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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第2期6卷 1186-1197页

作者： Nguyen, Thuy Dung Nguyen, Tuyen Nguyen, Thanh Hung Nguyen, Kien Le Nguyen, Phi Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi 100000 Vietnam Chiba Univ Grad Sch Engn Chiba 2638522 Japan

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks' lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and the charging time to avoid node failure in WRSNs. We formulate the optimization and propose a solution for that. The proposal includes an algorithm to find a list of potential charging locations and their associated optimal charging time. Moreover, the Q-learning technique is adopted to determine the optimal next charging location among the candidates. We implement the algorithm and conduct experiments to compare it to the related works. The results show that the proposed algorithm outperforms the others in terms of network lifetime. Specifically, the highest performance gap of our proposal to the best algorithm among the others is 8.29 times.

关键词： Optimization Heuristic algorithms Wireless sensor networks approximation algorithms Proposals Partitioning algorithms Mathematical models WRSN Q-learning on-demand charging algorithm target coverage connectivity

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Jamming-Assisted Legitimate Eavesdropping and Secure Communication in Multicarrier Interference Networks

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 954-965页

作者： Xu, Ding Zhu, Hongbo Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210003 Peoples R China

This article investigates legitimate eavesdropping and secure communication in a multicarrier interference network, where a suspicious link coexists with an unsuspicious link, in the presence of a full-duplex monitor. It is assumed that the unsuspicious users (UUs) are under illegal eavesdropping threat from the suspicious users (SUs), and the monitor can send jamming signals to interfere with the SUs. The UUs are required to cooperate with the monitor for reducing the secrecy outage probability of the UUs to be lower than that without the monitor's jamming. Under this secrecy outage constraint along with the transmit power constraints, the problem for maximizing the successful eavesdropping probability of the monitor by jointly allocating the transmit powers of the monitor and the UUs is investigated. The problem is solved with the Lagrange duality method by proving the time-sharing property holds, where two algorithms are proposed for obtaining the dual function based on the block coordinate descent method and the successive convex approximation method. Simulation results verify that the proposed algorithms are superior to the noncooperative strategy and the passive eavesdropping strategy, and achieve high successful eavesdropping probability and low secrecy outage probability simultaneously.

关键词： Monitoring Eavesdropping Jamming Interference approximation algorithms Quality of service Fading channels Legitimate eavesdropping physical layer security secure communication suspicious communication unsuspicious communication

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Dynamic Channel Selection and Transmission Scheduling for Cognitive Radio Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第23期9卷 24429-24443页

作者： Zhu, Xinyu Huang, Yang Wu, Qihui Zhou, Fuhui Ge, Xiaohu Liu, Yuan Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Elect Spectrum Space Minist Ind & Informat Technol Nanjing 210016 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs' network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs, Q-learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as Q-learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

关键词： Dynamic scheduling Resource management Optimization approximation algorithms Time-frequency analysis Data communication Sensors Basis function approximation (BFA) cognitive radio network (CRN) mutually embedded Markov decision processes (MDPs) Q-learning reinforcement learning (RL) resource allocation

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On clustering with discounts

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INFORMATION PROCESSING LETTERS 2022年 177卷

作者： Deng, Shichuan Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100084 Peoples R China

We study the k-median with discounts problem, wherein we are given clients with non negative discounts and seek to open at most k facilities. The goal is to minimize the sum of distances from each client to its nearest open facility which is discounted by its own discount value, with minimum contribution being zero. We obtain a bi-criteria constant factor approximation using an iterative LP rounding algorithm. Our result improves the previously best approximation guarantee for k-median with discounts (Ganesh et al. (2001) [9]). We also devise bi-criteria constant-factor approximation algorithms for the matroid and knapsack versions of median clustering with discounts. (c) 2022 Elsevier B.V. All rights reserved.

关键词： approximation algorithms Clustering with discounts Matroid constraint Knapsack constraint

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Lower Bounds on 0-Extension with Steiner Nodes 51

Lower Bounds on 0-Extension with Steiner Nodes

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Chen, Yu Tan, Zihan EPFL Lausanne Switzerland Rutgers University PiscatawayNJ United States

ISBN: (纸本)9783959773225

In the 0-Extension problem, we are given an edge-weighted graph G = (V, E, c), a set T ⊆ V of its vertices called terminals, and a semi-metric D over T, and the goal is to find an assignment f of each non-terminal vertex to a terminal, minimizing the sum, over all edges (u, v) ∈ E, the product of the edge weight c(u, v) and the distance D(f(u), f(v)) between the terminals that u, v are mapped to. Current best approximation algorithms on 0-Extension are based on rounding a linear programming relaxation called the semi-metric LP relaxation. The integrality gap of this LP, is upper bounded by O(log |T|/log log |T|) and lower bounded by Ω((log |T|)2/3), has been shown to be closely related to the quality of cut and flow vertex sparsifiers. We study a variant of the 0-Extension problem where Steiner vertices are allowed. Specifically, we focus on the integrality gap of the same semi-metric LP relaxation to this new problem. Following from previous work, this new integrality gap turns out to be closely related to the quality achievable by cut/flow vertex sparsifiers with Steiner nodes, a major open problem in graph compression. We show that the new integrality gap stays superconstant Ω(log log |T|) even if we allow a super-linear O(|T|log1−Ε |T|) number of Steiner nodes. © Yu Chen and Zihan Tan.

关键词： approximation algorithms

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Fast approximations and Coresets for (k, )-Median Under Dynamic Time Warping 40

Fast Approximations and Coresets for (k, )-Median Under Dyna...

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40th International Symposium on Computational Geometry, SoCG 2024

作者： Conradi, Jacobus Kolbe, Benedikt Psarros, Ioannis Rohde, Dennis University of Bonn Germany Hausdorff Center for Mathematics University of Bonn Germany Archimedes Athena Research Center Greece

ISBN: (纸本)9783959773164

We present algorithms for the computation of Ε-coresets for k-median clustering of point sequences in Rd under the p-dynamic time warping (DTW) distance. Coresets under DTW have not been investigated before, and the analysis is not directly accessible to existing methods as DTW is not a metric. The three main ingredients that allow our construction of coresets are the adaptation of the Ε-coreset framework of sensitivity sampling, bounds on the VC dimension of approximations to the range spaces of balls under DTW, and new approximation algorithms for the k-median problem under DTW. We achieve our results by investigating approximations of DTW that provide a trade-off between the provided accuracy and amenability to known techniques. In particular, we observe that given n curves under DTW, one can directly construct a metric that approximates DTW on this set, permitting the use of the wealth of results on metric spaces for clustering purposes. The resulting approximations are the first with polynomial running time and achieve a very similar approximation factor as state-of-the-art techniques. We apply our results to produce a practical algorithm approximating (k,)-median clustering under DTW. © Jacobus Conradi, Benedikt Kolbe, Ioannis Psarros, and Dennis Rohde.

关键词： approximation algorithms

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Projection-Based QLP Algorithm for Efficiently Computing Low-Rank approximation of Matrices

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 2218-2232页

作者： Kaloorazi, Maboud F. Chen, Jie Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China Xian Shiyou Univ Sch Elect Engn Xian 710000 Peoples R China

Matrices with low numerical rank are omnipresent in many signal processing and data analysis applications. The pivoted QLP (p-QLP) algorithm constructs a highly accurate approximation to an input low-rank matrix. However, it is computationally prohibitive for large matrices. In this paper, we introduce a new algorithm termed Projection-based Partial QLP (PbP-QLP) that efficiently approximates the p-QLP with high accuracy. Fundamental in our work is the exploitation of randomization and in contrast to the p-QLP, PbP-QLP does not use the pivoting strategy. As such, PbP-QLP can harness modern computer architectures, even better than competing randomized algorithms. The efficiency and effectiveness of our proposed PbP-QLP algorithm are investigated through various classes of synthetic and real-world data matrices.

关键词： Matrix decomposition approximation algorithms Signal processing algorithms Sparse matrices Singular value decomposition Signal processing Oceanography Low-rank approximation rank-revealing matrix factorization the pivoted QLP the singular value decomposition randomized numerical linear algebra

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Adaptive Dynamic Programming and Optimal Control of Unknown Multiplayer Systems Based on Game Theory

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IEEE ACCESS 2022年 10卷 77695-77706页

作者： Zhao, Jingang Weifang Univ Sch Machinery & Automat Weifang 261061 Shandong Peoples R China Weifang Univ Inst Intelligent Percept & Control Complex Syst Weifang 261061 Shandong Peoples R China

In this paper, we present a new adaptive dynamic programming (ADP) scheme to solve the optimal control problem of multi-player systems with unknown dynamics from the perspective of nonzero-sum (NZS) games. In the presented scheme, a new iterative equation is given. On the basis of the given iterative equation, the control policy and corresponding value function for each player can be learned by using the state and input data, which does not need to identify the system dynamics. To overcome the difficulty of unknown system dynamics, neural network (NN)-based function approximation techniques are employed in the implementation. Based on the given iterative equation and NN-based function approximation techniques, a new non-model-based ADP algorithm is developed. The convergence of the developed non-model-based ADP algorithm is rigorously analyzed and proved. Finally, two numerical simulation examples are provided to demonstrate the performance of the developed non-model-based ADP algorithm.

关键词： Games Mathematical models Heuristic algorithms approximation algorithms System dynamics Vehicle dynamics Optimal control Adaptive dynamic programming nonzero-sum (NZS) games multi-player systems coupled Hamilton-Jacobi (HJ) equations neural network (NN)

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Burning Graphs Through Farthest-First Traversal

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IEEE ACCESS 2022年 10卷 30395-30404页

作者： Garcia-Diaz, Jesus Cesar Perez-Sansalvador, Julio Xibai Rodriguez-Henriquez, Lil Maria Alejandro Cornejo-Acosta, Jose Consejo Nacl Ciencia & Technol Mexico City 03940 DF Mexico Inst Nacl Astrofis Opt & Electr Coordinat Ciencias Computacionales Puebla 72840 Mexico

Graph burning is a process to determine the spreading of information in a graph. If a sequence of vertices burns all the vertices of a graph by following the graph burning process, then such a sequence is known as a burning sequence. The graph burning problem consists in finding a minimum length burning sequence for a given graph. The solution to this NP-hard combinatorial optimization problem helps quantify a graph's vulnerability to contagion. This paper introduces a simple farthest-first traversal-based approximation algorithm for this problem over arbitrary graphs. We refer to this proposal as the Burning Farthest-First (BFF) algorithm. BFF runs in O(n(3)) steps and has a tight approximation factor of 3 - 2/b(G), where b(G) is the size of an optimal solution. The main attribute of BFF is that it has a better approximation factor than the state-of-the-art approximation algorithms for arbitrary graphs, which report an approximation factor of 3. Despite being simple, BFF proved practical when tested over some benchmark datasets.

关键词： approximation algorithms farthest-first traversal graph burning information spreading social contagion

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HARS: A High-Available and Resource-Saving Service Function Chain Placement Approach in Data Center Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第2期19卷 829-847页

作者： Niu, Meng Han, Qingmian Cheng, Bo Wang, Meng Xu, Ziqi Gu, Wenyuan Zhang, Shuhao Chen, Junliang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100000 Peoples R China China Elect Technol Grp Corp Res Inst 54 Shijiazhuang 050002 Hebei Peoples R China

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200%. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75% of the resources to achieve the same availability as the state-of-the-art models.

关键词： Switches Heuristic algorithms Data centers Costs Servers Reliability approximation algorithms Network function virtualization service function chain availability backup resources allocation

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