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Coordinated Computing Resource Allocation With Efficiency Maximization in Heterogeneous Platoon Edge Network

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15809-15826页

作者： Zhu, Shuya Meng, Kaitao Wang, Rui Li, Deshi Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England

Under numerous computation requirements in intelligent traffic, platoons comprised of several connected vehicles are expected to centralize vehicular computation resources, which can provide computing services for surrounding mobile users and thus facilitate the deployment of vehicular edge computing (VEC). Nevertheless, the computing resources in multi-platoon are distributed unevenly, making platoons' resource allocation highly selective, especially for randomly distributed mobile users. Moreover, existing works mainly focus on single-platoon-assisted VEC, which lacks resource coordination among platoons and may result in resource imbalance. Thus, through coordinating computing resource allocation in platoons and base stations (BSs), a coordinated computing resource allocation scheme is proposed in this paper to maximize the utilities of mobile users. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. To address this issue, the efficiency-maximized optimal computing resources allocated from platoons to users are derived in a semi-closed form. Then, to decouple the variables in allocation coordination, the efficiency maximization problem is proven to be equivalent to a low-complexity resource allocation problem oriented for single users. Based on the above results, two algorithms are proposed to convert the NP-hard problem into convex ones, 1) through efficiency-maximized task offloading and backtracking iteratively, a profit efficiency backtracking algorithm is proposed to coordinate computing resource allocation among platoons, and 2) heterogeneous profit efficiency algorithm is proposed to solve the primal problem, where a partition ratio-based efficiency maximization computing resource allocation problem is optimized in heterogeneous edges. Extensive simulation results show that our proposed scheme can improve resource allocation efficiency, task success rates, and service continuity over benchmark scheme

关键词： Resource management Task analysis Computational modeling partitioning algorithms Heuristic algorithms Backtracking Clustering algorithms Vehicular edge computing platoon collaborative task offloading resource allocation coordination

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A Hybrid Evolutionary Algorithm for the Clique partitioning Problem

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9391-9403页

作者： Lu, Zhi Zhou, Yi Hao, Jin-Kao Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Angers Dept Comp Sci LERIA F-49045 Angers France

The clique partitioning problem (CPP) of an edge-weighted complete graph is to partition the vertex set V into k disjoint subsets such that the sum of the edge weights within all cliques induced by the subsets is as large as possible. The problem has a number of practical applications in areas, such as data mining, engineering, and bioinformatics, and is, however, computationally challenging. To solve this NP-hard problem, we propose the first evolutionary algorithm that combines a dedicated merge-divide crossover operator to generate offspring solutions and an effective simulated annealing-based local optimization procedure to find high-quality local optima. The extensive experiments on three sets of 94 benchmark instances (including two sets of 63 classical benchmark instances and one new set of 31 large benchmark) show a remarkable performance of the proposed approach compared to the state-of-the-art methods. We analyze the key algorithmic ingredients to shed light on their impacts on the performance of the algorithm. The algorithm and its available source code can benefit people working on practical problems related to CPP.

关键词： partitioning algorithms Statistics Sociology Optimization Search problems Evolutionary computation Benchmark testing Clique partitioning crossover hybrid evolutionary search method local optimization

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I/O-Efficient Multi-Criteria Shortest Paths Query Processing on Large Graphs

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6430-6446页

作者： Zhou, Xinjie Huang, Kai Li, Lei Zhang, Mengxuan Zhou, Xiaofang Hong Kong Univ Sci & Technol Hong Kong Peoples R China Macau Univ Sci & Technol Sch Comp Sci & Engn Taipa 999078 Peoples R China Hong Kong Univ Sci & Technol Dept CSE Hong Kong Peoples R China Hong Kong Univ Sci & Technol Thrust DSA Guangzhou 511436 Peoples R China Australian Natl Univ Sch Comp Canberra ACT 2601 Australia

Shortest path computation is a basic operation for many graph-based applications and has been extensively studied. However, most existing works only consider the optimal path of a single criterion but ignore real-world situations involving multiple criteria. This paper investigates a new Multi-Criteria Shortest Paths (MCSPs) problem, aiming to compute the shortest paths of all criteria between a vertex pair. It is significant for real-world applications such as GPS navigation and social network analysis. Nevertheless, the rapid growth of graph size or memory-limited devices poses a memory-constraint challenge, making the adaptation of existing methods extremely time-consuming. To solve the memory-constraint MCSPs problem, we propose a general STOP & SHARE scheme to synchronize the search speeds of all criteria for sharing partition accesses. Two algorithms called OHP and MHP, adopting the one-hop strategy and partition exhaustive strategy, respectively, are proposed for implementing our scheme. Moreover, we develop two optimized algorithms, BMHP and BMHPS, to improve query efficiency by combining MHP with the bidirectional technique and a novel in-partition shortcut optimization. We also investigate partition-oriented I/O management. Experimental studies on large real-world graphs demonstrate the effectiveness of the proposed methods over the multi-pass adaptations of the existing methods.

关键词： Social networking (online) Indexes partitioning algorithms Servers Roads Memory management Synchronization Shortest paths multi-criteria I/O-efficient large graph

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Platoon Partition and Resource Allocation for Ultra-Reliable V2X Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第1期73卷 147-161页

作者： Chai, Guanhua Wu, Weihua Yang, Qinghai Qin, Meng Wu, Yan Yu, F. Richard Xidian Univ Sch Telecommun Engn State Key Lab ISN Xian 710071 Shaanxi Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

In this article, we study the joint platoon partition, power control and spectrum allocation for platoon communication in cellular vehicle-to-everything (V2X) networks with uncertain channel parameters. A distributionally robust (DR) optimization problem is formulated to maximize the vehicle-to-infrastructure (V2I) capacity whilst guaranteeing the reliability of intra-platoon communication. For solving the DR problem, a statistical-based approach is developed to learn the distributional ambiguity set from historical uncertain channels. Then, based on it, a data-driven equivalent transformation approach is proposed to transform the probabilistic vehicle-to-vehicle (V2V) reliability requirement into a deterministic semidefinite expression. Considering that the resource allocation is built on the platoon partition result, the formulated problem is decomposed into a resource allocation problem and a platoon partition problem. Then, a low-complexity two-stage wireless resource allocation strategy is proposed for V2X networks, which contains power control and spectrum matching. After that, the platoon partition is transformed into a specific Markov chain design problem. An exploration-consolidation learning algorithm is proposed to obtain the optimal platoon partition according to the designed Markov transition rate. Finally, simulation results verify that the proposed algorithm is superior to other existing schemes.

关键词： Resource management Reliability partitioning algorithms Wireless communication Vehicle dynamics Unicast Transforms Channel uncertainty reliable communication resource allocation V2X networks

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE ACCESS 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xian Univ Sci & Technol Coll Comp Sci & Technol Xian 710600 Peoples R China Qinghai Inst Technol Sch Comp & Informat Sci Xining 810016 Peoples R China Xian Univ Sch Informat Engn Xian 710065 Peoples R China Xian Big Data Serv Ctr Xian 710007 Peoples R China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms.

关键词： Optimization Detection algorithms Accuracy partitioning algorithms Complex networks Measurement Autoencoders Prediction algorithms Organizations Mutual information community detection node propagation attraction

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A Comprehensive Survey on Community Detection With Deep Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第4期35卷 4682-4702页

作者： Su, Xing Xue, Shan Liu, Fanzhen Wu, Jia Yang, Jian Zhou, Chuan Hu, Wenbin Paris, Cecile Nepal, Surya Jin, Di Sheng, Quan Z. Yu, Philip S. Macquarie Univ Sch Comp Sydney NSW 2109 Australia CSIRO Data61 Sydney NSW 2015 Australia Chinese Acad Sci Acad Math & Syst Sci Beijing 100093 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

关键词： Deep learning Taxonomy Optimization partitioning algorithms Clustering algorithms Social networking (online) Peer-to-peer computing Community detection deep learning graph neural network network representation social networks

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Distributed Control Design for Solving Linear Algebraic Equations via Adjustable Domains

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第12期69卷 8790-8797页

作者： Li, Juntao Liang, Cong Meng, Deyuan Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Peoples R China Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This article aims to develop a general and designable distributed algorithm for solving linear algebraic equations (LAEs), which departs from the design framework based on orthogonal projection. The concept of adjustable domains for the parameter matrix is introduced, enabling the algorithm to derive flexible and variable updating rules for agents. By leveraging adjustable domains in control design, all agents can exponentially converge to a common (least squares) solution of (un)solvable LAEs under arbitrary initialization conditions, regardless of whether the LAEs admit a unique solution or multiple solutions. Moreover, two novel distributed algorithms for obtaining the least squares solution are proposed within both row and column partitioning frameworks. A simulation example is provided to demonstrate the effectiveness of the proposed distributed algorithms.

关键词： Distributed algorithms Mathematical models Task analysis partitioning algorithms Topology Symmetric matrices Matrix decomposition Adjustable domain distributed algorithm linear algebraic equations (LAEs) multiagent system time-varying topology

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Entropy-Optimized Deep Weighted Product Quantization for Image Retrieval

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 1162-1174页

作者： Gu, Lingchen Liu, Ju Liu, Xiaoxi Wan, Wenbo Sun, Jiande Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Hashing and quantization have greatly succeeded by benefiting from deep learning for large-scale image retrieval. Recently, deep product quantization methods have attracted wide attention. However, representation capability of codewords needs to be further improved. Moreover, since the number of codewords in the codebook depends on experience, representation capability of codewords is usually imbalanced, which leads to redundancy or insufficiency of codewords and reduces retrieval performance. Therefore, in this paper, we propose a novel deep product quantization method, named Entropy Optimized deep Weighted Product Quantization (EOWPQ), which not only encodes samples into the weighted codewords in a new flexible manner but also balances the codeword assignment, improving while balancing representation capability of codewords. Specifically, we encode samples using the linear weighted sum of codewords instead of a single codeword as traditionally. Meanwhile, we establish the linear relationship between the weighted codewords and semantic labels, which effectively maintains semantic information of codewords. Moreover, in order to balance the codeword assignment, that is, avoiding some codewords representing most samples or some codewords representing very few samples, we maximize the entropy of the coding probability distribution and obtain the optimal coding probability distribution of samples by utilizing optimal transport theory, which achieves the optimal assignment of codewords and balances representation capability of codewords. The experimental results on three benchmark datasets show that EOWPQ can achieve better retrieval performance and also show the improvement of representation capability of codewords and the balance of codeword assignment.

关键词： Quantization (signal) Encoding Deep learning Image coding Semantics Probability distribution partitioning algorithms Product quantization deep learning image retrieval

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Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6653-6666页

作者： Li, Dong Zhou, Shuisheng Zeng, Tieyong Chan, Raymond H. Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China City Univ Hong Kong Dept Math Kowloon Tong Hong Kong Peoples R China Hong Kong Ctr Cerebrocardiovasc Hlth Engn Pak Shek Kok Hong Kong Peoples R China

K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given a priori. To solve these two issues, a multi-prototypes convex merging based K-Means clustering algorithm (MCKM) is presented. First, based on the structure of the spurious local minima of the K-Means problem, a multi-prototypes sampling (MPS) is designed to select the appropriate number of multi-prototypes for data with arbitrary shapes. Then, a merging technique, called convex merging (CM), merges the multi-prototypes to get a better local minima without k being given a priori. Specifically, CM can obtain the optimal merging and estimate the correct k. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given k first. Two theoretical proofs are given to guarantee that the cost of MCKM (MPS+CM) can achieve a constant factor approximation to the optimal cost of the K-Means problem. Experimental results performed on synthetic and real-world data sets have verified the effectiveness of the proposed algorithm.

关键词： Clustering algorithms Prototypes Merging partitioning algorithms Approximation algorithms Costs Shape Convex merging K-means multi-prototypes multi-prototypes sampling

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Urban Road Network partitioning Based on Bi-Modal Traffic Flows With Multiobjective Optimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20664-20680页

作者： Chen, Saifei Wu, NaiQi Fu, Hui Wang, Yefei Qiao, Yan Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Peoples R China Guangdong Univ Technol Dept Ind Engn Guangzhou 510006 Peoples R China Guangdong Yonghua Technol Co Ltd Guangzhou 510000 Peoples R China

The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the contribution of large occupancy vehicles such as buses in improving urban transportation efficiency. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized for further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. It is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjusting is performed by changing the region to which a boundary is clustered to optimize the result. Multi-sensor data collected from Shenzhen in China are utilized to verify the effectiveness of the proposed partition method.

关键词： partitioning algorithms Roads Merging Clustering algorithms Hysteresis Optimization Genetic algorithms Network partitioning bi-modal traffic flows multi-objective optimization bi-modal macroscopic fundamental diagram

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