Transactions on Emerging Telecommunications Technologies最新文献

The Internet of Things is growing tremendously due to new technologies, advancements, and big data. With the digitization of data and continuous technological progress, network data traffic has seen a significant increase. This growth makes IoT networks more vulnerable to attacks because of the rising number of devices and the massive amount of data they generate. One of the emerging topics in the research field is security in IoT. The enormous volume of data poses significant challenges to privacy and cybersecurity, and the frequency of attacks is directly proportional to Internet usage. Intrusion Detection Systems (IDS) have proven effective in detecting various attacks, malicious activities, and unauthorized access in IoT networks, helping to prevent intrusions. Furthermore, advanced AI technologies such as machine learning, deep learning, ensemble learning, and transfer learning have shown promising results in efficiently identifying intrusions, attacks, and malicious actions. This paper presents the development of an effective Intrusion Detection System using Machine and Deep Learning algorithms, compares their performance, and identifies the most effective algorithm for securing IoT data while preserving privacy. Random Forest, Convolutional Neural Networks, and Deep Neural Networks are implemented, tested, and compared with other machine learning algorithms, including Decision Trees, Gaussian Naïve Bayes, and XG-Boost. The implementation is carried out in Python, using the benchmark KDD dataset. This paper covers the processes of data generation, preprocessing, analysis, and intrusion detection. The experimental results are compared with other state-of-the-art methods to evaluate overall performance. The performance metrics such as accuracy, precision, recall, and F1 score have been computed for the case of deep learning and machine learning for given IoT network.

Overcrowding of tourists at scenic spots can easily lead to safety accidents and a decline in tourists' travel experience. Designing or recommending tourist routes for tourists is an effective method of passenger flow guidance. The crowdedness of scenic spots is used to describe the crowded conditions of scenic spots, and a tourism experience utility function is proposed. Based on this, considering the constraints of scenic spot service time, travel time and cost budget, a travel route optimization model based on the maximization of travel experience utility is established, and an ant colony algorithm is designed to solve it. On this basis, a time-sensitive travel route recommendation method based on dynamic transition graphs is proposed, a dynamic transition graph model method based on hierarchical clustering is constructed, a method for removing popular sequence anomalies is designed, and a stable pattern law is established. The pattern law accurately recommends the best tourist route suitable for the user's travel time. Through the experimental verification of real data, compared with the existing work, the user's income has increased by more than 10%, which verifies the effectiveness of the proposed method.

This work introduces an orthogonal frequency division multiplexing based discrete cosine transform assisted index modulation with novel signal identification technique. To take use of the design flexibility offered by the twice the number of accessible subcarriers under the same bandwidth, it combines the concepts of IM and DCT assisted Orthogonal Frequency Division Multiplexing (DCT-OFDM). The performance of DCT-OFDM-IM in contrast to OFDM-IM is enhanced in the proposed study by the employment of a deep learning detector. The Deep Learning based detector (DLD), in contrast to conventional detectors like Maximum Likelihood (ML), Greedy Detector (GD), Log Likelihood Ratio (LLR), and others, improves system performance and lowers system overhead. In order to perceive data bits at the OFDM-IM system's receiver in Rayleigh, Rician, and Nakagami-m Fading channels, the proposed DLD uses a Deep Neural Network with completely automated linking layers. To start with, DLD is trained offline by assembling datasets of simulated results in order to enhance BER performance. Next, the model is trained to recognize DCT-OFDM-IM signals at the receiver under various fading channels. The results demonstrate that the DLD outperforms conventional approaches for all multipath fading channels in terms of BER, and that BER for DCT OFDM-IM has improved over that of OFDM-IM.

The advancement of digital technologies is used for providing more enhanced healthcare services to patients in a timely and effective manner. The multi-modal data encompasses a huge amount of information when compared to the single-modal data. Fusing and analyzing various data types provides a more comprehensive understanding of the patient's condition. Fusing these multi-modal data poses several technical challenges because of its data incompatibility. Therefore, this research work focuses on implementing a deep learning-based disease prediction model using multi-modal data to generate precise prediction results regarding healthcare applications. Initially, the required multi-modal data such as signal, data and image are gathered from the standardized benchmark data sources. Then, the collected data is subjected to the implemented multi-modal data-based disease prediction network (MMPredNet). This network is developed by combining an adaptive hybrid deep learning network (AHDLN) and a multi-scale dilated residual attention network (MDRAN). Here, MDRAN performs a feature extraction process to extract the features from the input data. further, the prediction process is carried out using the AHDLN model. It is a hybridized network generated by fusing a deep Bayesian network (DBN) with a deep shallow network (DSN). The parameters of the AHDLN are optimized using the adaptive learning rate-based dove swarm optimization (ALR-DSO) algorithm to reduce FPR and enhance the precision, NPV, and accuracy of the prediction outcome. From the MMPredNet, the final disease prediction outcomes are provided. The performance of the implemented multi-modal data processing model is evaluated with various conventional methods to showcase its effectiveness in healthcare. The accuracy of the developed model on text data is 97.31%, images are 98.02%, and the signal is 97.23%, which is enhanced than the prior works. Hence, it is proved that the developed framework can accurately predict the disease at an early stage and helps to improve patient outcomes and prevent the progression of diseases in patients.

Windy conditions challenge precise payload delivery by unmanned aerial vehicle (UAV), but wind variability defined within lower and upper bounds at any instance can assist to optimize candidate release points. Therefore, at first, it is crucial to collect candidate release points caused by wind variability. In this paper, knowledge of candidate release points is drawn by applying ballistic equation. The knowledge about the points then initializes differential evolution (DE) optimization to search an optimum payload release point. Therefore, the proposed method is named as DE with knowledge-based initialization (KI), that is, DE-KI. Simulations demonstrate DE-KI's effectiveness by measuring landing error as root mean square error (RMSE) and achieve an average reduction in RMSE compared to existing methods. For instance, DE-KI outperforms two other alternative approaches by an average RMSE of $$ and $$ with varying payload weight, and $$ and $$ with varying wind speed.

Automatic monitoring of heterogeneous devices across Space-Air-Ground Integrated Networks (SAGIN) remains a significant challenge due to the complex temporal dependencies inherent in multivariate time series and the vast amount of data generated across space-based, aerial, and ground sensors. Hybrid models have proven effective for time series anomaly detection by identifying abnormal segments through high reconstruction errors, a strategy particularly valuable for multi-source data streams in SAGIN scenarios. However, these methods typically fall short in addressing the non-stationarity and noise inherent in multivariate time series, as they use fixed thresholds and lack mechanisms to adapt to changing data distributions—an issue exacerbated in SAGIN environments with widely varying network conditions. In contrast, our approach employs a dynamic threshold selection strategy that automatically adjusts based on the statistical properties of the reconstruction error, thus effectively mitigating these issues in SAGIN's dynamic environment. Consequently, these earlier models fail to extract rich differential features from both local and long-term sequences, thereby limiting detection performance—particularly under the multi-scale, distributed conditions of SAGIN. This study introduces VAML-Net, a composite architecture designed for unsupervised detection of anomalies within multivariate time series, and especially tailored to the heterogeneous data and distributed nature of SAGIN environments. The framework incorporates a Variational Autoencoder to derive compact representations from localized temporal segments, which are subsequently utilized for data reconstruction. To model extended and hierarchical temporal dependencies, the architecture integrates a multilevel LSTM configuration, enhanced with a cross-layer information aggregation mechanism, mirroring the multi-tier structure of SAGIN. Furthermore, we propose a dynamic threshold selection approach that adapts to the inherent non-stationarity and noise present in real-world time series data by continuously recalculating the threshold based on the evolving statistical properties of the reconstruction errors. Extensive experiments conducted on six anomaly detection benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art techniques.

Recently, underwater wireless communication (UWC) networks have garnered significant attention. In specific application scenarios, underwater electrocommunication technology exhibits distinct advantages over traditional acoustic and optical communication methods, emerging as a viable alternative for communication among autonomous underwater vehicles (AUVs). Most AUVs depend heavily on battery power, where the energy is highly precious. Given that the reliability of AUVs communications is tethered to limited energy storage, the imperative for energy-efficient communication strategies is paramount. The issue of power consumption control in underwater electrocommunication systems is addressed in this research by proposing an adaptive power control strategy based on transfer learning for transferring power. The method can predict the minimum voltage across the transmitting electrodes required to satisfy the communication task according to the changes in the operating environment and adjust the transmitting power level accordingly. To verify the effectiveness of this method, this paper establishes a transfer network based on simulation data obtained by finite element simulation combined with the theory and technique of transfer learning. It uses experimental samples to verify the effectiveness of this network in shallow waters. According to the findings, the transfer network outperforms the ordinary backpropagation neural network trained solely on experimental samples in terms of performance.

In recent years, the development of distributed learning systems and advancements in deep learning have led to significant improvements in music style transfer techniques. However, these improvements face significant challenges when implemented in space-air-ground integrated network (SAGIN) environments, due to issues such as high latency, limited bandwidth, and privacy concerns. This research explores a distributed, cross-domain music style transfer model based on SAGIN environments, proposing a federated learning (FL) approach to mitigate these challenges. The proposed method facilitates efficient music style transformation while maintaining high content and style fidelity, ensuring privacy protection by keeping sensitive data localized to edge devices. We analyze and compare the performance of several models on different music datasets (including classical, jazz, and rock genres), demonstrating that our method outperforms traditional centralized models in terms of latency, communication efficiency, and privacy preservation. Moreover, we present several ablation experiments, illustrating the contribution of each component in the model. The proposed method demonstrates its applicability in distributed, real-time environments, offering a solution for scalable and privacy-preserving music style transfer applications in the SAGIN framework.