Dynamic Throughput Optimization in Wireless Multi-Hop Networks

2013 ◽  
Vol 284-287 ◽  
pp. 2735-2740
Author(s):  
Jain Shing Liu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Distributed Algorithm ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Cross Layer ◽  
Throughput Optimization ◽  
Computational Results ◽  
Cross Layer Optimization ◽  
Stream Control

In this paper, we study the throughput optimization problem in wireless multi-hop networks. For this problem, we introduce a cross-layer formulation to accommodate routing, scheduling and stream control from different layers of network with relevant constraints. Specially, by using a Lagrangian approach and a Markov Chain Monte Carlo method, we extend the programming-based formulation to a distributed algorithm that can dynamically approximate the optimal solution without the overhead of centralization. Finally, through computational results, we discuss the insight that can be gained from the cross-layer optimization and the distributed algorithm.

scholarly journals Bayesian Metric Multidimensional Scaling

2013 ◽  
Vol 21 (1) ◽  
pp. 125-140 ◽  
Author(s):  
Ryan Bakker ◽  
Keith T. Poole
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Optimal Solution ◽  
Full Rank ◽  
Mcmc Methods ◽  
Ratio Scale ◽  
Posterior Distributions ◽  
Point Estimates ◽  
Inflection Points

In this article, we show how to apply Bayesian methods to noisy ratio scale distances for both the classical similarities problem as well as the unfolding problem. Bayesian methods produce essentially the same point estimates as the classical methods, but are superior in that they provide more accurate measures of uncertainty in the data. Identification is nontrivial for this class of problems because a configuration of points that reproduces the distances is identified only up to a choice of origin, angles of rotation, and sign flips on the dimensions. We prove that fixing the origin and rotation is sufficient to identify a configuration in the sense that the corresponding maxima/minima are inflection points with full-rank Hessians. However, an unavoidable result is multiple posterior distributions that are mirror images of one another. This poses a problem for Markov chain Monte Carlo (MCMC) methods. The approach we take is to find the optimal solution using standard optimizers. The configuration of points from the optimizers is then used to isolate a single Bayesian posterior that can then be easily analyzed with standard MCMC methods.

scholarly journals Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986701 ◽  
Author(s):  
Yongqiang He ◽  
Mingming Yang
Keyword(s):  
Sensor Network ◽  
Utility Maximization ◽  
Optimization Problem ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Chance Constrained Programming ◽  
Network Utility Maximization ◽  
Cross Layer Optimization ◽  
Network Utility ◽  
The Cross

Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.

scholarly journals Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants

Algorithms ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 21
Author(s):  
Qiao Yan ◽  
Xiaoqian Liu ◽  
Xiaoping Deng ◽  
Wei Peng ◽  
Guiqing Zhang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Energy Use ◽  
Time Series Data ◽  
Optimal Solution ◽  
Working Hours ◽  
Series Data ◽  
Air Conditioner ◽  
Desktop Computer

Prediction of energy use behaviors is a necessary prerequisite for designing personalized and scalable energy efficiency programs. The energy use behaviors of office occupants are different from those of residential occupants and have not yet been studied as intensively as residential occupants. This paper proposes a method based on Markov chain Monte Carlo (MCMC) to predict the energy use behaviors of office occupants. Firstly, an indoor electrical Internet of Things system (IEIoTS) for the office scenario is developed to collect the switching state time series data of selected user electrical equipment (desktop computer, water dispenser, light) and the historical environment parameters. Then, the Metropolis–Hastings (MH) algorithm is used to sample and obtain the optimal solution of the parameters for the office occupants’ behavior function, the model of which includes the energy action model, energy working hours model, and air-conditioner energy use behavior model. Finally, comparative experiments are carried out to evaluate the performance of the proposed method. The experimental results show that while the mean value performs similarly in estimating the energy use model, the proposed method outperforms the Maximum Likelihood Estimation (MLE) method on uncertainty quantification with relatively narrower confidence intervals.

scholarly journals A comparison between Gauss-Newton and Markov-chain Monte Carlo–based methods for inverting spectral induced-polarization data for Cole-Cole parameters

Geophysics ◽  
2008 ◽  
Vol 73 (6) ◽  
pp. F247-F259 ◽  
Author(s):  
Jinsong Chen ◽  
Andreas Kemna ◽  
Susan S. Hubbard
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Optimal Solution ◽  
Stochastic Method ◽  
Objective Functions ◽  
Unknown Parameters ◽  
Deterministic Method ◽  
Initial Values ◽  
Spectral Induced Polarization

We have developed a Bayesian model to invert spectral induced-polarization (SIP) data for Cole-Cole parameters using Markov-chain Monte Carlo (MCMC) sampling methods. We compared the performance of the MCMC-based stochastic method with an iterative Gauss-Newton-based deterministic method for Cole-Cole parameter estimation through inversion of synthetic and laboratory SIP data. The Gauss-Newton-based method can provide an optimal solution for given objective functions under constraints, but the obtained optimal solution generally depends on the choice of initial values and the estimated uncertainty information often is inaccurate or insufficient. In contrast, the MCMC-based inversion method provides extensive globalinformation on unknown parameters, such as the marginal probability distribution functions, from which we can obtain better estimates and tighter uncertainty bounds of the parameters than with the deterministic method. In addition, the results obtained with the MCMC method are independent of the choice of initial values. Because the MCMC-based method does not explicitly offer a single optimal solution for given objective functions, the deterministic and stochastic methods can complement each other. For example, the stochastic method can be used first to obtain the medians of unknown parameters by starting from an arbitrary set of initial values. The deterministic method then can be initiated using the medians as starting values to obtain the optimal estimates of the Cole-Cole parameters.

scholarly journals The SS-SCR Scheme for Dynamic Spectrum Access

2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
Vinay Thumar ◽  
Taskeen Nadkar ◽  
U. B. Desai ◽  
S. N. Merchant
Keyword(s):  
Dynamic Spectrum Access ◽  
Optimization Problem ◽  
Activity Patterns ◽  
Cooperative Relaying ◽  
Dynamic Spectrum ◽  
Cross Layer ◽  
Spectrum Access ◽  
Cross Layer Optimization ◽  
Simulation Results ◽  
Symbiotic Cooperative Relaying

We integrate the two models ofCognitive Radio (CR), namely, the conventionalSense-and-Scavenge (SS) Model and Symbiotic Cooperative Relaying (SCR). The resultant scheme, calledSS-SCR, improves the efficiency of spectrum usage and reliability of the transmission links.SS-SCRis enabled by a suitable cross-layer optimization problem in a multihop multichannel CR network. Its performance is compared for different PU activity patterns with those schemes which considerSSandSCRseparately and perform disjoint resource allocation. Simulation results depict the effectiveness of the proposedSS-SCRscheme. We also indicate the usefulness of cloud computing for a practical deployment of the scheme.

Agricultural Field Coverage Using Cooperating Unmanned Ground Vehicles

2019 ◽  
Author(s):  
Saba Faryadi ◽  
Mohammadreza Davoodi ◽  
Javad Mohammadpour Velni
Keyword(s):  
Water Stress ◽  
Mobile Robots ◽  
Biotic Stress ◽  
Distributed Algorithm ◽  
Optimization Problem ◽  
Optimal Solution ◽  
High Water ◽  
Simulation Studies ◽  
Agricultural Field ◽  
Ground Vehicles

Abstract In this paper, a distributed algorithm with obstacle avoidance capability is presented to deploy a group of ground robots for field-based agriculture applications. To this end, the field (consisting of many plots) is first modeled as a directed graph, and the robots are deployed to collect data from some important areas of the field (e.g., areas with high water stress or biotic stress). The key idea is to formulate the underlying problem as a locational optimization problem and then find the optimal solution based on the Voronoi partitioning of the associated graph. The proposed partitioning method is validated through simulation studies, as well as experiments using a group of mobile robots.

scholarly journals RJMCMC based Text Placement to Optimize Label Placement and Quantity

2018 ◽  
Vol 1 ◽  
pp. 1-3
Author(s):  
Guillaume Touya ◽  
Thibaud Chassin
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Reversible Jump ◽  
Optimal Position ◽  
Label Placement ◽  
Map Design ◽  
First Results

Label placement is a tedious task in map design, and its automation has long been a goal for researchers in cartography, but also in computational geometry. Methods that search for an optimal or nearly optimal solution that satisfies a set of constraints, such as label overlapping, have been proposed in the literature. Most of these methods mainly focus on finding the optimal position for a given set of labels, but rarely allow the removal of labels as part of the optimization. This paper proposes to apply an optimization technique called Reversible-Jump Markov Chain Monte Carlo that enables to easily model the removal or addition during the optimization iterations. The method, quite preliminary for now, is tested on a real dataset, and the first results are encouraging.

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

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