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A Computation Decomposition Strategy for Optimization-Based Coordination of Automated Vehicles in Confined Sites

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 558-568页

作者： Kojchev, Stefan Hult, Robert Kneissl, Maximilian Fredriksson, Jonas Volvo Autonomous Solut S-41873 Gothenburg Sweden Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden

Coordinating the motion profiles of fully automated vehicles in confined sites presents a challenge, particularly in avoiding conflicts in MUTually EXclusive (MUTEX) zones like intersections, merge-splits, and narrow roads. Prior methods have utilized optimization-based formulations, where the scheduling (order) of vehicles in MUTEX zones is found using a heuristic, followed by solving a nonlinear program (NLP) to determine the optimal state and control trajectories of each vehicle given the schedule. This paper presents a method that reduces the computational demand in the second step by decomposing the NLP into multiple smaller, parallelly solvable, NLPs. The method uses the confined area's road network geometry and the vehicle positions to identify non-significant MUTEX relations and leverage results from graph theory to use this information to identify independent subproblems. We demonstrate that the solution obtained using this approach is equivalent to that obtained by solving the original NLP. Furthermore, by utilizing the dual variables connected to the MUTEX enforcing constraints, the method is capable of a further, sub-optimality-inducing, subdivision of the problem, enabling a trade-off between optimality and computation. We show how the method can be utilized, both when the plan needs to be initially computed and when there is a need for updating an existing motion plan. Simulation examples demonstrate the computational improvement with respect to the non-decomposed problem.

关键词： Roads Trajectory Complexity theory Safety Optimal control Heuristic algorithms Graph theory Vehicle dynamics partitioning algorithms Intelligent transportation systems Autonomous vehicles cooperative systems graph theory motion control multi-agent systems optimization

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Gecko: Efficient Sliding Window Aggregation With Granular-Based Bulk Eviction Over Big Data Streams

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 698-709页

作者： Li, Jianjun Deng, Yuhui Huang, Jiande Yi, Zhou Yang, Qifen Min, Geyong Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China Columbus State Univ TSYS Sch Comp Sci Columbus GA 31907 USA Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF England

Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of O(1) for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose Gecko - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.

关键词： Streams Windows Out of order Binary trees Throughput Aggregates partitioning algorithms Time complexity Indexes Computer science Bulk eviction incremental computation out-of-order data streams sliding window aggregation stream algorithm

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Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第7期23卷 7712-7727页

作者： Lyu, Jiangbin Chen, Xu Zhang, Jiefeng Fu, Liqun Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Shenzhen Res Inst Sch Informat Xiamen 361005 Peoples R China

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to provide wireless connectivity for ground users (GUs) in various emergency scenarios. However, it is a NP-hard problem with exponential complexity in M and N , in order to maximize the coverage rate of M GUs by jointly placing N ABSs with limited coverage range. The problem is further complicated when the coverage range becomes irregular due to site-specific blockages (e.g., buildings) on the air-ground channel, and/or when the GUs are moving. To address the above challenges, we study a multi-ABS movement optimization problem to maximize the average coverage rate of mobile GUs in a site-specific environment. The Spatial Deep Learning with Multi-dimensional Archive of Phenotypic Elites (SDL-ME) algorithm is proposed to tackle this challenging problem by 1) partitioning the complicated ABS movement problem into ABS placement sub-problems each spanning finite time horizon;2) using an encoder-decoder deep neural network (DNN) as the emulator to capture the spatial correlation of ABSs/GUs and thereby reducing the cost of interaction with the actual environment;3) employing the emulator to speed up a quality-diversity search for the optimal placement solution;and 4) proposing a planning-exploration-serving scheme for multi-ABS movement coordination. In particular, the locations of ABSs/GUs are converted into grid pattern representations, whose dimension and associated DNN complexity are invariant with arbitrarily large M and/or N . Moreover, the virtual emulator-planning combined with the actual site-deployment effectively compensates for the prediction errors due to model approximation. Numerical results demonstrate that the proposed approach significantly outperforms the benchmark Deep Reinforcement Learning (DRL)-based method and other two baselines in terms of average coverage rate, training time and/or sample efficiency. Moreover, with one-time training, our proposed method can be applie

关键词： Autonomous aerial vehicles partitioning algorithms Optimization Deep learning Artificial neural networks Base stations Training Aerial base station user mobility movement control site-specific channel spatial deep learning

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A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第4期60卷 5317-5330页

作者： Xue, Lin Niu, Tao Ge, Huaichang Zhang, Jiahui Xue, Yixun Fang, Sidun Chen, Guanhong Wang, Zhirou Chongqing Univ Dept Elect Engn Chongqing 400044 Peoples R China Shanxi Energy Internet Res Inst Taiyuan 030000 Shanxi Peoples R China

To address the voltage violation problem caused by large numbers of electric vehicles (EVs) accessing community distribution networks, as well as the large investments in conventional energy storage and difficulties in EV scheduling, this paper proposes a joint distributed optimization framework for voltage control and emergency energy storage vehicle (EESV) scheduling in community distribution networks. First, a Louvain adaptive partitioning algorithm considering regional coupling and equilibrium is employed to divide the network into several independent subareas and reduce the scale of the original problem. Then, an EESV transportation routing model and the distribution network voltage/Var control are co-optimized based on the network partition result, which aims to minimize the actual power loss, voltage deviation degree, and EESV operation cost. Moreover, an improved ADMM algorithm with variable penalty parameters is used to improve the solution efficiency. Finally, the results of two modified distribution network systems demonstrate that the proposed framework can effectively reduce the network loss and voltage deviation compared with other methods in the literature and guarantee fast convergence.

关键词： Voltage control Distribution networks Optimization Costs partitioning algorithms Scheduling Routing Community distribution network emergency energy storage vehicle (EESV) joint distributed optimization voltage control

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A Distributed Higher-Order k-Medoids Clustering Algorithm for Network Partition

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4181-4193页

作者： Mo, Yuanqiu Xing, Ran Hou, Huazhou Southeast Univ Nanjing 211189 Peoples R China Univ Sydney Sydney NSW 2006 Australia Purple Mt Labs Nanjing 211102 Peoples R China

In this paper, we develop a distributed higher-order k-medoids clustering algorithm for networks using hop count as the distance metric, typical examples include social networks, wireless sensor networks and etc. Different than the classical k-medoids clustering where each cluster is assigned with one medoid (representative nodes in the network), the proposed algorithm is capable of partitioning the nodes into clusters dominated by multiple medoids. In this algorithm, a higher-order shortest path algorithm is first adopted to assign each node the clustering set comprising of its h (h >= 1) closest medoids using the hop count metric, then an aggregation algorithm collects the local information from each node to the medoids in its clustering set, based on which a medoids update mechanism helps each medoid decide whether to keep its medoid status or assign the status to one of its neighbors, so as to improve the clustering quality. The clustering performance of the proposed algorithm is proved to monotonically converge to a local optimum after running the algorithm finite times. Simulations results show that the proposed algorithm can achieve superior performance compared with several centralized $k$-medoids clustering algorithms and scale well as the number of nodes increases.

关键词： Clustering algorithms partitioning algorithms Length measurement Wireless sensor networks Social networking (online) Search problems Prototypes k -medoids clustering higher-order clustering graph algorithms

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Adaptive Task Assignment in Spatial Crowdsourcing: A Human-in-The-Loop Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2726-2739页

作者： Wu, Qingshun Li, Yafei Yan, Jinxing Zhang, Mei Xu, Jianliang Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

In recent years, adaptive task assignment has been explored in spatial crowdsourcing. The challenge lies in how to adaptively partition the task stream to achieve the best utility for task assignment. A number of existing works have attempted to solve this challenge and achieve better performance by utilizing learning-based methods. Specifically, they mainly employ reinforcement learning to divide the task stream into a series of suitable batches and then perform task assignment in a batch fashion. Drawing inspiration from the effectiveness of human-machine collaborative decision-making, we aim to investigate human-in-the-loop methods to further enhance the performance of adaptive task assignment. In this paper, we propose a novel framework called Human-in-the-Loop Adaptive Partition (HLAP), which consists of two primary modules: Reinforcement Learning Partition Decision (RL-PD) and Human Supervision and Guidance (HSG). In the RL-PD module, we develop an RL agent, referred to as the decision-maker, by integrating the dual attention network into the Deep Q-Network (DQN) algorithm to capture cross-dimensional contextual information and long-range dependencies for a better understanding of the environment. In the HSG module, we design a human-in-the-loop mechanism to optimize the performance of the decision-maker, focusing on addressing two key issues: when and how humans interact with the decision-maker. Furthermore, to alleviate the heavy workload on humans, we construct a supervisor based on RL to oversee the decision-maker's partition process and adaptively determine when human intervention is necessary. We conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency and effectiveness of the HLAP framework.

关键词： Human in the loop Crowdsourcing Roads Reinforcement learning Mobile computing Learning systems partitioning algorithms Costs Symbols Real-time systems Human-in-the-loop location-based services reinforcement learning spatial crowdsourcing task assignment

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Field-Based Security Testing of SDN Configuration Updates

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Malik, Jahanzaib Pastore, Fabrizio Univ Luxembourg SnT Ctr L-4365 Esch Sur Alzette Luxembourg

Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify;for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.

关键词： Clustering algorithms Testing Security Software Nearest neighbor methods Anomaly detection partitioning algorithms Inspection Software algorithms Machine learning algorithms Field-based testing software defined network (SDN) security testing upgrades testing

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Handling Multiobjective Optimization Problems With Complex Constraints: A Constraints Grouping-Based Approach

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 3866-3880页

作者： Xu, Biao Zheng, Yiwu Li, Wenji Gao, Xiaozhi Gong, Dunwei He, Jie Fan, Zhun Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Univ Eastern Finland Dept Comp Sci Joensuu 80101 Finland Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266100 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Real-world production scenarios often involve multiobjective optimization problems with intricate constraints. Although there has been a growing interest in multiobjective problems with complex constraints, such as the vehicle routing problem with time windows, existing multiobjective evolutionary optimization techniques still face significant challenges, particularly when addressing the fragmented and narrow feasible regions that arise from these constraints. Our research introduces a refined framework tailored for complex constrained multiobjective evolutionary optimization. The methodology conducts an initial strong-weak analysis to categorize constraints and merges each strong constraint with all weak constraints to form subsets. Each subset, combined with the original objective functions, defines a subproblem. Independent optimization of the original problem and subproblems is carried out by utilizing multiple populations. Information acquired from the subproblems' populations is transferred into the population of the original issue, thereby expediting the detection of the feasible region and simplifying the resolution of the original problem. The efficacy of our innovative algorithm, when benchmarked against traditional constrained multiobjective evolutionary algorithms across 72 test functions, has demonstrated superior convergence, diversity, and competitiveness.

关键词： Optimization Linear programming Search problems Production partitioning algorithms Pareto optimization Intelligent control Fans Convergence Vectors Co-evolutionary complex constraints constraints grouping multiobjective optimization

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New Evaluation Method for Fuzzy Cluster Validity Indices

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IEEE ACCESS 2025年 13卷 22728-22744页

作者： Perez-Sanchez, Ismay Medina-Perez, Miguel Angel Monroy, Raul Loyola-Gonzalez, Octavio Gutierrez-Rodriguez, Andres Eduardo Tecnol Monterrey Sch Engn & Sci Atizapan De Zaragoza 52926 Estado De Mexic Mexico Kayak Analyt Dubai U Arab Emirates NTT DATA Madrid 28050 Spain Tecnol Monterrey Inst Future Educ Monterrey 64849 Mexico MAHLE Shared Serv Monterrey 64650 Mexico

Cluster analysis is the process of associating data objects with classes of similar objects. One key aspect of the clustering problem is to determine the optimal number of groups on a data set to effectively partition it. The process of establishing accurate indicators is called cluster validity, and it evaluates the quality of a clustering, including the optimal number of clusters. A cluster validity index (CVI) is a function that carries out that evaluation based on a series of features like intra-class compactness, inter-class separation, object density, and membership degree of objects to each cluster, among others. Best-c validation protocol has been used to evaluate CVIs previously, but it makes a strong assumption about the correctness of the clustering performed. We propose a new protocol based on mapping the internal CVI values to an external CVI and performing statistical tests to analyze the results. The experiments are conducted evaluating the partitions generated by the fuzzy c-means clustering algorithm over 84 UCI datasets with 31 fuzzy CVIs. Experimental results evidenced the need for bigger experimental setups compared to previous studies. Also, they show that among the CVIs involved, there is none with a significant statistical difference over the other given by a Friedman test. However, the Wilcoxon signed-rank test demonstrates that in 1 vs. 1, the CVI KPBM outperforms the others in most of the fuzzifiers (m). Just except for m = 2.0 which consolidates the results in previous studies where none of the CVIs were able to improve consistently among the selected group of CVIs.

关键词： Clustering algorithms partitioning algorithms Indexes Protocols Prediction algorithms Hands Weather forecasting Technological innovation Organizations Object recognition Fuzzy cluster validation index evaluation method fuzzy clustering fuzzy sets

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FWCEC: An Enhanced Feature Weighting Method via Causal Effect for Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 685-697页

作者： Cao, Fuyuan Jing, Xuechun Yu, Kui Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Shanxi Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

Feature weighting aims to assign different weights to features based on their importance in machine learning tasks. In clustering tasks, the existing methods learn feature importance based on the clustering results derived from the collaborative contribution of all features, which overlooks the independent effect of each feature. In fact, there are underlying causal relationships between features and the clustering results, and the features with high causal effects are always more crucial for clustering. Therefore, we propose an enhanced Feature Weighting method via Causal Effect for Clustering, calculating the causal effect of each feature on the clustering results for obtaining the independent contribution of each feature. Specifically, we start by identifying the causal relationships among the features and utilizing the causal relationships to generate a reasonable treatment group. Next, we compare the changes in the data distribution between the treatment and control groups to determine the causal effect of each feature. Finally, the causal effects of features are used for enhancing the clustering-driven weight learning. Moreover, we present a theory of relative order consistency in causal effect. Experimental results demonstrate that utilizing causal effect in weight learning facilitates efficient convergence and achieves superior accuracy compared to state-of-the-art clustering algorithms.

关键词： Clustering algorithms Machine learning algorithms Machine learning partitioning algorithms Noise measurement Collaboration Linear programming Convergence Technological innovation Reviews Feature weights clustering causal effect feature importance

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