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Exploring network selection techniques for multicast groups in heterogeneous wireless environments

INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2014年第1期10卷 43-68页

作者： Boudko, Svetlana Leister, Wolfgang Gjessing, Stein Norsk Regnesentral Oslo Norway Univ Oslo Oslo Norway

Purpose - Coexistence of various wireless access networks and the ability of mobile terminals to switch between them make an optimal selection of serving networks for multicast groups a challenging problem. Since optimal network selection requires large dimensions of data to be collected from several network locations and sent between several network components, the scalability can easily become a bottleneck in large-scale systems. Therefore, reducing data exchange within heterogeneous wireless networks is important. The paper aims to discuss these issues. Design/methodology/approach - The authors study the decision-making process and the data that need to be sent between different network components. To analyze the operation of the wireless heterogeneous network, the authors built a mathematical model of the network. The objective is defined as a minimization of multicast streams in the system. To evaluate the heuristic solutions, the authors define the upper and lower bounds to their operation. Findings - The proposed heuristic solutions substantially reduce the usage of bandwidth in mobile networks and exchange of information between the network components. Originality/value - The authors proposed the approach that allows network selection in a decentralized manner with only limited information shared among the decision makers. The authors studied how different sets of information available to decision makers influenced the performance of the system. The work also investigates the usage of multiple paths for multicast in heterogeneous mobile environments.

关键词： Keywords Simulation decentralized algorithm Heterogeneous wireless networking Multicast Network selection

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D-FW: COMMUNICATION EFFICIENT DISTRIBUTED algorithmS FOR HIGH-DIMENSIONAL SPARSE OPTIMIZATION 41

D-FW: COMMUNICATION EFFICIENT DISTRIBUTED ALGORITHMS FOR HIG...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lafond, Jean Wai, Hoi-To Moulines, Eric Telecom ParisTech CNRS LTCI Inst Mines Telecom Paris France Arizona State Univ Sch ECEE Tempe AZ 85287 USA Ecole Polytech CMAP Palaiseau France

ISBN: (纸本)9781479999880

We propose distributed algorithms for high-dimensional sparse optimization. In many applications, the parameter is sparse but high-dimensional. This is pathological for existing distributed algorithms as the latter require an information exchange stage involving transmission of the full parameter, which may not be sparse during the intermediate steps of optimization. The novelty of this work is to develop communication efficient algorithms using the stochastic Frank-Wolfe (sFW) algorithm, where the gradient computation is inexact but controllable. For star network topology, we propose an algorithm with low communication cost and establishes its convergence. The proposed algorithm is then extended to perform decentralized optimization on general network topology. Numerical experiments are conducted to verify our findings.

关键词： decentralized algorithm sparse optimization large-scale optimization communication efficient algorithm

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Range-Free Localization using Grid Graph Extraction

Range-Free Localization using Grid Graph Extraction

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20th IEEE International Conference on Network Protocols (ICNP)

作者： Kubo, Takeshi Tagami, Atsushi Hasegawa, Teruyuki Hasegawa, Toru Walrand, Jean KDDI R&D Labs Inc 2-1-15 Ohara Fujimino City Saitama 2568502 Japan Univ Calif Berkeley Dept EECS Berkeley CA USA

ISBN: (纸本)9781467324472;9781467324458

This paper proposes a new type of range-free localization method based on affine transformation. Nodes extract subgraphs with a grid topology from a sensor network and assign x-y coordinates to themselves in a decentralized manner. The nodes estimate their positions using an affine transformation based on the mapping of the physical positions and the x-y coordinates of three anchors in an extracted graph. In contrast with multilateration-based localization methods, the proposed method works well even in a non-convex hull deployment, such as a terrain with big regions without sensors. We provide a theoretical analysis and simulation results. We also present a strategy for minimizing the position estimation error and maximizing the coverage of the proposed method. In the simulation results, the position estimation error is 0.18 (normalized by the radio communication range) and the coverage is almost 100% in a non-convex hull deployment.

关键词： Range-free localization grid graph decentralized algorithm affine transformation

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LINEAR SPEEDUP IN SADDLE-POINT ESCAPE FOR decentralized NON-CONVEX OPTIMIZATION

LINEAR SPEEDUP IN SADDLE-POINT ESCAPE FOR DECENTRALIZED NON-...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Vlaski, Stefan Sayed, Ali H. Ecole Polytech Fed Lausanne Sch Engn Lausanne Switzerland

ISBN: (纸本)9781509066315

Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to noncooperative approaches in the strongly-convex setting. More recently, these results have been extended to the pursuit of first-order stationary points in non-convex environments. In this work, we examine in detail the dependence of second-order convergence guarantees on the spectral properties of the combination policy for non-convex multi agent optimization. We establish linear speedup in saddle-point escape time in the number of agents for symmetric combination policies and study the potential for further improvement by employing asymmetric combination weights. The results imply that a linear speedup can be expected in the pursuit of second-order stationary points, which exclude local maxima as well as strict saddle-points and correspond to local or even global minima in many important learning settings.

关键词： Non-convex optimization saddle-point second-order stationarity minima decentralized algorithm centralized algorithm diffusion strategy

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decentralized Constrained Optimization With Dynamic Penalty For Harmonics Estimation

Decentralized Constrained Optimization With Dynamic Penalty ...

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Chinese Automation Congress (CAC)

作者： Wang, Shiqiang Xing, Jianchun Jiang, Ziyan Dai, Yunchuang PLA Univ Sci & Technol Coll Def Engn Nanjing Jiangsu Peoples R China Tsinghua Univ Bldg Energy Res Ctr Beijing Peoples R China

ISBN: (纸本)9781538635247

A novel decentralized method for harmonics detection and suppression is proposed. Compared with the traditional centralized style, harmonics optimal estimation task in micro-grid system is scattered to smart phasor measurement unit (PMU) nodes without a monitoring host. Similar to the structure, mechanism and characteristics of biological communities, a smart PMU can communicate with adjacent nodes and operate collaboratively to complete harmonics optimal estimation in a new fully distributed flat network. The task is formulated as a constrained optimization problem conducted using basic physical equations and solved by decentralized approach with varying penalty parameter. Convergence property of the novel method is analyzed theoretically. Simulation results of the harmonics estimation in micro-grid system illustrate the effectiveness of the proposed method.

关键词： harmonics estimation PMUs micro-grid system decentralized algorithm penalty function

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decentralized LOW-RANK MATRIX COMPLETION

DECENTRALIZED LOW-RANK MATRIX COMPLETION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ling, Qing Xu, Yangyang Yin, Wotao Wen, Zaiwen Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9781467300469

This paper introduces algorithms for the decentralized low-rank matrix completion problem. Assume a low-rank matrix W - [W-1, W-2, ..., W-L]. In a network, each agent l observes some entries of W-l. In order to recover the unobserved entries of W via decentralized computation, we factorize the unknown matrixWas the product of a public matrix X, common to all agents, and a private matrix Y = [Y-1, Y-2, ..., Y-L], where Y-l is held by agent l. Each agent l alternatively updates Y-l and its local estimate of X while communicating with its neighbors toward a consensus on the estimate. Once this consensus is ( nearly) reached throughout the network, each agent l recoversW(l) = XYl and thus W is recovered. The communication cost is scalable to the number of agents, and W-l and Y-l are kept private to agent l to a certain extent. The algorithm is accelerated by extrapolation and compares favorably to the centralized code in terms of recovery quality and robustness to rank over-estimate.

关键词： decentralized algorithm low-rank matrix completion matrix factorization privacy protection

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C-ABT: A continuous control layer for inter-agent collision avoidance based on asynchronous backtracking

C-ABT: A continuous control layer for inter-agent collision ...

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IEEE Conference on Control and Applications (CCA)

作者： Mueller, Joachim Ruiz, Alberto Viseras Manss, Christoph Wiedemann, Thomas German Aerosp Ctr DLR Inst Commun & Nav Munchner Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781479977871

When using multiple robotic agents for waypoint-based exploration or coverage tasks, collision avoidance between agents is an important issue. With a centralized planner, this issue arises only at one central instance. However, when using asynchronous and/or decentralized algorithms, decentralized methods for collision avoidance need to be used. We propose a novel approach based on the asynchronous backtracking (ABT) algorithm which provides decentralized constraint satisfaction called continuous ABT(C-ABT). C-ABT is a continuous extension to ABT intended as a collision avoidance layer for existing multi-agent waypoint navigation. It extends ABT in the sense that participating agents know when they found a valid solution. In this case, agents can safely move to their selected waypoints. While ABT only finds one static solution, C-ABT is suited for continuously providing new waypoints for all agents. By using the concept of local neighborhoods, C-ABT is also scalable to swarms of arbitrary size.

关键词： autonomous aerial vehicles collision avoidance constraint satisfaction problems decentralised control microrobots mobile robots multi-agent systems multi-robot systems C-ABT MAV continuous asynchronous backtracking continuous control layer decentralized algorithm decentralized constraint satisfaction interagent collision avoidance microaerial vehicle multiple robotic agent waypoint navigation Atmospheric measurements Collision avoidance Current measurement Navigation Optimization Particle measurements Robots Collision avoidance constraint satisfaction problems Particle measurements decentralised control atmospheric monitoring multi-robot systems Microbots Current measuring Electric current measurement KLHL2 gene navigation Mobile robots Multi-agent systems Robot Agents

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Tracking of Closed-Curve Trajectories for Multi-Robot Systems

Tracking of Closed-Curve Trajectories for Multi-Robot System...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Sabattini, Lorenzo Secchi, Cristian Fantuzzi, Cesare Possamai, Daniel de Macedo Univ Bologna Dept Elect Comp Sci & Syst DEIS I-40126 Bologna Italy Univ Modena Dept Sci Methods Engn DISMI Reggio Emilia Italy Univ Fed Santa Catarina Dept Automat & Syst Engn DAS Florianopolis SC Brazil

ISBN: (纸本)9781424466757

In this paper we address the trajectory tracking problem for groups of mobile robots. We consider trajectories described by completely arbitrary shaped closed curves. The proposed control strategy is a completely decentralized algorithm, and does not require any global synchronization. The desired behavior is obtained by means of some properly designed artificial potential functions.

关键词： artificial potential function decentralized algorithm global synchronization mobile robot mobile robots multi-robot systems path planning position control robot dynamics trajectory tracking

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Multi-user Edge-assisted Video Analytics Task Offloading Game based on Deep Reinforcement Learning 26

Multi-user Edge-assisted Video Analytics Task Offloading Gam...

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26th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Chen, Yu Zhang, Sheng Xiao, Mingjun Qian, Zhuzhong Wu, Jie Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Suzhou Inst Adv Study Hefei Peoples R China Temple Univ Ctr Networked Comp Philadelphia PA 19122 USA

ISBN: (纸本)9781728190747

With the development of deep learning, artificial intelligence applications and services have boomed in the recent years, including recommendation systems, personal assistant and video analytics. Similar to other services in the edge computing environment, artificial intelligence computing tasks are pushed to the network edge. In this paper, we consider the multi-user edge-assisted video analytics task offloading (MEVAO) problem, where users have video analytics tasks with various accuracy requirements. All users independently choose their accuracy decisions, satisfying the accuracy requirement, and offload the video data to the edge server. With the utility function designed based on the features of video analytics, we model MEVAO as a game theory problem and achieve the Nash equilibrium, For the flexibility of making accuracy decisions under different circumstances, a deep reinforcement learning approach is applied to our problem. Our proposed design has much better performance compared with some other approaches in the extensive simulations.

关键词： edge computing video analytics task offloading decentralized algorithm game theory Markov decision process deep reinforcement learning

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Research on Task Allocation of Heterogeneous UAVs Based on DLR-CBBA 4

Research on Task Allocation of Heterogeneous UAVs Based on D...

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4th International Conference on Computer, Control and Robotics (ICCCR)

作者： Yan, Shaokun Pan, Feng Shi, YanShan Xu, Jihua Zhang, Doudou Beijing Inst Technol Sch Automat Beijing Peoples R China Jiangsu Automat Res Inst Lianyungang Peoples R China

ISBN: (纸本)9798350373141;9798350373158

This paper considers the problem of heterogeneous UAVs task allocation in a distributed multi-UAV system under strict time constraints, where other constraints need also be considered. Due to the limited capability of each UAV, multiple UAVs are required to perform a task simultaneously. The presented algorithm termed double layer cycle consensus based bundle algorithm (DLC-CBBA) is an extension to consensus-based bundle algorithm(CBBA). This paper improves the baseline CBBA from the aspects of capability matrix, objective function, task bundle construction, consistency processing, and so on. Extensive simulation results show that conflict-free task assignments are gained based on DLC-CBBA, and the convergence time is much less than the discrete particle swarm optimization (DPSO) when performing multiple-UAV tasks.

关键词： task allocation UAV CBBA time constraints decentralized algorithm

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