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distributed scheduling algorithms for channel access in TDMA wireless mesh networks

JOURNAL OF SUPERCOMPUTING 2008年第1期45卷 105-128页

作者： Cheng, Hongju Xiong, Naixue Yang, Larence T. Jeong, Young-Sik Fuzhou Univ Dept Comp Sci Fuzhou 350002 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 1C0 Canada Wonkwang Univ Dept Comp Engn Iksan South Korea

In this paper, we have considered the distributed scheduling problem for channel access in TDMA wireless mesh networks. The problem is to assign time-slot(s) for nodes to access the channels, and it is guaranteed that nodes can communicate with all their one-hop neighbors in the assigned time-slot(s). And, the objective is to minimize the cycle length, i.e., the total number of different time-slots in one scheduling cycle. In single-channel ad hoc networks, the best known result for this problem is proved to be K-2 in arbitrary graphs (Chlamtac and Pinter in IEEE Trans. Comput. C-36(6):729-737, 1987) and 25K in unit disk graphs (http://***/en/ds/***) with K as the maximum node degree. There are multiple channels in wireless mesh networks, and different nodes can use different control channels to reduce congestion on the control channels. In this paper, we have considered two scheduling models for wireless mesh networks. The first model is that each node has two radios, and the scheduling is simultaneously done on the two radios. We have proved that the upper bound of the cycle length in arbitrary graphs can be 2K. The second model is that the time-slots are scheduled for the nodes regardless of the number of radios on them. In this case, we have proved that the upper bound can be (4K-2). We also have proposed greedy algorithms with different criterion. The basic idea of these algorithms is to organize the conflicting nodes by special criterion, such as node identification, node degree, the number of conflicting neighbors, etc. And, a node cannot be assigned to a time-slot(s) until all neighbor nodes, which have higher criterion and might conflict with the current node, are assigned time-slot(s) already. All these algorithms are fully distributed and easy to realize. Simulations are also done to verify the performance of these algorithms.

关键词： scheduling algorithms TDMA (Time Division Multiple Access) wireless mesh networks distributed algorithms channel access

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Efficient distributed algorithms for Convolutional Neural Networks 21

Efficient Distributed Algorithms for Convolutional Neural Ne...

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Proceedings of the 33rd ACM Symposium on Parallelism in algorithms and Architectures

作者： Rui Li Yufan Xu Aravind Sukumaran-Rajam Atanas Rountev P. Sadayappan University of Utah Salt Lake City UT USA Washington State University Pullman WA USA Ohio State University Columbus OH USA

ISBN: (纸本)9781450380706

Several efficient distributed algorithms have been developed for matrix-matrix multiplication: the 3D algorithm, the 2D SUMMA algorithm, and the 2.5D algorithm. Each of these algorithms was independently conceived and they trade-off memory needed per node and the inter-node data communication volume. The convolutional neural network (CNN) computation may be viewed as a generalization of matrix-multiplication combined with neighborhood stencil computations. We develop communication-efficient distributed-memory algorithms for CNNs that are analogous to the 2D/2.5D/3D algorithms for matrix-matrix multiplication.

关键词： distributed algorithms neural networks communication optimization

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Efficient distributed algorithms for computing shortest pairs of maximally disjoint paths in communication networks

Efficient distributed algorithms for computing shortest pair...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： V. Rutenburg SRI International Inc. Menlo Park CA USA

distributed algorithms are presented for finding two maximally disjoint paths of minimum total length from each possible source node to a destination, including both node-disjoint and link-disjoint versions of the algorithm. A synchronous algorithm having communication complexity O( mod E mod log D+ mod mod V mod D) and time complexity O( mod V mod W), where mod V mod and mod E mod denote the number of nodes and links in G, W is the maximum link length, and D is the depth of a shortest-path spanning tree directed toward the destination, is presented. For the important case W=O(1), the time complexity becomes O( mod V mod ). In this case, the asynchronous algorithm obtained by applying Synchronizer alpha of B. Awerbuch (1987) has communication complexity O( mod E mod V mod ) and time complexity O( mod V mod ). Serial, centralized versions of the algorithms with time complexity O( mod E mod + mod V mod log mod V mod ) and space complexity O( mod E mod ) are also presented.< >

关键词： distributed algorithms distributed computing Intelligent networks Complexity theory Contracts Communication networks IEL Network topology Routing Robustness

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Resilience for Consensus-based distributed algorithms in Hostile Environment†

Resilience for Consensus-based Distributed Algorithms in Hos...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Xuan Wang Shaoshuai Mou Shreyas Sundaram School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Consensus-based distributed algorithms have been the key to many problems arising in multi-agent systems including reinforcement learning [1], [2], formation control [3], [4], task allocation [5]and so on. Byconsensushere is meant that all agents in the network reach an agreement regarding a certain quantity of interest [6], [7]. Bydistributedhere is meant that the whole multi-agent system achieve global objectives by only local coordination among nearby neighbors [8]. On one hand, the absence of central controllers in multi-agent systems make them inherently robust against individual agent/link failures. On the other hand, the high dependence of the whole system on local coordination also raises a significant concern that algorithms for multi-agent networks may be crashed down in the presence of even one malicious agent [9]. This has motivated us to develop methodologies to achieveresiliencein order to guarantee nice performance for consensus-based distributed algorithms especially in hostile environment. One challenge along this direction comes from the fact that each agent is usually with locally available information, which makes it very difficult to identify or isolate malicious agents [10]. The authors of [11]-[13]have achieved significant progress by showing that given N-adversarial nodes under Byzantine attacks, there exists a strategy for normal agents to achieve consensus if the network connectivity is 2 N+1. These results are usually computationally expensive, assume the network topology to be all-to-all networks, or require normal agents to be aware of non-local information. Most recently the authors of [14], [15]have investigated consensus-based distributed optimizations under adversarial agents. They have introduced a local filtering mechanism which allows each agent to discard the most extreme values in their neighborhood at each step. This is not directly applicable to consensus-based distributed computation algorithms [16]-[19], in which extreme val

关键词： distributed algorithms Optimization Resilience Multi-agent systems Robustness Sun Mathematical model

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Novel distributed Beamforming algorithms for Heterogeneous Space Terrestrial Integrated Network

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IEEE INTERNET OF THINGS JOURNAL 2022年第13期9卷 11351-11364页

作者： Shi, Xiaoyan Liu, Rongke Thompson, John S. Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Midlothian Scotland Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Edinburgh Sch Engn Inst Digital Communciat Edinburgh EH9 3FG Midlothian Scotland

An integrated space-terrestrial network based on the ultradense low-earth-orbit (LEO) satellite constellations has been envisioned in both 5G and beyond 5G (B5G) networks. This approach is a powerful solution to some key challenges from Internet of Things (IoT) services, such as the lack of link capacity to deal with large data transfer or coverage in the remote areas. This article focuses on the beamforming design for the transmissions from multiple LEO satellites, equipped with massive phased array antenna, to a large number of heterogeneous terrestrial terminals. Superposition coding-based beamforming is efficient in dealing with the receiver heterogeneity, but at the cost of higher computational complexity. Based on the dual decomposition theory as well as deep neural networks (DNNs), this article proposes to combine the nonlinear approximation ability of DNNs with distributed algorithms. This combination not only supports advanced nonorthogonal beamforming algorithms for achieving superior throughput performance, but also keeps the overall computational complexity low and enables the beamforming process to be speed up dramatically through parallel computing.

关键词： Array signal processing Satellites Low earth orbit satellites Receivers NOMA Quality of service Phased arrays Beamforming distributed algorithms low-earth-orbit (LEO) satellites receiver heterogeneity

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ShareCam part II: approximate and distributed algorithms for a collaboratively controlled robotic Webcam

ShareCam part II: approximate and distributed algorithms for...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： D. Song A. Pashkevich K. Goldberg IEOR Depamnent University of California Berkeley CA USA State University of Informatics and Radioelectronics Minsk Belarus IEOR and EECS Departments University of California Berkeley CA USA

ShareCam is a robotic pan, tilt, and zoom Web-based camera controlled by simultaneous frame requests from online users. Part I describes the system. This paper, part II, focuses on algorithms. The ShareCam problem is to find a camera frame that optimizes a measure of total user satisfaction. We present a grid-based approximation algorithm: given camera frame requests from n users, and approximation bound /spl epsi/, we analyze the trade of between solution quality and processing speed and prove that the algorithm runs in O(n//spl epsi//sup 3/) time. The algorithm can be distributed to run in O(1//spl epsi//sup 3/) time at each client and in O(n + 1//spl epsi//sup 3/) time at the server. Experiments suggest that performance of the distributed algorithm degrades gracefully as clients fail to complete their part of the computation.

关键词： distributed algorithms Robot control Cameras Robot vision systems Internet Radio control Displays Online Communities/Technical Collaboration Approximation algorithms Algorithm design and analysis

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On Some distributed Scheduling algorithms for Wireless Networks With Hypergraph Interference Models

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第5期67卷 2952-2957页

作者： Ganesan, Ashwin INSOFE Educ Private Ltd Int Sch Engn INSOFE Mumbai 400093 Maharashtra India

It is shown that the performance of the maximal scheduling algorithm in wireless ad hoc networks under the hypergraph interference model can be further away from optimal than previously known. The exact worst-case per... 详细信息

关键词： Interference Admission control Scheduling algorithms Schedules Wireless networks Scheduling Indexes Hypergraph interference models wireless networks admission control fractional chromatic number upper bounds maximal scheduling distributed algorithms

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Solving nonconvex power control problems in wireless networks: low SIR regime and distributed algorithms

Solving nonconvex power control problems in wireless network...

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GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.

作者： Chee Wei Tan D.P. Palomar Mung Chiang Department of Electrical Engineering Princeton University USA

In wireless cellular networks that are interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective subject to many QoS constraints from individual users. Previous work have been done in the high SIR regime by solving these problems with nonlinear objectives and constraints as geometric programs. However, in the medium to low SIR regime, these problems cannot be transformed into tractable convex optimization problems. This paper makes two contributions: (1) In the low SIR regime, we propose a method with centralized computation to obtain the globally optimal solution by solving a series of geometric programs. (2) While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been investigated before this paper. We present a systematic method of distributed algorithms for power control based on geometric programs in high SIR regime. These two contributions can be readily combined to distributively solve nonlinear power control problems in general SIR regime

关键词： Power control Intelligent networks Wireless networks distributed algorithms Quality of service Land mobile radio cellular systems Constraint optimization Interference constraints Throughput Power system modeling

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Group-Based distributed Auction algorithms for Multi-Robot Task Assignment

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第2期20卷 1292-1303页

作者： Bai, Xiaoshan Fielbaum, Andres Kronmuller, Maximilian Knoedler, Luzia Alonso-Mora, Javier Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Delft Univ Technol Dept Cognit Robot NL-2628 CD Delft Netherlands

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots' total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots' total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method.

关键词： Robots Task analysis Heuristic algorithms Robot sensing systems Collision avoidance Optimization distributed algorithms Multi-robot task assignment time-windows NP-hard distributed auction algorithm

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Local Prediction for Enhanced Convergence of distributed Optimization algorithms

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2018年第4期5卷 1962-1975页

作者： Mai, Van Sy Abed, Eyad H. Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

This paper studies distributed optimization problems where a network of agents seeks to minimize the sum of their private cost functions. We propose new algorithms based on the distributed subgradient method and the finite-time consensus protocol introduced by Sundaram and Hadjicostis (2007). In our first algorithm, the local optimization variables are updated cyclically through a subgradient step while the consensus variables follow a usual consensus protocol periodically interrupted by a predictive consensus estimate reset operation. For convex cost functions with bounded subgradients, this algorithm is guaranteed to converge to a certain range of the optimal value if using a constant step size or to the optimal value if a diminishing step size is in place. For differentiable cost functions whose sum is convex and has a Lipschitz continuous gradient, convergence to the optimal value can be ensured when using a constant step size, even if some of the individual cost functions are nonconvex. In addition, exponential convergence to the optimal solution is achieved when the global cost function is further assumed to be strongly convex. In these cases, the local optimization variables reach consensus in finite time, and then behave as they would under the centralized subgradient method applied to the global problem, except on a slower time scale. The second algorithm is specialized for the case of quadratic cost functions and converges in finite time to the optimal solution. Simulation examples are given to illustrate the algorithms.

关键词： distributed algorithms gradient methods graph theory network consensus

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