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In order to further improve the accurate detection signal, reduce interference between signals, this paper designs a new type of signal detection algorithm for satellite communication systems, using stochastic resonance technology improve the signal-to-noise ratio of the input signal, the signal by using energy detection, double threshold, accurate judgment. The first step in the conventional energy of double threshold detection, the second step into the energy detection method based on stochastic resonance detection process. The experimental results show that this algorithm under the condition of low SNR signals effectively detect, promoted the whole satellite communication system performance. [ABSTRACT FROM AUTHOR]

This paper presents novel way to achieve fast and accurate Artificial Neural Network (ANN) modeling of Radio Frequency (RF) front end Low Noise Amplifier (LNA). Multilayer perceptron neural network is implemented to model high gain, ultra-low noise pseudomorhic High Electron Mobility Transistor (pHEMT) based LNA. Datasets are developed for input and output that is obtained from the Electro-Magnetic simulator. Different neural networks such as PatternNet, FitNet, and CascadeForwardNet with different algorithms are trained, tested, and compared to learn the behavior of the amplifier, and the best model is analyzed. Neural networks are modeled for LNA S-parameters and NF. It is observed that greater than 99% accuracy is achieved for PatternNet Bayesian regularization algorithm with less number of hidden layers and also it is found that the simulation results are almost similar to the developed ANN. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms require a lot of fitness calculations to get good enough solutions, which constitutes an obstacle to solving computationally complex practical problems. Recently, it has been found that surrogate-assisted meta-heuristic algorithms show potential in solving expensive complex optimization problems. This paper proposes an efficient surrogate-assisted Taguchi salp swarm algorithm (SATSSA) to solve expensive complex optimization problems. The SATSSA uses a combination of the local surrogate-assisted model (LSAM), global surrogate-assisted model (GSAM), and k-means clustering surrogate-assisted model (KCSAM) to fit the fitness function. To enhance the prediction ability of the model, an improved salp swarm algorithm with the Taguchi method (TSSA) is proposed to update and predict the model. GSAM is mainly used to capture the entire landscape of the search space. KCSAM is designed to capture part of the search space to improve the exploration capability of the algorithm. LSAM is used to capture the contours around the optimal individual. The proposed SATSSA is compared with other four excellent algorithms on 30D, 50D, and 100D benchmark functions. In addition, SATSSA is also applied to intrusion detection. Simulation results show that SATSSA is effective in improving detection rate and reducing false alarm rate. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]

Recently, the unmanned aerial vehicle (UAV)-assisted edge computing is proposed to improve the quality of service in some scenarios within sparse or unavailable base stations (BSs). Meanwhile, the caching technology is adopted to reduce the wireless traffic load and the data transmission delay. However, due to the limited storage capacity of edge nodes, the edge nodes cannot store all of the contents required by user equipment (UE). So, how to select the reasonable contents for caching on edge nodes to reduce the content delivery delay becomes a challenge in the UAV-assisted edge computing environment. In this paper, the Dijkstra algorithm based cooperative caching strategy for UAV-assisted edge computing system is proposed. Specially, the content transmission delay between two nodes is computed. Then, for each requested content, the weighted edge-undirected graph (WEUG), in which one vertex represents one node, is built. Furthermore, Dijkstra algorithm is adopted to achieve the minimal content transmission delay from the edge node caching the requested content to UE. Finally, the optimization problem of content caching is built, and the corresponding cache strategy is achieved by solving the optimization problem. The experimental results imply that the proposed cooperative caching algorithm can achieve better performance on the average content transmission delay, the average cache hit rate, and the total of hops, respectively, comparing with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Sixth-generation wireless systems not only have more demanding communication requirements, they are also expected to have high-precision sensing capabilities and sufficient computing power. Integrated sensing, communication, and computation (ISCC) can meet the above system requirements and save spectrum resources. In this paper, we build a resource allocation and offloading decision problem in an ISCC scenario that makes considerations for user mobility and partial offloading policies. The established problem minimizes the average task cost when given constraints such as the typical sensing failure rate and task completion delay. We use Lyapunov optimization theory to transform the proposed problem and propose a two-level optimization algorithm based on matching theory to offer a solution for the transformed problem. The inner layer obtains the task offloading ratio through theoretical derivation, and the outer layer determines the base station access and channel assignment based on the inner layer results. The simulation results show that the average task cost can be effectively reduced while also guaranteeing high-quality sensing performance. [ABSTRACT FROM AUTHOR]

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range. [ABSTRACT FROM AUTHOR]

This paper proposes a quantitative evaluation algorithm based on a comprehensive analysis of material's shape, texture, color, style, and other characteristics using indoor wireless networking design and cloud data to improve the application effectiveness of ecological environment materials. It also adopts a data-driven method to quantitatively evaluate the application effectiveness of ecological environment materials using indoor wireless network design based on preliminary feature information. Specifically, this work reconstructs the feature space of ecological environment materials by extracting dimensionality features related to the quantitative evaluation, and analyzes the mutual information features of big data fusion. Moreover, a quantitative regression analysis and statistical model construction is used to evaluate the application effectiveness by using preliminary feature information as a constraint condition. In addition, this study employs the spatial effect element feature analysis method to quantitatively analyze and rigorously test the application effectiveness assessment. The empirical results show that the proposed quantitative evaluation algorithm has good accuracy and high reliability, which can effectively improve the application quality of ecological environment materials in indoor wireless network design. [ABSTRACT FROM AUTHOR]

Cognitive Radio Wireless Sensor Networks (CRWSNs) promise optimized spectrum utilization but face challenges in sustaining energy balance, particularly due to the emergence of “hot spots.” In CRWSNs, Cluster Heads (CHs) closer to the sink experience higher traffic as compared to those farther away, primarily due to their role in data collaboration and relaying to the sink. This leads to early depletion of their energy reserves and potentially causing the network to partition creating hot spots or energy holes. Effective clustering algorithms are needed to mitigate these hot spots. The main objective of the paper is to propose a novel clustering scheme titled “Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm” to address hot spot issues in CRWSNs. UCEHA partitions the network into clusters based on sink proximity, selecting CHs considering node energy, communication channels, neighbors, and sink distance. An enhanced spectrum-aware AODV mechanism facilitates efficient data routing. To test and validate the proposed methodology, extensive experimentations were conducted and the results demonstrate UCEHA’s superiority over existing methods, exhibiting reduced energy consumption (average 19%), improved network load balance (average 26%), increased network lifetime (average 40%), and enhanced throughput (average 8%). These results highlight the effectiveness of UCEHA algorithm in addressing energy imbalance and hot spot issues in CRWSNs, ultimately leading to enhanced network performance and longevity. [ABSTRACT FROM AUTHOR]