IET Networks最新文献

Flying Ad-Hoc Network (FANET) is a promising ad hoc networking paradigm that can offer new added value services in military and civilian applications. Typically, it incorporates a group of Unmanned Aerial Vehicles (UAVs), known as drones that collaborate and cooperate to accomplish several missions without human intervention. However, UAV communications are prone to various attacks and detecting malicious nodes is essential for efficient FANET operation. Trust management is an effective method that plays a significant role in the prediction and recognition of intrusions in FANETs. Specifically, evaluating node behaviour remains an important issue in this domain. For this purpose, the authors suggest using fuzzy logic, one of the most commonly used methods for trust computation, which classifies nodes based on multiple criteria to handle complex environments. In addition, the Received Signal Strength Indication (RSSI) is an important parameter that can be used in fuzzy logic to evaluate a drone's behaviour. However, in outdoor flying networks, the RSSI can be seriously influenced by the humidity of the air, which can dramatically impact the accuracy of the trust results. FUBA, a fuzzy-based UAV behaviour analytics is presented for trust management in FANETs. By considering humidity as a new parameter, FUBA can identify insider threats and increase the overall network's trustworthiness under bad weather conditions. It is capable of performing well in outdoor flying networks. The simulation results indicate that the proposed model significantly outperforms FNDN and UNION in terms of the average end-to-end delay and the false positive ratio.

Devising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting-edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple-input, multiple-output (M-MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.

In recent years, wireless charging technology is widely used for sensor charging. Though carts equipped with wireless charging tools were able to reach sensor nodes and continuously recharge them, their applications are limited and could not be used in many territories such as rugged mountainous terrain and severely damaged disaster areas. With the significant cost reduction of UAVs due to the mature technology, using UAVs to replace charging carts for wireless sensor networks is a future trend. In this study, the problem of energy replenishment and path planning for large-scale wireless sensor networks by multiple UAVs is considered. First, sensor nodes are distributed evenly by using an improved k-means clustering algorithm, modelling the problem of the UAV traversal over a certain range of sensors as a travelling salesman problem. Then, a heuristic algorithm is proposed to solve the path planning of UAVs and the hovering time at each sensor. The simulations show the effect of different charging ratios r on the number of non-functional nodes and the loss of the UAV. The experimental results show that the proposed algorithm is able to plan a sensible path for the UAV, which leads to a smaller UAV energy loss and a smaller number of non-functional nodes.

Conventional Mobile Communication Systems (MCS) reliably transfer critical messages from authorised remote information sources like military bases and ground control stations in the war field. As tactical advancement grows rapidly, the challenges of transmitting tactical messages via conventional satellite methods increase the processing overhead related to cost, Line of Sight (LOS) communication, packet loss, delay, and retransmission requests. Modern mobile communication systems use Airborne Node Networks (ANN) between satellites and Mobile Ground Nodes (MGNs) as they provide many advantages in mobile communication systems. Hence airborne network reduces the burden of satellites as they are only used as relay stations. The mobility problem caused by ANN and MGNs is solved by proposing a novel constructive airborne-ground matrix algorithm known as the Who-To-Whom (WTW) matrix. With the help of this matrix acting as a reference index, it determines which nodes relate to whom at every time interval T in the tactical environment. This WTW matrix holds precise status/information about connectivity among all stakeholders in the operating environment by carefully and effectively accounting for frequent location changes. The methodology of the proposed matrix is that it contains the physical parameters of the nodes and their behaviour in the tactical environment. The WTW matrix construction algorithm discusses how the reference matrix is constructed in every aerial node to monitor the mobile ground nodes by leading them to safety and providing aerial guidance as they move along the tactical environment. The performance metrics are measured with other existing schemes, and the merits and demerits of the proposed WTW matrix are identified and discussed in detail.

According to the urgent low latency and the heavy computation tasks demands required for sixth-generation (6G) wireless networks, the authors introduce the conventional resource allocation algorithms, including the game theory, artificial-intelligence (AI) methods, and matching theory enabled framework, in which the multi-access edge computing (MEC) scheme collaborative with the cloud platform to serve the primary users (PUs) and cognitive users (CUs) for next generation multiple access (NGMA). The proposed framework allows both the PUs and CUs to offload their computation tasks in a 6G-enabled cognitive radio (CR) networks, so called cloud-assisted CR-MEC networks. In particular, the fundamentals of this conceived networks based on NGMA are first introduced. Hence, a number of methods based on the resource allocation algorithms are proposed in order to improve the quality of service for the mobile users, and reduce their transmission latency as well as the energy consumptions. Moreover, the motivations, challenges, and representative models for these conventional algorithms are described for integrated-intelligent communication and computing aided NGMA networks. Furthermore, the open issues and future research directions for this conceived networks are summarised.

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.

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.

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.

An antenna selection followed by a relay selection scheme (ASRS) is proposed for cooperative non-orthogonal multiple access (NOMA)-based network. The proposed work selects an antenna to maximise the instantaneous rate of poor channel condition users while providing good quality of services (QoS). The outage probability performance of the proposed scheme is plotted for different antenna configuration while varying the SNR and distance with variable number of antennas and available relays. The fairness factor of the proposed work is also discussed, considering three different density areas. The proposed scheme is compared with novel schemes in literature for average sum rate and average energy efficiency to show its superiority.

Infrastructure-less networks connect communication devices end-to-end by managing links and routes independent of fixed networking facilities, relying on dedicated protocols running on end-user devices. The large variety of infrastructure-less concepts and related aspects can be confusing both for beginning Ph.D. students as well as experienced researchers who wish to get an overview of neighbouring areas to their own research foci. Frequently discussed topics such as different types of sensor-, vehicular-, or opportunistic networks are covered. The authors describe different networking concepts by looking at aspects such as the main properties, common applications, and ongoing research. Furthermore, the concepts by common characteristics such as node mobility, network density, or power consumption are compared. The authors also discuss network performance evaluation by describing commonly used metrics, different evaluation techniques, and software tools for simulation-based evaluation. The references given in each section help obtain in-depth information about the presented topics and give hints about open research questions, which can be a starting point for own investigations.