In response to sudden high-traffic signal demand caused by massive device access in urban Internet of Things environments, Unmanned Aerial Base Stations (UAV-BSs), as dynamic network nodes, can effectively enhance the coverage capacity and quality of communication network services. However, how to efficiently deploy UAV-BSs in complex urban environments while meeting the differentiated communication needs of common and special areas remains an urgent challenge. In this paper, we propose an Average Hamming Distance Modified Particle Swarm Optimization (AHDPSO) algorithm based on the similarity calculation of DNA sequences, which firstly matrices the position and velocity information, and then calculates the average Hamming distance between particles using DNA mapping sequences to identify ’outlier points’. Further, the search guidance coefficientc3is introduced to quantify the guiding effect of ’outlier points’ on the global search, and the values ofc1,c2, andc3are dynamically adjusted by combining with the chaotic mapping, so as to balance the exploratory and developmental capabilities of the algorithm. Compared with the original particle swarm optimization algorithm, matrix particle swarm optimization algorithm, and seven other improved particle swarm optimization algorithms, the experimental results show that AHDPSO can quickly converge to the optimal solution. Compared with the traditional PSO algorithm, the absolute improvement values in the coverage of the entire region and special regions are 7.82% and 7.29%, respectively. It also shows good stability in different scenarios, indicating that the proposed algorithm has significant advantages in convergence speed, coverage, and stability.

Efficient deployment of thousands of energy-constrained sensor nodes (SNs) in large-scale wireless sensor networks (WSNs) is critical for reliable data transmission and target sensing. This study addresses the Minimum Energy Q-Coverage and C-Connectivity (MinEQC) problem for heterogeneous SNs in three-dimensional environments. MnPF (Metaheuristic–Neural Network Parallel Framework), a two-phase method that can embed most metaheuristic algorithms (MAs) and neural networks (NNs), is proposed to address the above problem. Phase-I partitions the monitoring region via divide-and-conquer and applies NN-based dimensionality reduction to accelerate parallel optimization of local Q-coverage and C-connectivity. Phase-II employs an MA-based adaptive restoration strategy to restore connectivity among subregions and systematically assess how different partitioning strategies affect the number of restoration steps. Experiments with four NNs and twelve MAs demonstrate efficiency, scalability, and adaptability of MnPF, while ablation studies confirm the necessity of both phases. MnPF bridges scalability and energy efficiency, providing a generalizable approach to SN deployment in large-scale WSNs.

With the fast development of the Internet of Things (IoT), the growing amounts of data transmitted through edge devices tempt hackers to attack vulnerabilities. Because of data fragmentation and heterogeneous data distribution of IoT, attack detection models on edge devices are proficient at detecting only a limited set of specific attacks, causing a high false alarm rate when detecting new traffic data. Personalized Federated Learning (PFL) widely expands the range of detectable attacks and adapts local models to new traffic data by one step of gradient descent. However, it demands a part of the new traffic data (test-support set) with correct labels to realize adaptation, which is labor-consuming when detecting large amounts of traffic data. To solve this issue, our main idea is to find helpful models to pre-label the test-support set, we propose a novel self-labeled PFL called SOH-FL, including an autoencoder based on cosine similarity (CT-AE) to extract features and an aggregation method (BS-Agg) to tailor models for pre-labeling test-support sets depending on features extracted from edge devices. SOH-FL is evaluated in three heterogeneous scenarios using the CICIDS2017 dataset, and consistently outperforms the baselines across all metrics, achieving performance comparable to PFL without manual labeling. In the real-world feature heterogeneous scenarios of the IoT-23 and TON-IoT datasets, SOH-FL achieves accuracy improvements of 11.5% and 9.1% over the baseline, respectively. The experimental code is publicly available at https://github.com/deer-echo/SOH-FL.git.

This paper presents a novel Generalized Exponential Splatting framework for scene reconstruction. Unlike neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS) offers rapid rendering and high fidelity. However, existing 3DGS frameworks integrating RGB-D SLAM face challenges in system efficiency and aliasing artifacts. Inspired by geometric features, we first categorize the space into three regions—edge, planar, and non-planar—based on their information richness. The edge extraction is realized through an energy function, while planar and non-planar regions are segmented via a fast segmentation algorithm proposed in this paper. Secondly, a sparseness function is established to screen key points within the these regions to build a geometric points-guided Generalized Exponential Splatting model (GP-GES), strategically placing primitives to reduce redundancy. Finally, by integrating a 3D low-pass filter, a smooth GP-GES model is further built, ensuring that the frequency of the primitives adheres to the Nyquist-Shannon theorem, to mitigate high-frequency artifacts. Extensive experiments show that our method reduces the map size by 3.7× to 6× without sacrificing rendering quality, thereby accelerating the computational efficiency of the system. In addition, the segmentation algorithm has a fast detection speed, with a frame rate exceeding 90Hz.

Learning multi-scale feature representations is essential for dense prediction tasks in autonomous driving. Most existing works are based on U-shaped architectures, where high-resolution representations are progressively recovered by connecting different levels of the decoder with low-resolution representations from the encoder. We observed that rich details from low-level representation and high semantic information from high-level representations are not fully utilized in the cross-stage fusion process. Additionally, current architectures often struggle to extract efficient discriminative feature along object boundaries. To address this issue, we propose CSNet, a generic cross-stage subtraction network that extracts spatial and semantic multi-scale representations through guided contextual feature. This approach allows fine-grained features to refine deeper layers, capturing discriminative high-resolution features while filtering out redundant information. Specifically, we introduce a cross-stage subtraction module (CSM), which consists of three sub-modules: 1) a Short Path Unit, focusing on capturing complementary adjacent information; 2) Medium Path Unit for effective middle-stages features aggregation; and 3) Long Path Unit for redundant information masking and long-range context modeling. Additionally, we propose the Semantic Guided Context Reasoning (SGCR) module to reason and model contextual relations between different subtraction units. CSNet demonstrates consistent performance gains across various semantic segmentation datasets. Our model, CSNet-M, achieves 82.2% mIoU on the Camvid dataset, while CSNet-S and CSNet-M attain 79.6% and 80.5% mIoU accuracy, respectively, on the Cityscapes dataset. These results show that the proposed CSNet has the potential for enhancing real-time semantic segmentation in autonomous driving applications, offering improved accuracy and efficiency in diverse urban scenarios. The source code for this work will be published athttps://github.com/mohamedac29/CSNet.

Hierarchical Federated Learning (HFL) improves the scalability and communication efficiency of the system and achieves load balancing at each level. Incentive mechanisms enhance participant motivation and optimize resource allocation for HFL. However, existing mechanisms mainly focus on maximizing individual utility from the quantity of client data while neglecting to optimize social utility from the learning quality perspective. Meanwhile, strategic behavior and heterogeneous devices can significantly degrade the performance of incentive mechanisms. To this end, we propose a quality-aware incentive mechanism (QAIM) for HFL to improve training efficiency. Specifically, we first systematically evaluate the learning quality of clients based on their training loss and historical records, which allows us to recruit high-quality clients for model updating selectively. Then, we model the cloud-edge-end interaction and cooperation as a three-layer Stackelberg game to analyze the strategies of participants and utilize carefully designed algorithms to derive the solution of the unique Stackelberg Equilibrium (SE). Through the Pareto improvement of client association modeled as a coalition game, we can maximize social utility. Experimental results on both synthetic and real-world datasets demonstrate that our QAIM outperforms the state-of-the-art baselines, with an average increase in accuracy and social utility of 17% and 45%, respectively.

Federated learning (FL) stands as a promising distributed machine learning approach today, allowing model training on local clients without data sharing. However, varied contributions among clients often impact the global model’s performance, while FL confronts challenges like communication overhead, data heterogeneity, and privacy concerns. In this paper, we introduce a novel federated learning server aggregation algorithm: the Federated Learning Algorithm with Optimized Weight Aggregation via Particle Swarm Optimization Algorithm (AdpFedPSO). This approach dynamically adjusts client model contribution weights based on their performance and stability, aiming to enhance global model accuracy, convergence speed, and make model aggregation smarter and more adaptable. Through experimental validation on real datasets, we find that AdpFedPSO enhances accuracy by about 15% on the 0.6-Dirichlet MNIST dataset, 7.3% on the FashionMNIST dataset, and 13.4% on the CIFAR-10 dataset compared to the traditional FL aggregation method, FedAvg. These results indicate that AdpFedPSO not only enhances the accuracy of the global model but also expedites the model’s convergence speed. Additionally, it demonstrates resilience across various levels of client numbers, participation rates, and client heterogeneity, providing valuable reference and guidance for the further development of FL technology. Moreover, the concept of employing the particle swarm optimization algorithm for FL model aggregation also offers new insights and directions for future research.