IET Communications最新文献

As massive distribution automation terminals connect and data is acquired at high frequencies, the demand for low-latency processing of distribution service data has increased dramatically. Edge clusters, integrating multiple edge servers, can effectively mitigate transmission delays. Cloud-edge fusion leverages its data processing capabilities and the real-time responsiveness of edge computing to meet the needs of efficient data processing and optimal resource allocation. However, existing access methods for distribution automation terminals in cloud-edge fusion architectures exclusively depend on either cloud or edge computing for data processing. These conventional approaches fail to incorporate critical aspects such as: adaptive access mechanisms for edge clusters of distribution automation terminals, flexible strategies including data offloading, knowledge sharing among edge clusters, and load awareness capabilities. Consequently, they demonstrate significant limitations in achieving deep fusion between cloud and edge computing paradigms. Additionally, they lack consideration for the perception of global information and queue backlog, making it difficult to meet the low-latency data transmission requirements of distribution automation services in dynamic environments. To address these issues, we propose an adaptive access method for edge clusters of distribution automation terminals based on cloud-edge fusion. Firstly, a data processing architecture for adaptive access of distribution automation terminal edge clusters are designed to coordinate terminal access, data processing distribution, and decision optimization for computing resource allocation, enabling efficient data transmission and processing. Secondly, an optimization problem for adaptive access in edge clusters of distribution automation terminals is formulated, aiming to minimize the weighted sum of total queuing delay and load balancing degree. Finally, a federated twin delayed deep deterministic policy gradient (federated TD3)-based edge cluster adaptive access method for distribution automation terminal is proposed. This approach integrates model parameters from edge servers at the cloud level and distributes them to the edge cluster level, learning strategies for terminal access, data processing allocation, and computing resource allocation based on queue backlog fluctuations. This enhances load balancing between the distribution terminal layer and edge layer, achieving collaborative optimization of load balancing and delay under massive distribution terminal access. Simulation results demonstrate that the proposed method significantly reduces system queuing delay, optimizes load balancing, and enhances overall operation efficiency.

This paper presents a novel method for optimising intrusion detection systems (IDS) by using two powerful techniques, namely ‘Principal component analysis (PCA)’ and ‘Particle swarm optimisation (PSO).’ Furthermore, the proposed approach is implemented on two categories of classifiers, Neuro-Fuzzy and support vector machines (SVM), which function on four widely used intrusion detection system datasets: CAIDA, DARPA, NSLKDD, and ISCX2012. Performance results are analysed individually based on a set of established evaluation criteria. Furthermore, the PSO algorithm is applied in search of the best combination of the outputs from the Neuro-Fuzzy and the SVM models, resulting in better attack detection accuracy with reduced false alarm rates. Another benefit of using PCA in the proposed method is that it considerably reduces the dimensions of the data by computing the principal components. This offers several advantages, such as reduced model complexity, training and execution time, memory usage, and model overfitting prevention. By focusing on the major components, PCA reduces noise in data to a certain extent, leading to increased classification accuracy and robustness. It also improves model interpretability by highlighting the key components. The application of PSO to find the most optimal parameters leads to the optimisation of the Neuro-Fuzzy and SVM models' parameters. The results achieved support that the proposed method for output combination in both Neuro-Fuzzy and SVM categories significantly enhances the accuracy of attack detection while reducing the false alarm rate.

The proliferation of the Internet of Things (IoT) has reshaped industries based on seamless connectivity. However, it has also brought about immense security challenges, especially in the communication protocol of routing protocol for low-power and lossy networks (RPL). One of these security threats vital to the RPL-based IoT networks includes the Clone ID attack on malicious nodes when they clone the identity of legitimate nodes to access their sensitive data without authorization. Detecting Clone ID attacks in RPL-based IoT networks is complex because network traffic data has high dimensions and substantial data imbalances while facing limited resources in these environments. The unmanaged control message system and insufficient identity authentication methods within the RPL protocol directly expose networks to state-of-the-art cyber security threats. This paper proposes a new edge layer-based deep neural network (DNN) approach to detect Clone ID attacks from IoT sensor networks by network traffic pattern analysis. The proposed method is based on deep data features to distinguish legitimate nodes from cloned nodes and improve the overall security, resilience, and operational efficiency of RPL-based IoT networks. To check the efficiency of our proposed method, we designed a synthetic dataset called CID-RPL. The CID-RPL dataset consists of 25 attributes and 2,131,328 samples. The experimental results are best to describe that our proposed approach outperformed the previously designed methods by offering an accuracy improvement of 5.06%, precision improvement of 7.60%, recall increment of 7.0%, and F1 score enhancement of 11.0%. Similarly, residual energy at the network level increased by 32.84%, which infers that the lifetime of the network will be extended and its energy efficiency increased under attack situations. Thus, the results testify to the effectiveness of the DL-based solution proposed herein to detect Clone ID attacks in dynamic and evolving network environments.

Reconfigurable intelligent surface (RIS)-based communication has emerged as a novel concept that can transform signal in a cost-effective and energy-efficient manner. However, the RIS system is confronted with the following several problems. First, the substantial number of reflecting elements complicates the estimating of channel state information (CSI). Second, passive RIS systems merely reflect signals to the receiver. The enhancements in bit error rate (BER) performance are insufficient. Third, the existing RIS systems only consider the influence of noise at the receiver. Nevertheless, in reality, the signal is affected by noise on both RIS and receiver. To address these limitations, a noncoherent reflecting modulation (NRM) system is designed in this paper. In the NRM system, the active RIS is adopted. It modifies not only the phase but also the amplitude, which significantly enhances the BER performance. Energy signals and differential techniques are employed, allowing the system to function without any CSI at the transmitter, RIS, or receiver. The simulation results demonstrate that NRM exhibits a 9 dB improvement in BER performance and exhibits superior noise resistance compared to the existing differential RIS system. The upper bound of the average symbol error probability is derived. Extensive simulations validate the superiority of the NRM scheme in scenarios such as 6G.

The cell-free massive multiple input multiple output (CF-mMIMO) approach, due to its high coverage and the ability to attenuate the large-scale fading impacts in wireless communications, has drawn a lot of attention. Additionally, because of their movement ability, low power, and low-cost employed infrastructures, unmanned aerial vehicles (UAVs) are considered a promising technology to provide service on demand in various applications, deployed as either base stations (BSs) or user equipment (UEs). This paper considers a UAV-equipped CF-mMIMO wireless network, assuming millimetre-wave (mmWave) connections between UAV-BSs and ground users. Leveraging the additive quantisation noise model (AQNM), closed-form expressions for the uplink data rate and energy efficiency (EE) under maximum ratio combining (MRC) detection are obtained. The impacts of effective parameters, including the UAV's altitude, the number of antennas, and the resolution of analogue-to-digital converters (ADCs), on system performance are investigated. Simulation results demonstrate that EE can be optimised for these factors to achieve the maximum value. In addition, the optimal number of quantisation bits to maximise EE is based on the number of antennas and the height of the UAVs. Comparing analytical results with accurate ones shows the accuracy of our approximations due to the same trend of variations.

This paper investigates the momentary extraction of a part of the frequency components of the signal resulting from the frequency modulation process. Based on this analysis, a new method for extracting frequency information is proposed. The changes of the instantaneous periods of the FM signal are investigated, and a simple method to determine the instantaneous frequency of the signal in the time domain is proposed. The proposed method was evaluated through simulations and compared against conventional FM demodulation techniques. In similar SNR, the relative error power of the proposed method is approximately 2 dB less than other conventional types of FM demodulators.

The real-time reconfigurable characteristics of meta-surface antennas can be used to separate multi-stream signals under the condition of single radio frequency (RF). However, with the increase of the symbol rate and the number of antenna arrays in the future, it will face the problem that the state switch rate of the electromagnetic unit is not enough to reach the upper limit of array effective degrees of freedom (DOF) of the meta-surface antenna. To solve this problem, a theory of asynchronous control meta-surface antenna is proposed in this paper. By designing the starting time of different element state switching, different electromagnetic element states are staggered to improve the array effective DOF of the meta-surface antenna. Then, an electromagnetic unit state design algorithm of asynchronous control meta-surface antenna based on the minimum condition number of equivalent channel matrix is proposed. We improve the sparrow search algorithm to solve the condition number minimization problem in order to obtain the better multi-stream signal separation performance. The simulation results show that compared with the synchronous control meta-surface antenna, theory proposed in this paper can improve the effective DOF of array under the condition of limited switch rate, and can effectively reduce the receiving bit error rate and improve spectral efficiency when separating multi-stream signals.

Intelligent vehicles require extensive data processing to enhance safety and improve driver comfort. With limited onboard computing resources, these vehicles often offload tasks to nearby vehicles or servers for auxiliary processing to meet real-time response requirements. However, the complexity and highly dynamic nature of the vehicular environment render the design of effective offloading strategies. While existing approaches can adapt to changes in environmental parameters within vehicular networks, they are fundamentally limited by their inability to process variable-dimensional environmental information and make decisions that scale with network size. Traditional methods typically rely on fixed-size input representations and static computational frameworks, which are inherently unsuitable for the dynamic and scalable nature of real-world vehicular networks that require adaptive responses to varying network sizes. As a result, existing alternatives lack feasibility to highly dynamic real-world vehicle networks that require adaptive responses to varying network sizes. To alleviate this limitation, we develop an original approach to address the task offloading and resource allocation problem with a scalable size, via a framework based on a graph neural network (GNN). Leveraging its neighbour aggregation mechanism, GNN effectively adapts to varying-scale topologies in dynamic vehicular networks, ensuring robust performance regardless of network size. To evaluate our proposed approach, we conducted extensive simulations to analyse its performance. The experimental results demonstrate that our method provides a more scalable and real-time capable solution, surpassing existing approaches by seamlessly handling dynamic network size variations.

One of the basic challenges that the sixth-generation (6G) telecommunication is facing is the possibility of wide coverage to users in diverse geographical environments. Due to environmental events and unforeseen events such as problems with ground base stations, sometimes the services of these ground stations are disrupted, and as a result, to serve users and cover them, a drone base station (DBS) must be used. But the cost of creating the infrastructure for DBS is very high, and it should be possible to provide the maximum coverage for users with the least number of DBS. Therefore, the location of DBS is very important. Also, in order to provide services to users with the best quality, DBSs should be placed optimally in such a way that power consumption is minimized. In this research, we have presented a scheme based on the grasshopper optimization algorithm (GOA) for the optimal location of DBSs in IoT networks. Specifically, we conducted a performance analysis of one distinct scenario. Employing various intelligence strategies, the proposed method has been very successful in the optimal location of DBSs compared to other methods, and it has performed better in terms of power consumption.

The proliferation of the Internet of Things has precipitated an escalating demand for indoor positioning and navigation systems that exhibit a confluence of heightened precision and economic viability. However, non-line-of-sight has an impact on the accuracy of ultra-wideband indoor location. To address this issue, we proposed a multilevel optimised algorithm based on particle filter and Bayesian unscented Kalman filter (PF-BUKF) to approach the nonlinear state and then achieve accurate three-dimensional position estimation. This approach comprises two stages. Firstly, the PF is utilised to determine the tag's coordinate's state vector and covariance as the initial optimised values. Then, the results are employed as the prior information for BUKF in order to anticipate the state of tag. The process of two steps utilises discrete points to approach the true state, which enhances the robustness and accuracy of the positioning system. Furthermore, we investigated the effect of time step size on the precision of positioning. Experimental results reveal a substantial improvement over traditional positioning methods, with mean absolute error and root mean square error values of 8.84 and 2.70 cm, respectively, as opposed to 19.02 and 8.45 cm using conventional algorithms in a nonlinear system.