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M-Polar: Channel Allocation for Throughput Maximization in SDR Mesh Networks

M-Polar: Channel Allocation for Throughput Maximization in S...

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2010 Proceedings IEEE INFOCOM

作者： Eugene Chai Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engioneering and Computer Science The University of Michigan Ann Arbor MI 48109-2121 U.S.A.

ISBN: (纸本)9781424458363

In traditional wireless networks, nodes use only a single channel per radio interface, thus limiting the overall channel diversity of the network. This restriction is due to the inherent limitations of commercially-available RF devices. With the advent of high-bandwidth software-defined radios (SDRs), we now have the option of assigning multiple contiguous, independent channels to a single wireless interface. This new-found opportunity raises an important question: how do we assign contiguous channels to nodes in order to maximize overall network throughput? This question lies at the often-ignored intersection of single-radio-multi-channel and multi-radio-multi-channel assignment schemes. In this paper, we develop a protocol that assigns contiguous channels with the goal of evenly spreading the load across the multiple channels. Neighboring nodes greedily adjust their channel ranges according to channel conditions to achieve an overall pattern of partially-overlapping bandwidths that maximizes the network throughput. The end-result is a network that can dynamically adapt its bandwidth usage to the network load and the conditions of the different channels. The proposed protocol is evaluated with a prototype built upon the USRP as well as with detailed simulation.

关键词： Channel allocation RF Devices radio interface Ganglia Mesh networks single-channel MULTICHANNEL Network throughput SDPR gene LOAD protocols Software radio Bandwidth

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Impact of mobility on spectrum sensing in cognitive radio networks

Impact of mobility on spectrum sensing in cognitive radio ne...

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2009 ACM Workshop on Cognitive Radio Networks, CoRoNet'09, Co-located with the Mobile computing and Networking Conference, MobiCom'09

作者： Min, Alexander W. Shin, Kang G. Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2121 United States

ISBN: (纸本)9781605587387

In cognitive radio networks (CRNs), spectrum sensing is key to opportunistic spectrum access while preventing any unacceptable interference to primary users' communications. Although cognitive radios function as spectrum sensors and move around, most, if not all, of existing approaches assume stationary spectrum sensors, thus providing inaccurate sensing results. As part of our effort to solve/alleviate this problem, we consider the impact of sensor mobility on spectrum sensing performance in a joint optimization framework for sensor cooperation and sensing scheduling. We show that sensor mobility increases spatio-temporal diversity in received primary signal strengths, and thus, improves the sensing performance. This is intuitively plausible, but have not been tested previously. Based on this observation, we propose a sensing strategy that minimizes the sensing overhead by finding an optimal combination of the number of sensors to cooperate and the number of times spectrum sensing must be scheduled. This result provides a useful insight to understand the spectrum sensing and its coupling with sensor mobility. Copyright 2009 ACM.

关键词： Cognitive radio

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An introduction to the echo state network and its applications in power system

An introduction to the echo state network and its applicatio...

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2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

作者： Dai, Jing Venayagamoorthy, Ganesh K. Harley, Ronald G. Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA 30332 United States Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9781424450985

Echo State Network (ESN) is a new type of Recurrent Neural Network (RNN) proposed in recent years. The training process of ESN is easier and requires less computational effort than regular RNN which has the same size. Due to its high modeling capability of complex dynamic system, ESN has been used in various power system applications such as power system nonlinear load modeling and true harmonic current detection, wide area monitoring, intelligent control of an Active Power Filter (APF), overhead conductor thermal dynamics identification, wind speed or water inflow forecasting, etc. This paper introduces the basic concept and the offline and online training algorithms of the ESN in detail and reviews the state of the art of ESN applications in power systems. © 2009 IEEE.

关键词： Recurrent neural networks

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Predictive routing of contexts in an overlay network

Predictive routing of contexts in an overlay network

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Hahnsang Kim Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

While mobile nodes (MNs) undergo handovers across inter-wireless access networks, their contexts must be propagated for seamless re-establishment of on-going application sessions, including IP header compression, secure mobile IP, authentication, authorization, and accounting services, to name a few. Routing contexts via an overlay network either on-demand or based on prediction of an MNs' mobility, introduces a new challenging requirement of context management. This paper proposes a context router (CXR) that manages contexts in an overlay network. A CXR is responsible for (1) monitoring of MNs' cross-handover, (2) analysis of MNs' movement patterns, and (3) context routing ahead of each MN's arrival at an AP or a network. The predictive routing of contexts is performed based on statistical learning of (dis)similarities between the patterns obtained from vector distance measurements. The proposed CXR has been evaluated on a prototypical implementation based on an MN mobility model in an emulated access network. Our evaluation results show that the prediction mechanisms applied on the CXR outperform a Kalman-filter-based method with respect to both prediction accuracy and computation performance.

关键词： Routing Context-aware services Authentication Authorization Monitoring Pattern analysis Performance evaluation Statistical learning Distance measurement Prototypes

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On Dynamic Reconfiguration of a Large-Scale Battery System

On Dynamic Reconfiguration of a Large-Scale Battery System

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IEEE Symposium on real-time and Embedded Technology and Applications

作者： Hahnsang Kim Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

Electric vehicles powered with large-scale battery packs are gaining popularity as gasoline price soars. Large-scale battery packs usually consist of an estimated 12,000 battery cells connected in series and parallel, which are susceptible to battery-cell failures. Unfortunately, current battery-management systems are unable to handle the inevitable battery-cell failures very well. To address this problem, we propose a dynamic reconfiguration framework that monitors, reconfigures, and controls large-scale battery packs online. The framework is built upon a syntactic bypassing mechanism that provides a set of rules for changing the battery-pack configuration, and a semantic bypassing mechanism by which the battery-cell connectivity is reconfigured to recover from a battery-cell failure. In particular, the semantic bypassing mechanism is dictated by constant-voltage-keeping and dynamic-voltage-allowing policies. The former policy is effective in preventing unavoidable voltage drops during the battery discharge, while the latter policy is effective in supplying different amounts of power to meet a wide-range of application requirements. Our experimental evaluation has shown the proposed framework to enable the battery packs to be 9 times as fault-tolerant as a legacy scheme.

关键词： Large-scale systems Vehicle dynamics Centralized control Control systems Voltage real time systems Battery management systems Application software Embedded computing Power engineering computing

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Scheduling of Battery Charge, Discharge, and Rest

Scheduling of Battery Charge, Discharge, and Rest

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real-time Systems Symposium (RTSS)

作者： Hahnsang Kim Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

Electric vehicles operate inefficiently with a naive battery management system that charges or discharges battery cells in a pack based solely on application load demands. The battery pack's operation-time and lifetime can be extended significantly by effectively scheduling (the cyber part) battery charge, discharge, and rest activities, based on the battery characteristics (the physical part). We propose a set of policies for scheduling battery-cell activities, called the weighted-k round-robin (kRR) scheduling framework. This framework dynamically adapts battery-cell activities to load demands and the condition of individual cells, thereby extending the battery pack's operation-time and making them robust to anomalous voltage-imbalances. The framework comprises two key components. First, an adaptive filter estimates the upcoming load demand. Then, based on the estimated load demand, the kRR scheduler determines the number of parallel-connected cells to be discharged simultaneously. The scheduler also effectively partitions the cells in the pack, allowing the cells to be simultaneously charged and discharged in coordination with the battery reconfiguration system we developed earlier [17]. Besides the kRR scheduling framework, we characterize the discharge and recovery efficiency of a Lithium-ion battery cell. The kRR scheduling framework is shown to outperform three alternative scheduling mechanisms with respect to the operation-time by 7-56%, and improve the tolerance of voltage-imbalance by up to 50%.

关键词： real time systems Robustness Global warming Threshold voltage Processor scheduling Laboratories Battery powered vehicles Battery management systems Application software Adaptive filters

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Load forecasting of a desert: A computational intelligence approach

Load forecasting of a desert: A computational intelligence a...

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2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

作者： Saber, Ahmed Yousuf Al-Shareef, Abdulaziz Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409-0040 United States Electrical and Computer Engineering Department King Abdulaziz University Jeddah KSA Saudi Arabia

ISBN: (纸本)9781424450985

This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research, two models are implemented - firstly load forecasting model for prediction;however, it is not sufficient for desired level of accurate forecasting, and secondly, optimization to improve the results up to at least better than existing results. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is a mathematical tool for mapping complex relations;it is well proved for the successful use of prediction, function approximation with dynamics, categorization, classification, and so forth. In this research, 24- step ahead calculations are performed in the ANN model and results are moderate. On the other hand, PSO is the most promising optimization tool. It is a swarmed based optimization method;it has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;it can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization tool that is applied on the weight matrix of ANN to improve results. In this research, PSO reliably and accurately tracks the continuously changing weights of ANN for uncertain load demand. By analyzing the model of ANN for the load-forecasting problem of SEC-WOA with hundreds of thousands of data and changing-uncertain load demand, the PSO is applied for the ANN weight adjustment and to optimize the uncertain load demand, as the ANN is not an optimization method. Results

关键词： Neural networks

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Comparison of feedforward and feedback neural network architectures for short term wind speed prediction

Comparison of feedforward and feedback neural network archit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Richard L. Welch Stephen M. Ruffing Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri S & T MO USA

This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP performance is comparable. The better performance of the feedback architectures is also shown using the mean absolute relative error. While the SRN performance is superior, the increase in required training time for the SRN over the other networks may be a constraint, depending on the application.

关键词： Neurofeedback Neural networks Feedforward neural networks Wind speed Recurrent neural networks Multi-layer neural network Particle swarm optimization Multilayer perceptrons Testing Meteorology

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Economic dispatch of a differential evolution based generator maintenance scheduling of a power system

Economic dispatch of a differential evolution based generato...

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IEEE General Meeting Power& Energy Society

作者： Y. Yare G. K. Venayagamoorthy A. Y. Saber Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

This paper presents generator maintenance scheduling (GMS) and economic dispatch (ED) of the Indonesian power system based on minimization of an economic cost function for the economical and reliable operation of a power system, while satisfying crew/manpower, maintenance window, load demand, generation limits and spinning reserve constraints. Differential evolution (DE), an evolutionary computation technique and an optimization algorithm that utilizes the differential information to guide its further search, is known to effectively solve large scale optimization problems and has been widely applied in power system. In this paper, the DE approach is proposed for solving the GMS problem and the resulting optimal maintenance schedules are used by the Lambda-iteration (LI) method to calculate the ED for the 19 generating units supplying power to two major industrial parks located in Bintan and Batam in Indonesian. The simulation results show great prospect for application in control centers by solving GMS and ED problems of many utilities which seek to minimize running costs. The results obtained offer an effective alternative for solving the GMS and ED problems.

关键词： Power system economics Power generation economics Power generation Power systems Power system reliability Maintenance Job shop scheduling Processor scheduling Cost function Spinning

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Getting an NSF CAREER award and beyond

Getting an NSF CAREER award and beyond

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IEEE PES Power Systems Conference and Exposition, PSCE

作者： Ganesh K. Venayagamoorthy Real-Time and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

The National Science Foundation (NSF), USA, CAREER program is a premier program that emphasizes the importance the Foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper presents the research and education experiences gained by the principal investigator - Venayagamoorthy and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the Power, Control and Adaptive Networks (PCAN) program of the electrical, Communications and Cyber Systems Division, effective June 1, 2004. The main emphasis of this paper is how and what a CAREER proposal awardee do to be a double winner in all aspects - research, education and outreach. In addition, suggestions on writing a winning NSF CAREER proposal are presented.

关键词： Awards

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