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Energy Optimization in Smart Agriculture Wireless Sensor Networks Based on the RK4PSO Algorithm

IEEE SENSORS JOURNAL 2025年第8期25卷 13797-13809页

作者： Wang, Zuoxun Wu, Wangyao Sui, Jinxue Zhao, Guojian Pang, Chuanzhe Xu, Liteng Chinese Acad Sci Beijing 100094 Peoples R China Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Shandong Peoples R China

Inspired by the particle swarm optimization-based low-energy adaptive hierarchy of clusters (PSO-LEACH) protocol, this article proposes a particle swarm optimization algorithm with fourth-order Runge-Kutta (RK4PSO) algorithm, aimed at optimizing cluster head (CH) selection within the PSO-LEACH protocol, thereby reducing network energy consumption (EC). First, a fourth-order Runge-Kutta numerical integration method is introduced to reconstruct the particle motion model, significantly improving the accuracy of particle position and velocity estimation, thus enhancing the global search capability of the algorithm. Second, a population evolution mechanism based on an elite retention strategy is designed, with dynamic integration of the global optimal solution to guide the population's iterative direction, thereby improving the algorithm's convergence speed. Furthermore, adaptive individual and social cognitive coefficients are employed to adjust the particle weight distribution, further improving its robustness. Finally, simulation results demonstrate that the RK4PSO-LEACH protocol significantly improves the compactness of the cluster structure, reduces intracluster communication distance and lowers communication EC. Under the condition of a packet size of 3500 bits, the network lifetime before the death of the first node is extended by 103% and 56.4% compared to PSO-LEACH and traditional LEACH protocols, respectively, thus validating the significant advantages of the proposed algorithm in optimizing energy efficiency in wireless sensor networks (WSNs).

关键词： Wireless sensor networks Energy consumption Heuristic algorithms clustering algorithms Routing protocols Optimization Particle swarm optimization Energy efficiency Intelligent sensors Business Energy optimization fourth-order Runge-Kutta low energy adaptive hierarchy of cluster (LEACH) protocol particle swarm optimization (PSO) wireless sensor networks (WSNs)

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Compact Workspace Decomposition Based on a Bottom-Up Approach

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IEEE ACCESS 2025年 13卷 3917-3928页

作者： Skorobogatov, Georgy Calvo, Toni Barrado, Cristina Salami, Esther Tech Univ Catalonia Comp Architecture Dept Castelldefels 08860 Spain

In this paper, we present an algorithm that addresses the challenge of dividing a workspace among multiple UAVs. The workspace can be any convex or non-convex polygon and may contain holes of various shapes that represent no-fly zones. The UAVs can be heterogeneous, with different levels of autonomy, speed, and range. The goal of the workspace division is to obtain areas whose sizes are best matched to the capabilities of the UAVs while maximizing compactness. The algorithm decomposes the polygon representing the workspace into a triangular grid, followed by an iterative process of accumulating adjacent triangles while maximizing the compactness of the resulting regions. The performance of the algorithm and the quality of the partitions generated by the algorithm are compared to existing methods. Results show that this approach outperforms others in several metrics, achieving a 5% to 10% improvement in compactness, while maintaining reasonable performance, with a time overhead of up to approximately two seconds when splitting a polygon into ten parts.

关键词： Partitioning algorithms Autonomous aerial vehicles Drones Robots clustering algorithms Classification algorithms Shape Approximation algorithms Sensors Area measurement UAV multi-robot systems workspace division polygon partition

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Flight Pattern Recognition and Precision Tracking Algorithm for UAV Cluster Targets Imitating Geese Flocks

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IEEE SENSORS JOURNAL 2025年第1期25卷 709-724页

作者： Gao, Wei Rao, Bin Song, Dan Lai, Tao Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518000 Peoples R China Natl Univ Def Technol Test Ctr Xian 710106 Peoples R China

The rise of biomimetic unmanned aerial vehicle clusters has brought new challenges to radar target tracking and recognition. The purpose of flying imitating geese flocks is long-distance transport with low energy consumption. In this article, the methods of recognition and tracking are proposed for cluster targets with flight pattern of geese. The pattern recognition method, by detecting specific distance pairs according to five constraints including distance, vertical, pedal, orientation, and slope, judges whether there is a "V" characteristic in the measurement set and selects the key individuals such as the leading or following geese. Based on the pattern recognition result, the hypothetical pattern measurements are introduced into the filtering process as additional observation information, which aims to solve the problem of tracking accuracy decrease caused by high maneuverability in the formation stage of flying geese. Simulation results show that the proposed methods can not only judge the pattern of the cluster effectively and identify the key individuals but also improve the accuracy compared with the traditional tracking algorithms.

关键词： Radar tracking Target tracking Autonomous aerial vehicles Pattern recognition clustering algorithms Sensors Filtering Cluster targets filtering algorithm geese flight pattern recognition radar target tracking

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Properties of Horizontal Pod Autoscaling algorithms and Application for Scaling Cloud-Native Network Functions

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1889-1898页

作者： Do, Tien Van Do, Nam H. Rotter, Csaba Lakshman, T. V. Biro, Csaba Berczes, Tamas Budapest Univ Technol & Econ Dept Networked Syst & Serv H-1117 Budapest Hungary Nokia Bell Labs Hungary H-1083 Budapest Hungary Nokia Bell Labs Dept Network Syst Res Murray Hill NJ 07974 USA Univ Debrecen Fac Informat H-4028 Debrecen Hungary

With the growing adoption of network function virtualization, telco core network elements and network functions will increasingly be designed and deployed as cloud-native application instances. To ensure the efficient use of virtualised resources and meet diverse requirements for quality of services a resource scaling algorithm is used to scale the number of application instances up or down depending on variations in offered traffic from customers. Most of the observed performance metrics for a service are a function of the current customer traffic and the current number of application instances providing the service. The ubiquitous use of Kubernetes, the popular open-source framework for deployment and management of cloud-native functions, has resulted in variants of the Kubernetes Horizontal Pod Autoscaling (HPA) algorithm being widely used to change the number of application instances providing network functions as traffic demands vary. This change is done by determining whether a selected performance metric of interest is outside a range set by two input parameters (the desired metric value and the tolerance parameter). In this paper, we investigate the characteristics of the HPA algorithms and prove that there are only a finite number of intervals for its tolerance parameter. Further any choice of the tolerance parameter from each interval leads to similar computational decisions on the recommended number of application instances. As a consequence, the number of parameter setting choices is finite due to the rule that the desired metric value can only be an integer in specific ranges. Additionally, we investigate the use of HPA for scaling application instances that provide session-based services and establish lower and the upper bounds for the performance of the HPA scaling algorithms in this scenario. Our contributions can help operators find appropriate parameter settings efficiently - administrators of Kubernetes clusters only need to select parameters from

关键词： Measurement Cloud computing clustering algorithms Prediction algorithms Containers Heuristic algorithms Software algorithms Servers Q-learning Surveys Network functions visualization resource management kubernetes horizontal pod autoscaling algorithm metrics

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A Priority-Based Multi-Robot Search Algorithm for Indoor Source Searching

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10457-10469页

作者： Wang, Miao Xin, Bin Jing, Mengjie Qu, Yun Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

It is extremely important to quickly locate the source of a hazardous substance leak in order to reduce damage to life and property. Multi-robot source localization faces challenges in unknown indoor environments, such as navigating through dense environments, encountering large areas without airflow or concentration clues, experiencing frequent changes in robot measurements, and managing clusters of robots in confined spaces. This study proposes a priority-based multi-robot search algorithm to tackle these challenges. The algorithm consists of a priority-based search strategy, an exploration method based on frontier and Voronoi diagram, an airflow tracking method based on Rapidly-exploring Random Trees Star (RRT*), and a multi-robot collaborative method. The algorithm was compared with three other state-of-the-art algorithms in simulated environments, assessing varying team sizes, airflow speeds, and diverse scenarios. The algorithm was also evaluated in real-robots experiments. The evaluation results demonstrate that the algorithm exhibits outstanding performance in both simulated and real-robots experiments. Note to Practitioners-The aim of this study is to propose a multi-robot search algorithm designed to address the challenges encountered in source searching within unknown indoor environments. These challenges include navigating through dense environments, large areas without airflow and concentration clues, frequent changes in robot measurements, and the clusters of robots in confined spaces. This study proposes a priority-based multi-robot search algorithm. The core idea of the algorithm is to enable robots to adopt different search methods depending on their measurements. When lacking valid airflow and concentration measurements, robots engage in spatial exploration using an exploration method based on frontier and Voronoi diagram to increase the likelihood of encountering the plume. When robots are within a plume, they rely on an RRT*-based airflow trackin

关键词： Robots Robot sensing systems Navigation Atmospheric modeling Indoor environment Dispersion clustering algorithms Search problems Collaboration Position measurement Multi-robot source searching indoor environment rapidly-exploring random trees frontier-based exploration

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Semantic Segmentation of High-Resolution Remote Sensing Imagery via an End-to-End Graph Attention Network With Superpixel Embedding

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 7236-7252页

作者： Tang, Ying Hu, Xiangyun Ke, Tao Zhang, Mi Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China

Semantic segmentation of high-resolution remote sensing images is crucial in ecological evaluation, natural resource surveys, etc. Compared with CNN-based and transformer-based methods, graph neural networks (GNNs) have drawn increasing attention because they can flexibly model topologies of arbitrary irregular objects on graphs. Researchers typically use superpixels as graph nodes to reduce image noise and computational complexity. However, most superpixel-based GNN methods view superpixel segmentation as a data preprocessing step. This results in fixed graphs input to GNNs and overlooks the effects of undersegmentation. In addition, these methods often employ one graph construction approach, which makes them susceptible to interclass similarity (ICS) or intraclass variability (ICV), leading to segmentation inaccuracies. To address these issues, we propose an end-to-end graph attention network with superpixel embedding (SEGAT) to achieve semantic segmentation with well-delineated boundaries. We first use a learnable neural network, the superpixel generation module (SGM), to generate superpixels, which is cotrained with subsequent graph segmentation module (GSM) to refine boundaries continuously. Dynamically fine superpixels produce dynamically optimized graphs and mitigate undersegmentation errors. To reduce the interference of ICS and ICV, we then use the GSM to construct local and global graphs based on superpixel spatial positions and feature similarity, respectively, and update superpixel features and graph structure. Finally, updated superpixel features are classified for superpixel-wise classification, which is then mapped back to pixel features through the pixel-superpixel association map. Extensive experiments on three datasets, Vaihingen, Potsdam, and UAVid, demonstrate that SEGAT can outperform state-of-the-art methods.

关键词： Transformers Semantic segmentation Feature extraction Semantics GSM clustering algorithms Shape Image edge detection Decoding Computational modeling Graph attention networks (GATs) remote sensing (RS) semantic segmentation superpixel

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Identification of Modal Parameters Using an Improved Sparse Blind Source Separation Method

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IEEE ACCESS 2025年 13卷 73903-73915页

作者： Yu, Gang Wang, Aoran Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China

During the past decade, blind source separation (BSS) method has become an effective tool to characterize and identify modal parameters of linear systems. However, in practical engineering, the assumptions of guaranteeing conventional BSS method successful application cannot be frequently satisfied, which often lead to some challenging issues. One of these challenges is how to tackle the challenge of modal identification in situations of under-determination, which means that the total sensors should be not more than that of the active modals. In this paper, we explore an efficient under-constrained BSS approach called sparse BSS (SBSS) for addressing this problem. The drawbacks of conventional BSS are first listed and an improved SBSS method is proposed to deal with the above mentioned problem, which is shown to be more suitable for engineering applications. A 5-degrees-of-freedom numerical system and two analyses are employed to confirm the effectiveness of the suggested approach. The identified outcomes of modal parameters show highly satisfied accuracy via comparative analysis, which illustrates that the proposed technique has a potential application in structural engineering.

关键词： Time-frequency analysis Sensors Blind source separation Vectors Shape Estimation Principal component analysis Noise clustering algorithms Sensor arrays Modal identification sparse blind source separation under-constrained blind source separation

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A Cylinder Head Identification and Localization Method Using OFS-GMS Feature Matching and Minimum Surrounding Contour

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Chen, Lin Li, Lulu Pan, Haihong Wan, Zhenhua Chen, Jiachun Guangxi Univ Sch Mech Engn Nanning 530000 Peoples R China

Rotational offsets, oil reflections, and noise interference pose significant challenges to cylinder heads identifying and localizing. In this article, we propose a recognition and localization method that integrates oriented FAST (oFAST), speeded-up robust features (SURFs), and grid-based motion statistics (GMSs) with minimum surrounding contours. First, the oFAST extracts feature points from the cylinder head image, and a 64-D SURF descriptor is generated for these feature points to obtain initial matching results. Second, the GMS algorithm performs grid segmentation and eliminates the mismatched points. Then, an adaptive threshold key point evaluation method is applied to refine the feature points and eliminate residual edges. Finally, the pose information of the cylinder head is obtained by background suppression and minimum surrounding contour (BSMSC) method. We conduct cylinder head recognition and localization experiments on the self-developed sorting robot experimental platform. Experimental results show that the OFS-GMS matching recognition algorithm has an average recognition accuracy of 99%. The BSMSC-based pose estimation method operates with an average processing time of 48.14 ms and a maximum angular error of less than 1.5 degrees.

关键词： Head Feature extraction Pose estimation Accuracy Magnetic heads Lighting Sorting Robustness clustering algorithms Noise Cylinder head feature matching grid-based motion statistics (GMSs) minimum surrounding contour pose estimation

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Distributed RAN Slicing Based on MATD3 Joint With Evolutionary Game Assisted User Association for MEC-Enabled HetNets

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 260-276页

作者： Chen, Geng Mu, Xinzheng Liang, Hongjia Zeng, Qingtian Zhang, Yu-Dong Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao 266590 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England

To cope with the dramatic growth of future network traffic and the diversity of services, beyond fifth-generation (B5G) and sixth-generation (6G) wireless communication need to balance the network load while being service-oriented. In this paper, we consider a multi-access edge computing (MEC) driven radio access network (RAN) slicing scenario in a heterogeneous cellular network (HetNet) with local traffic overload. First, since users are generally non absolutely rational when selecting base stations (BSs), an evolutionary game (EG) based user association (UA) scheme is proposed to solve the problem of overload. Specifically, we innovatively define the load function of the base stations (BSs) and combine resource capacity of BSs to form the payoff function to dynamically adjust the load. Second, we model the network slicing (NS) problem using the transmission rate, average latency and quality of service (QoS). The problem is further relaxed and an NS algorithm based on distributed successive convex approximation (DSCA) is presented to derive a theoretical upper bound reference value as a static offline criterion. Finally, considering the high randomness of user task arrival in the real scenario, we formulate the multi-base station slicing problem as a stochastic game (SG) and a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based distributed network slicing algorithm is proposed to obtain excellent slicing strategies. Simulation results show that our proposed UA algorithm has a unique evolutionary equilibrium (EE) solution and is highly scalable. The proposed MATD3-based NS algorithm has better performance compared to other baseline algorithms and converges to a utility value that best approximates the theoretical upper bound.

关键词： Heuristic algorithms Bandwidth Quality of service Games Network slicing Wireless communication Resource management Load management clustering algorithms Upper bound User association network slicing evolutionary game distributed successive convex approximation MATD3

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CCO: A Cluster Core-Based Oversampling Technique for Improved Class-Imbalanced Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1153-1165页

作者： Mondal, Priyobrata Ansari, Faizanuddin Das, Swagatam Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Supervised classification problems from the real world typically face a challenge characterized by the scarcity of samples in one or more target classes compared to the rest of the majority classes. In response to such class imbalance, we propose an oversampling technique based on clustering, aiming to populate the minority class with synthetic samples. This approach capitalizes on the notion of "Cluster Cores," representing locally dense regions within clusters. These Cluster Cores act as central, densely crowded areas that capture intricate topological properties of the corresponding clusters, especially in complex datasets with a non-convex spatial orientation in the feature space. By concentrating on these high-density regions, our clustering-based oversampling technique generates synthetic samples within the convex hull region of minority class instances in the formed clusters. This strategy ensures the creation of points that align with the data space and considers each minority instance within a specific cluster, thereby averting the problems encountered due to the generation of artificial samples by mere linear combination of the minority class data points, as is encountered in SMOTE (Synthetic Minority Oversampling Technique)-based algorithms. To assess the efficacy of our proposal, we conducted experimental comparisons against several cutting-edge algorithms, considering an array of evaluation metrics on well-known datasets used in the literature for both binary and multi-class classification. Additionally, we undertook a detailed ablation study, scrutinized existing algorithms in our context, delineated their strengths and limitations, and contemplated potential research directions in this domain.

关键词： clustering algorithms Noise measurement Interpolation Noise Computational intelligence Classification algorithms Task analysis Classification imbalanced data oversampling synthetic minority oversampling technique

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