In this process, the multiobjective functions such as residual energy, route energy, route quality, congestion, and signal interference noise ratio (SINR) are derived by the same HDFGOA. This supports to performance of the energy-efficient optimal routing process without any interference. Moreover, this process improves the traffic capacity of the ad hoc wireless networks. Finally, thorough experiments are performed for the developed energy-efficient optimal routing process with conventional algorithms to ensure the suggested process's effectiveness.

Wireless sensor networks, or WSNs, are essential for energy monitoring, home automation, the medical industry, computing, and other fields. The two problems with wireless sensor network are the network lifetime and data transmission delay. The cluster head selection (CHS) is a difficult task. Therefore, a novel Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network (HYB-AO-AOA-CHS-WSN) is proposed in this paper. The proposed HYB-AO-AOA integrates Archimedes and Arithmetic optimization algorithm (AO-AOA). The stimulation of sensor nodes initiates the CHS process. The stimulated sensor nodes are evaluated separately for delay, energy, node degree, intercluster distance, and intracluster distance for determining the cluster heads. Then, the multipath routing is handled by the HYB-AO-AOA technique. The proposed HYB-AO-AOA-CHS-WSN model is implemented in MATLAB. The metrics, like delay, number of alive nodes, throughput, and residual energy is examined. The proposed SRD-DCGAN approach attains 25.72%, 21.32% and 19% higher throughput, 26.56%, 21.35% and 32.09% lower delay than the existing techniques.

Several changes have been implemented over the years to provide better resource management and service delivery for artificial wireless sensor networks (WSNs) that rely on the Internet of Things (IoT). Here, 5G networks offer high data rates with ultra-low latency and robust reliability, which is essential for managing the substantial data volumes generated by IoT devices in 5G WSNs. IoT needs an optimal communication network to transmit data among different devices. The whole network is categorized as heterogeneous clusters in clustering. The cluster head (CH) selection achieves proficient data communication to the sink node through the chosen CH. In this manuscript, an energy-efficient communication using auto-associative polynomial convolutional neural network in 5G WSN (EEC-HAPCNN) is proposed for improved data transmission through the selected route. Initially, clustering is done by parallel adaptive canopy k-means clustering (PaC-k-M) algorithm. Then, Tasmanian devil optimization algorithm (TDOA) selects the CH required for facilitating the high capacity and low latency features of 5G. The data are given to sink node through the selected CH utilizing hierarchical auto-associative polynomial convolutional neural network (HAPCNN) for efficient routing in 5G wireless communication network. The proposed EEC-HAPCNN method is implemented in NS-3 (network simulator 3). The proposed approach is examined using performance metrics like throughput, energy consumption, network lifetime, and number of nodes alive. The proposed EEC-HAPCNN method provides 17.45%, 17.63%, and 18.43% lesser energy consumption and 17.64%, 17.64%, 18.54%, and 19.33% greater network life time compared with existing DBN-MRFO-5G-WSN, IDCNN-t-DSBO, DACP-WSN-ANN, EEO-IWSN-ML, and EECA-ML-WSN techniques.

Wireless sensor networks (WSNs) have become essential in applications such as environmental monitoring and smart infrastructure due to their ability to provide real-time data collection and analysis. However, WSNs face significant challenges related to limited battery life and the need for efficient energy management, which can impact their performance and longevity. Traditional routing protocols often fail to adapt to dynamically changing conditions and energy constraints inherent in WSNs, necessitating innovative approaches to enhance energy efficiency and network longevity. This paper introduces a federated learning–based adaptive routing (FLAR) model designed to address these issues by integrating federated learning with adaptive routing protocols. The primary aim of this research is to optimize energy utilization across the network and extend the operational lifespan of WSNs. The novelty of the proposed FLAR model lies in its unique combination of energy-aware participant selection (EaPS), adaptive model compression (AMC), and dynamic data sampling (DDS), which collectively enhance energy efficiency and adapt dynamically to changing network environments. The FLAR model was simulated and analyzed using Network Simulator 2 (NS2) under various network conditions and node densities. The results demonstrate that the FLAR model significantly outperforms traditional protocols by reducing energy consumption by up to 30% and enhancing network longevity by approximately 25%. Additionally, the proposed methodology improves packet delivery ratio and reduces latency, making it a robust solution for sustainable WSN deployment. Overall, the FLAR model offers a significant advancement in WSN technology by effectively managing energy resources and dynamically adapting to network changes.

The Internet of Things (IoT) represents the Internet's future by including devices that can connect with one another. IoT devices in IoT networks collect data from their surroundings and send it in bursts to a server in a remote location. The quantity of applications using computer networks causes resource competition, which in turn causes congestion. The most difficult task in enhancing network quality of service (QoS) is IoT congestion control. For IoT items with modest resource requirements, various protocols have arisen. Different shortages and issues affect basic congestion control, which increases bandwidth use, data loss, and delay. Thus, to solve this issue, in this paper, we propose a bio-inspired fuzzy inference system (FIS)–based constrained application protocol (BIFIS-CoAP) for avoiding congestion over network. In order to determine the level of congestion, we use the bottleneck bandwidth gradient (BG-gradient) and round-trip time gradient (RT-gradient) as inputs for BIFIS-CoAP. By using this indication, BIFIS-CoAP may anticipate impending congestion and alter the sending rate to prevent it. In order to achieve high performance for burst data transfer, BIFIS-CoAP constantly checks for bandwidth that is available and uses the congestion degree for updating the variable retransmission time out (RTO) for retransmissions. Using the adaptive Tasmanian devil optimization method (ATDO), input parameters like the RT-gradient and BG-gradient are modified to enhance the performance of the FIS. Numerous simulation studies have shown that BIFIS-CoAP is feasible, and the simulation results show that it performs better than basic CoAP and fuzzy CoAP in terms of delay, throughput, delivery ratio, and retransmissions.

In the context of 5G vehicle-to-everything (V2X) communications, network slicing has been presented as a prominent solution to enable the coexistence of different V2X use cases within the same infrastructure. Among the main challenges in V2X network slicing is proposing an appropriate resource management approach that allows resources to be used efficiently while protecting the isolation between slices. One of the benchmarking resource management approaches is the static allocation that provides a full isolation between the slices. This static allocation has serious limitations when dealing with dynamic scenarios, particularly when some slices are overloaded. This paper proposes a deep Q-learning approach to readjust static resource allocation by enabling an opportunistic sharing mechanism in case there is an overloaded slice in the system. More specifically, the main idea of our proposal is to extract an appropriate amount of resources from each available slice within the same infrastructure when receiving rejected connection requests from overloaded slices, while taking into account the isolation aspect of the slicing environment. Simulation results showed better performance in terms of maximizing the use of resources, reducing the probability of new calls being blocked and the probability of handover dropping in the system, while maintaining high isolation compared with other solutions.

VANETs are essential for future intelligent transportation systems, enabling communication among vehicles and infrastructure. However, their decentralized nature presents significant security challenges. This paper proposes a holistic framework to address these issues, comprising three components. First, the Self-Improved Kookaburra Optimization Algorithm (SIKOA) optimizes cluster formation by considering trust, mobility patterns, and link lifetime, enhancing network efficiency and resilience. Second, an Improved Long Short-Term Memory (LSTM) model is introduced for intrusion detection, incorporating link quality and trust metrics to minimize false positives. Finally, an Improved Blowfish Algorithm is applied for data encryption, ensuring confidentiality and integrity. Experimental results in simulated VANET environments show that the proposed framework outperforms existing solutions in terms of detection accuracy, computational efficiency, and attack resilience.

In today's world, communication is critical to multi-UAV (Unmanned Aerial Vehicle) system design, enabling UAVs to collaborate and operate cohesively. UAVs generally rely on infrastructure-based communication through ground stations or satellites. However, this approach has numerous limitations, particularly in multi-UAV systems. Ad hoc networking among UAVs offers a solution by allowing direct communication without needing fixed infrastructure. This work introduces two innovative protocols: Artificial Intelligence-enabled Fully Echoed Q-Routing (AI-FEQ) and Position-Prediction-based directional MAC (PPMAC) protocols for improving the performance of multi-UAV systems. These protocols leverage AI techniques like unsupervised, supervised, and reinforcement learning to make intelligent decisions regarding topology formation, maintenance, and routing management. Furthermore, the proposed AI algorithm will enhance the development and sustainability of Flying Ad Hoc Network (FANET) topologies that help to enlarge the communication efficiency and reliability of multi-UAV systems. The simulation results reveal that the proposed AI-FEQ protocol achieves an impressive network density association of 90% and minimal data transmission latency of 4.9 s as compared to the existing protocols.

The development of the new 5G mobile generation is among the most significant and recent breakthroughs in communications and information technology. Compared to older networks, the most recent wireless cellular technology offers more capacity, dependable connections, and quicker upload and download rates. The newest generation of mobile phones, or 5G phones, have significantly quicker speeds and are more dependable. They have the potential to completely change the way we access social media, websites, and information. This study was carried out to address the issue that many 5G mobile users frequently face when deciding which phone is best for them based on many factors. This study contributes to putting out a strategy for assessing and ranking the various 5G mobile possibilities in light of consumers' order of predilections in multicriteria decision-making (MCDM). In this study, we studied 15 different 5G mobiles based on data from mobile companies with 10 criteria. First, the most appealing criteria that affect a person's decision about a 5G mobile are determined. This is accomplished by a survey of the target audience, the expertise of industry professionals in the communications area, and research in the literature. For assessing the 5G mobile alternatives, the TOPSIS approach is employed, and the relative weights of assessment criteria are calculated by the cross-entropy approach, which is new in terms of MCDM. This study demonstrates the usefulness of the suggested strategy.

The novel design, prototyping, and characterization of an omnidirectional to directional radiation pattern switching planar antenna system are presented. The incorporation of the Yagi-Uda antenna with an Alford loop antenna is proposed. The antenna is formed by a dipole, driver, reflector, and two directors. In a DC bias switching circuit with a combination of eight SMD PIN diodes and two parasitic elements, the antenna's radiation patterns can be shaped to concentrate energy in an omnidirectional radiation pattern. The introduction of switches using an SMD PIN diode at the arms of the driver element produces two switchable frequencies at 915 MHz and 2.45 GHz. Switches at the directors and reflector control the radiation pattern and can be configured to omnidirectional pattern to directional radiation pattern at those resonance frequencies. The radiation patterns as well as the simulated and measured reflection coefficients (S₁₁) are illustrated and analyzed. Return loss (S₁₁) is observed to be −36.36 dB at resonance frequency of 2.45 GHz (2.35–2.69 GHz) with bandwidth 340 MHz and impedance bandwidth 21.1%, and −24.45 dB at resonance frequency of 915 MHz (854–1006 MHz) with bandwidth 152 MHz and impedance bandwidth 16.61%. When compared with the omnidirectional pattern while maintaining the horizontal polarization, the gain of the antenna increases from 4.6 dBi with 90.02% efficiency to 5.1 dB with 87.77% efficiency in a directional pattern. The developed antenna system's final dimensions are 2.45 λ × 1.80 λ × 0.01 λ. The antenna system is tested in a nuclear reactor containment building equipped with a wireless sensor node. The signal transmission indicates the zero (0) invalid frames with 100 dBm received signal strength indication (RSSI). The printable nature of the antenna architecture and successful transmission of signals across the nuclear reactor containment building make the antenna a good candidate for the wireless sensor node (WSN) in the nuclear reactor.