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An adaptive latency mitigation scheme for massively multiuser virtual environments

JOURNAL OF network AND COMPUTER APPLICATIONS 2009年第5期32卷 1049-1063页

作者： Hariri, Behnoosh Shirmohammadi, Shervin Pakravan, Mohammad Reza Alavi, Mohammad Hossein Univ Ottawa Sch Informat Technol & Engn Distributed Collaborat Virtual Environm Res DISCO Ottawa ON K1N 6N5 Canada Sharif Univ Technol Dept Elect Engn ACRI Tehran Iran

As massively multiuser virtual environments (MMVEs) expand in terms of size and user population, they tend toward using P2P architectures as a way to provide scalability without the need for large centralized resources. Distributed hash table (DHT)-based networks have been introduced as a promising option for overlay-based distributed massively multiuser virtual environment applications. However, overlay latency stretch seriously affects MMVE performance where QoS is crucial for real-time User collaboration. This work includes a series of efforts in the alleviation of such undesired latency. Our approach to latency mitigation consists of two phases. First, we propose a position-based ID assignment approach to minimize message hop-count by exploiting the clustered pattern of traffic exchange among MMVE Users. Second, we introduce a new ant-based distributed neighbor selection scheme that can be used by MMVE Users to select the best neighbors within their areas of interest. In order to evaluate the performance of this heuristic approach, we model the neighbor selection problem in the form of a network flow problem and use its solution as an optimality bound to compare the results. Simulation results demonstrate that the proposed algorithms will compensate for DHT latency stretch to a high extent and the performance of the resulting System would closely follow the optimal bound while communication overhead is negligible. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Massively multiuser virtual environments Hilbert curve Proximity neighbor selection network flow optimization Ant routing

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Dynamic Route Guidance Based on Model Predictive Control

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2013年第5期92卷 477-491页

作者： Zhou, Yonghua Yang, Xun Mi, Chao Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

Route selections for vehicles can be equivalent to determine the optimized operation processes for vehicles which intertwine with each other. This paper attempts to utilize the whole methodology of model predictive control to engender rational routes for vehicles, which involves three important parts, i.e. simulation prediction, rolling optimization and feedback adjustment. The route decisions are implemented over the rolling prediction horizon taking the real-time feedback information and the future intertwined operation processes into account. The driving behaviors and route selection speculations of drivers and even traffic propagation models are on-line identified and adapted for the simulation prediction in next prediction horizon. The mesoscopic traffic model is utilized for the simulation prediction so as to achieve both computing efficiency and prediction accuracy, where the partial link density in front of the vehicle rather than the density of total link is utilized to calculate the vehicle propagation velocity. The path traveling time is accumulated in a way related to the departure time and the operation process of a vehicle. The system architecture is composed of two parts. One is to simulate the true traffic system with stochastic behaviors such as speed fluctuations and inclinations to obey or disobey navigation commands, and the other one is the simulation prediction, rolling optimization and feedback adjustment system. In this way, the case study of medium traffic network shows that the simulation prediction-based rolling-horizon feedback implementation can prevent possible congestion in advance. It provides an engineering solution to the real-time closed-loop prediction-based traffic navigation.

关键词： route guidance dynamic traffic assignment model predictive control network flow optimization non-analytical iterative algorithm

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Supervised Feature Selection in Graphs with Path Coding Penalties and network flows

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JOURNAL OF MACHINE LEARNING RESEARCH 2013年第8期14卷 2449-2485页

作者： Mairal, Julien Yu, Bin Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA

We consider supervised learning problems where the features are embedded in a graph, such as gene expressions in a gene network. In this context, it is of much interest to automatically select a subgraph with few connected components;by exploiting prior knowledge, one can indeed improve the prediction performance or obtain results that are easier to interpret. Regularization or penalty functions for selecting features in graphs have recently been proposed, but they raise new algorithmic challenges. For example, they typically require solving a combinatorially hard selection problem among all connected subgraphs. In this paper, we propose computationally feasible strategies to select a sparse and well-connected subset of features sitting on a directed acyclic graph (DAG). We introduce structured sparsity penalties over paths on a DAG called "path coding" penalties. Unlike existing regularization functions that model long-range interactions between features in a graph, path coding penalties are tractable. The penalties and their proximal operators involve path selection problems, which we efficiently solve by leveraging network flow optimization. We experimentally show on synthetic, image, and genomic data that our approach is scalable and leads to more connected subgraphs than other regularization functions for graphs.

关键词： convex and non-convex optimization network flow optimization graph sparsity

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Optimal-constrained multicast sub-graph over coded packet networks

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JOURNAL OF COMBINATORIAL optimization 2015年第4期29卷 723-738页

作者： Raayatpanah, M. A. Fathabadi, H. Salehi Bahramgiri, H. Pardalos, P. M. Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran Univ Florida Dept Ind & Syst Engn Ctr Appl Optimizat Gainesville FL 32611 USA

network coding is a technique which can be used to improve the performance of multicast communications by performing encoding operations at intermediate nodes. In real-time multimedia communication applications, there are usually several weights associated with links, such as cost, delay, jitter, loss ratio, security, and so on. In this paper, we consider the problem of finding an optimal multicast sub-graph over coded packet networks, where the longest end-to-end weight from the source to each destination does not exceed an upper bound. First, a mixed integer programming model is proposed to formulate the problem which is NP-hard. Then, a column-generation approach is described for this problem, in which the problem is decomposed into a master linear programming problem and several integer programming sub-problems. Moreover, two methods based on linear and Lagrangian relaxation are proposed to compute a tight lower bound of the optimal solution value. Computational results show that the proposed algorithm provides an efficient way for solving the problem, even for relatively large networks.

关键词： Communication networks network coding network flow optimization Multicast

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Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017年第3期39卷 589-602页

作者： Wang, Bing Wang, Gang Chan, Kap Luk Wang, Li Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.

关键词： Multi-object tracking tracklet association target-specific metric learning motion dynamics network flow optimization

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Self-Supervised Association of Wi-Fi Probe Requests Under MAC Address Randomization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第12期22卷 7044-7056页

作者： He, Tianlang Tan, Jiajie Chan, S. -H. Gary Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

Wi-Fi-on devices such as smartphones search for network availability by periodically broadcasting probe requests which encapsulate MAC addresses as device identifiers. To protect identity privacy, modern devices embed random MAC addresses in probe frames, the so-called MAC address randomization. Such randomization disrupts the frame association, inadvertently frustrating identity-oblivious statistical analytic efforts such as people counting and trajectory inference. To address that, we propose Cappuccino, a novel privacy-preserving approach that captures the association of probe requests under MAC address randomization. Cappuccino first estimates pairwise frame correlation and then associates frames over time. For frame correlation, it employs a self-supervised estimator that jointly considers multiple modalities, i.e., information elements, sequence number, and received signal strength. For multiple frame association, Cappuccino formulates frames as a minimum-cost flow optimization. To the best of our knowledge, this is the first piece of work that leverages self-supervised learning to estimate frame correlation based on multiple modalities and formulates the probe request association problem as the network flow optimization. We have conducted extensive experiments in a leading and crowded shopping mall for more than three months. Cappuccino achieves remarkable performance in terms of V-measure scores (>0.85).

关键词： Wi-Fi MAC address randomization self-supervised learning multiple modalities network flow optimization

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Optimal information extraction in energy-limited wireless sensor networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2004年第6期22卷 1121-1129页

作者： Ordónez, F Krishnamachari, B Univ So Calif Dept Ind & Syst Engn Los Angeles CA 90036 USA Univ So Calif Dept Elect Engn Los Angeles CA 90036 USA

The current practice in wireless sensor networks (WSNs) is to develop functional system designs and protocols for information extraction using intuition and heuristics, and validate them through simulations and implementations. We address the need for a complementary formal methodology by developing nonlinear optimization models of static WSN that yield fundamental performance bounds and optimal designs. We present models both for maximizing the total information gathered subject to energy constraints (on sensing, transmission, and reception), and for minimizing the energy usage subject to:information constraints. Other constraints in these models correspond to fairness and channel capacity (assuming noise but no interference). We also discuss extensions of these models that can handle data aggregation, interference, and even node mobility. We present results and illustrations from computational experiments using these models that show how the, optimal solution varies as a function of the energy/information constraints, network size, fairness constraints, and reception power. We also compare the performance of some simple heuristics with respect to the optimal solutions.

关键词： maximum information extraction network flow optimization wireless sensor networks (WSNs)

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GIS-Based Decision Support System for Improved Operations and Efficiency Conservation in Large-Scale Irrigation Systems

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2012年第10期138卷 857-867页

作者： Triana, Enrique Labadie, John W. Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA MWH Amer Inc Ft Collins CO 80525 USA

The long-term sustainability of irrigated agriculture is contingent on application of cost-effective water conservation measures and improved management in the face of intense competition for limited freshwater resources from municipal, industrial, and environmental/ecological interests. A geographic information system (GIS)-based decision support system (GeoDSS) is presented for maximizing water savings from efficiency improvements and conservation measures through integrated management of large-scale irrigation systems. The GeoDSS incorporates the GeoMODSIM network flow optimization model as implemented within ArcGIS Desktop (ESRI, Inc.) and is applied to the large-scale, complex Imperial Irrigation District (IID) water delivery system in California in support of the IID/San Diego County Water Authority (SDCWA) Water Transfer Agreement. IID GeoDSS automatically constructs a GeoMODSIM flow network within an ESRI geodatabase composed of feature classes for IID canals, drains, reservoirs, farm gates, interceptors, and pumpback locations. Following calibration of the baseline network, GeoMODSIM simulations predict substantial water savings from mixtures of strategies of varying cost for improved water management and increased delivery system efficiency and on-farm water conservation, without the need for fallowing or retirement of IID irrigated lands. DOI: 10.1061/(ASCE)IR.1943-4774.0000481. (C) 2012 American Society of Civil Engineers.

关键词： Canals Decision support system Geographic information system Irrigation efficiency network flow optimization Water conservation Water delivery systems

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Assessing a Synergistic Use of Alternate Broadband Delivery Models in Rural Areas

Assessing a Synergistic Use of Alternate Broadband Delivery ...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Max-Onakpoya, Esther Baker, Corey E. Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

ISBN: (纸本)9781665416474

Access to broadband services is an important component in fostering the overall development of communities. However, some rural communities lack access to such services. To bridge this gap, various rural areas have adopted different kinds of alternate connectivity models. Such models attempt to mitigate the effect of the access gap on Internet-challenged rural areas. This work explores the synergistic use of such models by creating a theoretical framework for analyzing its benefits in predictable scenarios. More specifically, this work investigates a method of routing time-dependent data in quasi-deterministic delay tolerant networks that co-exist with communal access points within challenged rural environments. We formulate the problem as a maximum unsplittable multicommodity flow problem and propose an approximation algorithm that performs close to optimal.

关键词： rural connectivity network flow optimization multi-commodity flow alternate internet architectures

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network Coding-Based QoS and Security for Dynamic Interference-Limited networks

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20th International Conference on Computer networks (CN)

作者： Mohajer, Amin Mazoochi, Mojtaba Niasar, Freshteh Atri Ghadikolayi, Ali Azami Nabipour, Mohammad CSRI Integrated Network Management Grp Tehran Iran

ISBN: (纸本)9783642388651

In this paper first the problem of secure minimum-cost multicast with network coding is studied, while the maximum end-to-end parameters, such as security, delay, and rate are assumed to be bounded in one multicast session. We present a decentralized algorithm that computes minimum-cost QoS flow subgraphs in network coded multicast networks. In next stage we generalize this idea to interference-limited dynamic networks where capacities are functions of the signal-to-noise-interference ratio (SINR). Since dynamic link capacities can be controlled by varying transmission powers, minimum-cost multicast must be achived by jointly optimizing network coding subgraphs with power control schemes. Simulation results shows this approach provides an efficient way for solving the optimization problem. The optimization numerical results show that using power control algorithm, higher success ratio is obtained, in comparison with previous algorithms.

关键词： network coding Quality of Service network flow optimization security dynamic networks

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