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Multi-Layer Decomposition of Network Utility Maximization Problems

IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第5期28卷 2077-2091页

作者： Karakoc, Nurullah Scaglione, Anna Nedic, Angelia Reisslein, Martin Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

We describe a distributed framework for resource sharing problems that arise in communications, micro-economics, and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (ML-NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions with central management and distributed computations to the resource allocation problems. In non-stationary environments, the technique aims to respond quickly to the dynamics of the network by decreasing delay by partially shifting the communication and computational burden to the network edges. Our main contribution is a detailed analysis under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms used for local computations. Moreover, assuming strong concavity and smoothness of the users' objective functions, and under some stability conditions for each layer, we present convergence rates and optimality bounds for the ML-NUM framework. In addition, the main benefits of the proposed method are demonstrated with numerical examples.

关键词： Resource management Convergence Heuristic algorithms Indexes Gradient methods distributed algorithms IEEE transactions distributed computation network resource allocation

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Joint Incentive and Resource Allocation Design for User Provided Network Under 5G Integrated Access and Backhaul Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第2期7卷 673-685页

作者： Liu, Yao Tang, Aimin Wang, Xudong Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai 200240 Peoples R China

User provided network (UPN) allows a user with high channel quality to share the network access for users with poor channel quality. As a result, both the quality of experience and efficiency of network resource can be improved by UPN. However, the success of UPN relies heavily on the willingness of users to participate in sharing, so the design of incentive mechanisms is critical for UPN. In this paper, the UPN formed by D2D links under 5G integrated access and backhaul (IAB) networks is considered. In IAB networks, both access and backhaul links use wireless transmissions and dynamically share all the spectrum resources. Thus, the resource allocation for all the links also has a great impact on the UPN efficiency. To this end, a novel joint incentive and resource allocation design is explored. More specifically, considering the fairness between users, a Nash bargaining problem as a cooperative game is formulated by considering the user utility, the sensitivity of battery energy, the incentive compensation, and the limitation of network resources. To achieve the optimal Nash bargaining solution, a centralized algorithm is first designed, in which all the user information is collected by the operator for conducting centralized optimization. Thus, the centralized algorithm leads to a privacy problem. To this end, a distributed algorithm is developed to decompose the primal problem into subproblems for the operator and each user. By passing intermediate parameters between users and iterative execution of subproblems, the solution of the distributed algorithm is proved to converge to the optimal solution of the centralized algorithm. Extensive numerical results have been conducted to show that our design can effectively improve both the user experience and network throughput, i.e., operator's revenue.

关键词： Resource management 5G mobile communication Device-to-device communication Wireless communication distributed algorithms Mesh networks Cellular networks User provided network integrated access and backhaul incentive mechanism bargain

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Sparse matrix multiplication and triangle listing in the Congested Clique model

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THEORETICAL COMPUTER SCIENCE 2020年 809卷 45-60页

作者： Censor-Hillel, Keren Leitersdorf, Dean Turner, Elia Technion Dept Comp Sci Haifa Israel

We show how to multiply two n x n matrices S and T over semirings in the CONGESTED CLIQUE model, where n nodes communicate in a fully connected synchronous network using O(log n)-bit messages, within O (nz(S)(1/3) nz(T)(1/3)/n + 1) rounds of communication, where nz(S) and nz(T) denote the number of non-zero elements in S and T. respectively. By leveraging the sparsity of the input matrices, our algorithm greatly reduces communication costs compared with general multiplication algorithms [Censor-Hillel et al. (2015) [9]], and thus improves upon the state-of-the-art for matrices with o(n(2)) non-zero elements. Moreover, our algorithm exhibits the additional strength of surpassing previous solutions also in the case where only one of the two matrices is such. Particularly, this allows to efficiently raise a sparse matrix to a power greater than 2. As applications, we show how to speed up the computation on non-dense graphs of 4-cycle counting and all-pairs-shortest-paths. Our algorithmic contribution is a new deterministic method of restructuring the input matrices in a sparsity-aware manner, which assigns each node with element-wise multiplication tasks that are not necessarily consecutive but guarantee a balanced element distribution, providing for communication-efficient multiplication. Moreover, this new deterministic method for restructuring matrices may be used to restructure the adjacency matrix of input graphs, enabling faster deterministic solutions for graph related problems. As an example, we present a new sparsity aware, deterministic algorithm which solves the triangle listing problem in O (m/n(5/3) + 1) rounds, a complexity that was previously obtained by a randomized algorithm [Pandurangan et al. (2018) [26]] and that matches the known lower bound of (Omega) over bar (n(1/3)) when m = n(2) of [Izumi and Le Gall (2017) [19], Pandurangan et al. (2018) [26]]. Naturally, our triangle listing algorithm also implies triangle counting within the same complexity

关键词： distributed algorithms Congested clique Matrix multiplication Triangle listing

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Combining Contract Theory and Lyapunov Optimization for Content Sharing With Edge Caching and Device-to-Device Communications

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1213-1226页

作者： Asheralieva, Alia Niyato, Dusit Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

The paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users' locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies.

关键词： Device-to-device communication Contracts Wireless communication Optimization Stochastic processes Computational modeling Clustering algorithms Content sharing contract theory device-to-device communication distributed algorithms dynamic spectrum access incomplete information Lyapunov optimization stochastic network control

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Energy Efficient Scheduling algorithms for Sweep Coverage in Mobile Sensor Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第6期19卷 1332-1345页

作者： Gao, Xiaofeng Chen, Zhiyin Pan, Jianping Wu, Fan Chen, Guihai Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Key Lab Scalable Comp & Syst Shanghai 200240 Peoples R China Univ Victoria Victoria BC V8P 5C2 Canada

Nowadays, with the development of micro-electro-mechanical technologies, sweep coverage with mobile sensors is more and more popular in wireless sensor networks, which is also applied widely in other scenarios, such as message ferrying and data routing in ad-hoc networks. In order to reduce the sweep cycle and the number of mobile sensors, we propose the Distance-Sensitive-Route-Scheduling (DSRS) problem, which is to consider the effect of sensing range. We prove that DSRS is NP-hard, and consider three different scenarios: the single sensing-point case, the general case, and the extended case. In the single sensing-point case, we propose an approximation algorithm ROSE to schedule the routes of the mobile sensors efficiently. For the general case and the extended case, we present two other approximation algorithms G-ROSE and E-ROSE based on ROSE. We further characterize the non-locality property and design a distributed algorithm D-ROSE, coordinating sensors to meet the sweep requirements with best effort. Our algorithms are scalable to different sweep coverage problems, and according to the simulation results, they greatly outperform other existing algorithms up to 45 percent especially with a large sensing range.

关键词： Robot sensing systems Approximation algorithms Scheduling distributed algorithms Surveillance Mobile computing Mobile sensor networks sweep coverage approximation distributed algorithm

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Constrained consensus over continuous-time multi-agent networks with state constraints, non-convex input constraints and time delays

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IET CONTROL THEORY AND APPLICATIONS 2020年第20期14卷 3731-3737页

作者： Mo, Lipo Yu, Yongguang Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China Guizhou Inst Technol Key Lab Power Big Data Guizhou Prov Guiyang 550003 Peoples R China Beijing Jiaotong Univ Dept Math Beijing 100044 Peoples R China

This study considers the constrained consensus problem of continuous-time multi-agent networks with hypercube state constraints, non-convex input constraints and non-uniform delays. It is assumed that each agent can only perceive its own constraint sets, the communication graphs are switching over time and the joint communication graphs are strongly connected. By introducing the time-varying gains and the constraint operators, a new distributed algorithm is proposed. Then, it is proved that the constraint consensus can be reached under the proposed algorithm by reduction, while the states and the inputs are constrained to stay at the corresponding constraint sets. Finally, simulation examples are given to examine the effectiveness of the proposed results.

关键词： time-varying systems continuous time systems graph theory multi-agent systems multi-robot systems delays set theory distributed algorithms nonlinear control systems convex programming Lyapunov methods constraint consensus corresponding constraint sets continuous-time multiagent networks nonconvex input constraints time delays constrained consensus problem hypercube state constraints nonuniform delays joint communication graphs time-varying gains constraint operators

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Decentralized Cooperative Optimal Power Flow of Multiple Interconnected Microgrids via Negotiation

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IEEE TRANSACTIONS ON SMART GRID 2020年第5期11卷 3827-3836页

作者： Li, Fangyuan Qin, Jiahu Wan, Yanni Yang, Tao Zhengzhou Univ Dept Automat Zhengzhou 450001 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

The microgrid is widely recognized as a promising concept for integrating distributed energy resources (DERs). Considering the enormous number of DERs, the future smart grid will likely be a grid containing a number of interconnected microgrids. Thus, the optimal power flow (OPF) problem should be studied by properly taking account of the coupling between the microgrids. In this paper, the models of standalone microgrid and coupled microgrids are formulated first. Then, a decentralized approach is shown in detail to cooperatively solve the coupled OPF problem. Specifically, each microgrid solves the local OPF problem, leading to the optimal solution of the coupled OPF problem via negotiation between microgrids. The privacy of each microgrid is also preserved since no sensitive information is shared between microgrids. Finally, simulation results show that the optimality gaps between the proposed approach and an existing solver are small for both the AC OPF and the DC OPF. Furthermore, the total cost of the microgrids is reduced by the cooperative OPF.

关键词： Microgrids Generators Load modeling Photovoltaic systems Admittance distributed algorithms distributed algorithms optimal power flow multiple microgrids

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Data aggregation algorithms for wireless sensor network: A review

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AD HOC NETWORKS 2020年 100卷 102083-000页

作者： Kaur, Mandeep Munjal, Amit Thapar Inst Engn & Technol Dept Elect & Commun Patiala 147004 Punjab India

In recent years, wireless sensor network (WSN) is one of the rapidly growing area in various domains such as disaster relief operation, military applications, health applications, etc. In WSN, to eliminate the redundant information from the gathered data, data aggregation is a prime approach by which we can considerably reduce the energy consumption of each node. This reduction supports the achievement of the higher lifetime of WSN. In this paper, we have endeavored to introduce a review of various existing data aggregation algorithms in the literature. We have also tried to summarize all these existing data aggregation algorithms on the basis of different performance metrics such as energy consumption, network lifetime, delay, energy cost, etc. Finally, this paper supports the reader an idea to select the data aggregation algorithm for the desired application. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Data aggregation Centralized algorithms distributed algorithms Hybrid algorithm

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Decentralized Optimization Over Time-Varying Directed Graphs With Row and Column-Stochastic Matrices

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第11期65卷 4769-4780页

作者： Saadatniaki, Fakhteh Xin, Ran Khan, Usman A. Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA

In this article, we provide a distributed optimization algorithm, termed as TV-AB, that minimizes a sum of convex functions over time-varying, random directed graphs. Contrary to the existing work, the algorithm we propose does not require eigenvector estimation to estimate the (non-1) Perron eigenvector of a stochastic matrix. Instead, the proposed approach relies on a novel information mixing approach that exploits both row- and column-stochastic weights to achieve agreement toward the optimal solution when the underlying graph is directed. We show that TV-AB converges linearly to the optimal solution when the global objective is smooth and strongly convex, and the underlying time-varying graphs exhibit bounded connectivity, i.e., a union of every C consecutive graphs is strongly connected. We derive the convergence results based on the stability analysis of a linear system of inequalities along with a matrix perturbation argument. Simulations confirm the findings in this article.

关键词： Directed graphs Convergence Optimization Estimation Radio frequency Convex functions Standards Directed graphs distributed algorithms first-order methods optimization time-varying graphs

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Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction

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IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第12期66卷 7835-7855页

作者： Acharya, Jayadev Canonne, Clement L. Tyagi, Himanshu Cornell Univ Dept Elect & Comp Engn Ithaca NY 14853 USA IBM Res Almaden CA 95120 USA Indian Inst Sci Dept Elect Commun Engn Bengaluru 560012 India Indian Inst Sci Robert Bosch Ctr Cyberphys Syst Bengaluru 560012 India

Multiple players are each given one independent sample, about which they can only provide limited information to a central referee. Each player is allowed to describe its observed sample to the referee using a channel from a family of channels W, which can be instantiated to capture, among others, both the communication- and privacy-constrained settings. The referee uses the players' messages to solve an inference problem on the unknown distribution that generated the samples. We derive lower bounds for the sample complexity of learning and testing discrete distributions in this information-constrained setting. Underlying our bounds is a characterization of the contraction in chi-square distance between the observed distributions of the samples when information constraints are placed. This contraction is captured in a local neighborhood in terms of chi-square and decoupled chi-square fluctuations of a given channel, two quantities we introduce. The former captures the average distance between distributions of channel output for two product distributions on the input, and the latter for a product distribution and a mixture of product distribution on the input. Our bounds are tight for both public- and private-coin protocols. Interestingly, the sample complexity of testing is order-wise higher when restricted to private-coin protocols.

关键词： distributed algorithms inference algorithms statistical analysis minimax techniques parameter estimation

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