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On the Reconstruction of a Quality Virtual Backbone in a Wireless Sensor Network With Faulty Links

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 1277-1292页

作者： Liang, Jiarong He, Feng Wang, Dajin Li, Qingnian Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Nanning Univ Nanning 530200 Guangxi Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Montclair State Univ Dept Comp Sci Montclair NJ 07043 USA Nanning Univ Coll Informat Engn Nanning 530200 Guangxi Peoples R China

The work of routing and topological control for a wireless sensor network (WSN) is often undertaken by means of its virtual backbone (VB). Usually, a WSN and its VB can be modeled as a unit disk graph (UDG) and a corresponding connected dominating set (CDS), respectively. A smaller CDS in a UDG is preferred because it will lead to less overhead. In practical applications, sensor nodes or their links in a WSN may fail due to obstacles or accidental damage. Thus, it is desirable to either construct a robust VB or be able to reconstruct a new VB. In this article, the problem of reconstructing CDSs for UDGs with faulty links is considered. First, we propose a centralized approximation algorithm for the problem. We theoretically show that for a given UDG, the size of the CDS constructed by our algorithm does not exceed xi & sdot;opt+gamma+2m , where xi & sdot;opt+gamma is the upper bound on the original CDS size, opt is the minimum CDS size in the UDG, xi and gamma are two positive constants, and m is the number of faulty links. Next, we design a distributed approximation algorithm on the basis of our centralized approximation algorithm and analyze its time and message complexity. Related simulation experiments are presented to compare our algorithm with other state-of-the-art algorithms for solving this problem, and the results show that our algorithm outperforms its competitors.

关键词： Wireless sensor networks approximation algorithms Routing Storms Fault tolerant systems Fault tolerance Task analysis Wireless sensor network approximation algorithm reconstruction faulty links distributed algorithm connected dominating set

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Efficient and Privacy Preserving approximation of Distributed Statistical Queries

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IEEE TRANSACTIONS ON BIG DATA 2022年第5期8卷 1399-1413页

作者： Derbeko, Philip Dolev, Shlomi Gudes, Ehud Ullman, Jeffrey D. Ben Gurion Univ Negev Comp Sci Dept IL-8410501 Beer Sheva Israel Stanford Univ Stanford CA 94305 USA

In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release was intensively investigated. However, despite a considerable progress, computational complexity and consequently, the performance of the computations, due to an increasing size of data, remains a limiting factor in real-world deployments. Especially in the case of privacy-preserving computations. In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research in the past that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on an approximation of intersection set both with and without a privacy-preserving mechanism. An analysis of the bound on the error as a function of the sample size is discussed and a heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.

关键词： Distributed databases Protocols Differential privacy Estimation approximation algorithms Heuristic algorithms Law enforcement Differential privacy distributed computations approximate computations

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Ordinal Maximin Share approximation for Goods

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2022年 74卷 353-391页

作者： Hosseini, Hadi Searns, Andrew Segal-Halevi, Erel Penn State Univ University Pk PA 16802 USA Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Ariel Univ Ariel Israel

In fair division of indivisible goods, l-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the l least preferred bundles. Most existing works aim to guarantee to all agents a constant fraction of their 1-out-of-n MMS. But this guarantee is sensitive to small perturbation in agents' cardinal valuations. We consider a more robust approximation notion, which depends only on the agents' ordinal rankings of bundles. We prove the existence of l-out-of- left perpendicular (l + 1/2)n right perpendicular MMS allocations of goods for any integer l >= 1, and present a polynomial-time algorithm that finds a 1-out-of- inverted right perpendicular 3n/2 inverted left perpendicular MMS allocation when l = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any l > 1.

关键词： approximation algorithms

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Distributed Fusion of Multiple Model Estimators Using Minimum Forward Kullback-Leibler Divergence Sum

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第3期60卷 2934-2947页

作者： Wei, Zheng Duan, Zhansheng Hanebeck, Uwe D. Xi An Jiao Tong Univ Ctr Informat Engn Sci Res Xian 710049 Peoples R China Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab D-76131 Karlsruhe Germany

The problem of distributed fusion of Gaussian mixture models (GMMs) provided by the local multiple model (MM) estimators is addressed in this article. Taking GMMs instead of combined Gaussian assumed probability density functions (pdfs) as the output of local MM estimators can retain more detailed (or internal) information about local estimations, but the accompanying challenge is to perform the fusion of GMMs. For this problem, a distributed fusion framework of GMMs under the minimum forward Kullback-Leibler (KL) divergence sum criterion is proposed first. Then, because the KL divergence between GMMs is not analytically tractable, two suboptimal distributed fusion algorithms are further developed within this framework. These two fusion algorithms all have closed forms. Numerical examples verify their effectiveness in terms of both computational efficiency and estimation accuracy.

关键词： Target tracking Probability density function Sensor fusion approximation algorithms Time measurement Predictive models Optimization Distributed fusion Kullback-Leibler (KL) divergence maneuvering target tracking multiple model (MM) estimation

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Rearrangement Distance Problems: An Updated Survey

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ACM COMPUTING SURVEYS 2024年第8期56卷 1页

作者： Oliveira, Andre Rodrigues Brito, Klairton Lima Alexandrino, Alexsandro Oliveira Siqueira, Gabriel Dias, Ulisses Dias, Zanoni Univ Presbiteriana Mackenzie Fac Comp & Informat Rua Consolacao 896 Comp BR-01302907 Sao Paulo SP Brazil Univ Estadual Campinas UNICAMP Inst Comp Ave Albert Einstein 1251Cidade Univ BR-13083852 Campinas SP Brazil Univ Estadual Campinas UNICAMP Fac Tecnol Rua Paschoal Marmo 1888 BR-13083852 Limeira SP Brazil

One of the challenges in the Comparative Genomics field is to infer how close two organisms are based on the similarities and differences between their genetic materials. Recent advances in DNA sequencing have made complete genomes increasingly available. That said, several new algorithms trying to infer the distance between two organisms based on genome rearrangements have been proposed in the literature. However, given the diversity of approaches, the diversity of genome rearrangement events, or even how each work models the genomes and what assumptions are made by each of them, finding the ideal algorithm for each situation or simply knowing the range of applicable approaches can be challenging. In this work, we review these approaches having the algorithmic and combinatorial advances since 2010 as our main focus. This survey aims to organize the recently published papers using a concise notation and to indicate the gaps filled by each of them in the literature. This makes it easier to understand what still needs to be done and what has room for enhancement.

关键词： Genome rearrangements approximation algorithms sorting permutations comparative genomics

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Multiagent Trust Region Policy Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第9期35卷 12873-12887页

作者： Li, Hepeng He, Haibo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents' observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.

关键词： Optimization Training approximation algorithms Convergence Games Gradient methods Observability Decentralized learning multiagent reinforcement learning (MARL) partially observable trust region policy optimization (TRPO)

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Imaging Approach for FMCW Synthetic Aperture Radar Sensor Using Modified ω-k Method

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IEEE SENSORS JOURNAL 2024年第24期24卷 41243-41250页

作者： Ling, Qing Ma, Jingtao Ni, Tao Huang, Penghui Xia, Xiang-Gen Liu, Yanyang Liu, Zhiling Liu, Xingzhao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China Shanghai Inst Satellite Engn Shanghai 201109 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Nanjing Elect Equipment Inst Nanjing 211103 Peoples R China

The radar sensors with the frequency modulation continuous wave system are easily miniaturized for scene imaging on compact platforms, and the omega-k imaging algorithm is widely utilized. However, the conventional omega-k algorithm for synthetic aperture radar (SAR) imaging may not consider the high-order azimuth spatial-variant nonstationary phase errors. In this article, a novel approach for SAR imaging is developed with a modified omega-k method. Under the frequency-modulated continuous radar system, after dechirp operation on the echo signal, a modified time-scaled transformation is proposed to correct the range cell migration (RCM) where the azimuth nonstationary phase information is retained to promote the azimuth focusing. Finally, the well-focused SAR images can be obtained after compensating the intrapulse motion error and the high-order azimuth spatial-variant nonstationary phase errors. Both simulation results of point targets and real-measured FMCW SAR data verify the effectiveness and feasibility proposed algorithm.

关键词： Azimuth Radar imaging Synthetic aperture radar Sensors Imaging Radar polarimetry Frequency modulation Interpolation approximation algorithms Doppler effect Frequency modulated continuous wave modified time-scaled transform modified omega-k synthetic aperture radar (SAR) imaging

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A Novel Prior-Based Channel Estimation and Activity Detection in Cell-Free mMTC Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5500-5516页

作者： Rajoriya, Anupama Singh, Nakul Budhiraja, Rohit Chaturvedi, Ajit K. IIT Kanpur Dept Elect Engn Kanpur 208016 India Georgia Inst Technol Atlanta GA 30332 USA

We consider the problem of joint channel estimation (CE) and device activity detection (DAD) in the uplink of a cell-free millimeter wave massive multiple-input multiple output (mMIMO) system for massive machine-type communication. We know that the mMIMO channel is spatially correlated, and is sparse in the angular domain. The correlation and sparsity are captured by tailoring a Gaussian prior. This prior is then used to design a centralized variational Bayesian learning (cVBL) algorithm for CE and DAD. The variational approximation in cVBL algorithm reduces its complexity from cubic to linear in terms of devices. We next propose an asynchronous decentralized VBL (adVBL) algorithm, wherein each AP locally estimates its channel from all the devices. The adVBL algorithm is robust to the AP failures, and its complexity is invariant of the number of APs. The adVBL algorithm is developed by reformulating cVBL updates as global optimization problems, and by deriving their local counterparts using the alternating direction method of multipliers. Through extensive numerical studies, we show that the proposed cVBL and adVBL algorithms i) outperform several existing algorithms;ii) require much less pilot overhead;and iii) estimate large-scale fading, unlike the existing ones.

关键词： approximation algorithms Signal processing algorithms Correlation Channel estimation Antennas Millimeter wave communication Complexity theory Optimization Bayes methods Vectors Decentralized algorithm expectation maximization variational Bayesian learning

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PTAS FOR MINIMUM COST MULTICOVERING WITH DISKS

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SIAM JOURNAL ON COMPUTING 2024年第4期53卷 1181-1215页

作者： Huang, Ziyun Feng, Qilong Wang, Jianxin Xu, Jinhui Behrend Coll Dept Comp Sci & Software Engn Penn State Erie Erie PA 16563 USA Cent South Univ Sch Comp Sci & Engn Changsha Hunan Peoples R China SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

In this paper, we study the following Minimum Cost Multicovering (MCMC) problem: Given a set of n client points C and a set of m server points S in a fixed dimensional Rd space, determine a set of disks centered at these server points so that each client point c is covered by at least k(c) disks and the total cost of these disks is minimized, where k(& sdot;) is a function that maps every client point to some nonnegative integer no more than m and the cost of each disk is measured by the alpha th power of its radius for some constant alpha>0 . MCMC is a fundamental optimization problem with applications in many areas such as wireless/sensor networking. Despite extensive research on this problem for about two decades, only constant approximations were known for general k . It has been a long standing open problem to determine whether a PTAS is possible. In this paper, we give an affirmative answer to this question by presenting the first PTAS for it. Our approach is based on a number of novel techniques, such as balanced recursive realization and bubble charging, and new counterintuitive insights to the problem. Particularly, we approximate each disk with a set of sub-boxes and optimize them at the subdisk level. This allows us to first compute an approximate disk cover through dynamic programming, and then obtain the desired disk cover through a balanced recursive realization procedure.

关键词： minimum cost multi-covering geometric set cover PTAS approximation algorithms computational geometry

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Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation

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IEEE ACCESS 2024年 12卷 29571-29582页

作者： Chen, Xixiang Zheng, Jingjing Zhao, Li Jiang, Wei Zhang, Xiaoqin Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Univ British Columbia Dept Math Vancouver BC V6T 1Z4 Canada

In this paper, a method for orthogonal tensor recovery based on non-convex regularization and rank estimation (OTRN-RE) is proposed, which aims to accurately recover the low-rank and sparse components of the tensor. Specifically, a new low-rank tensor decomposition algorithm is designed, which can efficiently establish the equivalence between the rank of a large tensor before decomposition and the rank of the coefficient tensor after decomposition. The large tensor is decomposed into a small standard orthogonal tensor and another coefficient tensor, and a generalized non-convex regularization is used to inscribe the low rank of the coefficient tensor. Meanwhile, a new rank estimation strategy is developed to dynamically adjust the size of small orthogonal tensors and coefficient tensors. Experimental results on image denoising and salient object detection tasks confirm the state-of-the-art performance of the proposed method in terms of denoising capability and computational speed.

关键词： Tensors Estimation Discrete Fourier transforms approximation algorithms Task analysis Convex functions Standards Orthogonal tensor recovery non-convex regularization tensor decomposition low-rank recovery

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