3,994 research outputs found
Mobility modeling and management for next generation wireless networks
Mobility modeling and management in wireless networks are the set of tasks performed in order to model motion patterns, predict trajectories, get information on mobiles\u27 whereabouts and to make use of this information in handoff, routing, location management, resource allocation and other functions.
In the literature, the speed of mobile is often and misleadingly referred to as the level of mobility, such as high or low mobility. This dissertation presents an information theoretic approach to mobility modeling and management, in which mobility is considered as a measure of uncertainty in mobile\u27s trajectory, that is, the mobility is low if the trajectory of a mobile is highly predictable even if the mobile is moving with high speed. On the other hand, the mobility is high if the trajectory of the mobile is highly erratic. Based on this mobility modeling concept, we classify mobiles into predictable and non-predictable mobility classes and optimize network operations for each mobility class. The dynamic mobility classification technique is applied to various mobility related issues of the next generation wireless networks such as location management, location-based services, and energy efficient routing in multihop cellular networks
Design and theoretical analysis of advanced power based positioning in RF system
Accurate locating and tracking of people and resources has become a fundamental requirement for many applications. The global navigation satellite systems (GNSS) is widely used. But its accuracy suffers from signal obstruction by buildings, multipath fading, and disruption due to jamming and spoof. Hence, it is required to supplement GPS with inertial sensors and indoor localization schemes that make use of WiFi APs or beacon nodes. In the GPS-challenging or fault scenario, radio-frequency (RF) infrastructure based localization schemes can be a fallback solution for robust navigation. For the indoor/outdoor transition scenario, we propose hypothesis test based fusion method to integrate multi-modal localization sensors. In the first paper, a ubiquitous tracking using motion and location sensor (UTMLS) is proposed. As a fallback approach, power-based schemes are cost-effective when compared with the existing ToA or AoA schemes. However, traditional power-based positioning methods suffer from low accuracy and are vulnerable to environmental fading. Also, the expected accuracy of power-based localization is not well understood but is needed to derive the hypothesis test for the fusion scheme. Hence, in paper 2-5, we focus on developing more accurate power-based localization schemes. The second paper improves the power-based range estimation accuracy by estimating the LoS component. The ranging error model in fading channel is derived. The third paper introduces the LoS-based positioning method with corresponding theoretical limits and error models. In the fourth and fifth paper, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) system and power contour circle fitting (PCCF) algorithm are proposed to address antenna directivity effect on power-based localization. Overall, a complete LoS signal power based positioning system has been developed that can be included in the fusion scheme --Abstract, page iv
ON THE ACCURACY OF MOBILITY MODELLING IN WIRELESS NETWORKS
In a wireless mobile network, two major
problems arise: poor performance of the wireless layer and effect of
user mobility. The problems related to limited available radio bandwidth and
radio channel errors seem to be solved by CDMA technique applied in 3G
mobile systems. The bandwidth that is provided by CDMA air interface is
enough for present mobile multimedia applications. However such applications
are sensitive to the degradation of QoS parameters. Graceful degradation
could happen when too many mobiles arrive to the same radio cell. To avoid
such situations the Call Admission Control (CAC) has to limit the number of
newly accepted connections. Terminal mobility causes problems in call
admission control because the number of active mobile terminals in a cell is
a random variable. In this paper we introduce a new method to predict the
number of terminals in each cell. Based on this information a more effective
CAC algorithm can be applied in order to ensure user´s satisfaction
ColDICE: a parallel Vlasov-Poisson solver using moving adaptive simplicial tessellation
Resolving numerically Vlasov-Poisson equations for initially cold systems can
be reduced to following the evolution of a three-dimensional sheet evolving in
six-dimensional phase-space. We describe a public parallel numerical algorithm
consisting in representing the phase-space sheet with a conforming,
self-adaptive simplicial tessellation of which the vertices follow the
Lagrangian equations of motion. The algorithm is implemented both in six- and
four-dimensional phase-space. Refinement of the tessellation mesh is performed
using the bisection method and a local representation of the phase-space sheet
at second order relying on additional tracers created when needed at runtime.
In order to preserve in the best way the Hamiltonian nature of the system,
refinement is anisotropic and constrained by measurements of local Poincar\'e
invariants. Resolution of Poisson equation is performed using the fast Fourier
method on a regular rectangular grid, similarly to particle in cells codes. To
compute the density projected onto this grid, the intersection of the
tessellation and the grid is calculated using the method of Franklin and
Kankanhalli (1993) generalised to linear order. As preliminary tests of the
code, we study in four dimensional phase-space the evolution of an initially
small patch in a chaotic potential and the cosmological collapse of a
fluctuation composed of two sinusoidal waves. We also perform a "warm" dark
matter simulation in six-dimensional phase-space that we use to check the
parallel scaling of the code.Comment: Code and illustration movies available at:
http://www.vlasix.org/index.php?n=Main.ColDICE - Article submitted to Journal
of Computational Physic
Object Tracking in Video with Part-Based Tracking by Feature Sampling
Visual tracking of arbitrary objects is an active research topic in computer vision, with applications across multiple disciplines including video surveillance, activity analysis, robot vision, and human computer interface. Despite great progress having been made in object tracking in recent years, it still remains a challenge to design trackers that can deal with difficult tracking scenarios, such as camera motion, object motion change, occlusion, illumination changes, and object deformation. A promising way of tackling these types of problems is to use a part-based method; one which models and tracks small regions of the object and estimates the location of the object based on the tracked part's positions. These approaches typically model parts of objects with histograms of various hand-crafted features extracted from the region in which the part is located. However, it is unclear how such relatively homogeneous regions should be represented to form an effective part-based tracker. In this thesis we present a part-based tracker that includes a model for object parts that is designed to empirically characterise the underlying colour distribution of an image region, representing it by pairs of randomly selected colour features and counts of how many pixels are similar to each feature. This novel feature representation is used to find probable locations for the part in future frames via a Bhattacharyya Distance-based metric, which is modified to prefer higher quality matches. Sets of candidate patch locations are generated by randomly generating non-shearing affine transformations of the part's previous locations and locally optimising the most likely sets of parts to allow for small intra-frame object deformations. We also present a study of model initialisation in online, model-free tracking and evaluate several techniques for selecting the regions of an image, given a target bounding box most likely to contain an object. The strengths and limitations of the combined tracker are evaluated on the VOT2016 and VOT2018 datasets using their evaluation protocol, which also allows an extensive evaluation of parameter robustness. The presented tracker is ranked first among part-based trackers on the VOT2018 dataset and is particularly robust to changes in object and camera motion, as well as object size changes
A consensus-based global optimization method for high dimensional machine learning problems
We improve recently introduced consensus-based optimization method, proposed
in [R. Pinnau, C. Totzeck, O. Tse and S. Martin, Math. Models Methods Appl.
Sci., 27(01):183--204, 2017], which is a gradient-free optimization method for
general non-convex functions. We first replace the isotropic geometric Brownian
motion by the component-wise one, thus removing the dimensionality dependence
of the drift rate, making the method more competitive for high dimensional
optimization problems. Secondly, we utilize the random mini-batch ideas to
reduce the computational cost of calculating the weighted average which the
individual particles tend to relax toward. For its mean-field limit--a
nonlinear Fokker-Planck equation--we prove, in both time continuous and
semi-discrete settings, that the convergence of the method, which is
exponential in time, is guaranteed with parameter constraints {\it independent}
of the dimensionality. We also conduct numerical tests to high dimensional
problems to check the success rate of the method
Magnetohydrodynamic-Particle-in-Cell Method for Coupling Cosmic Rays with a Thermal Plasma: Application to Non-relativistic Shocks
We formulate a magnetohydrodynamic-particle-in-cell (MHD-PIC) method for
describing the interaction between collisionless cosmic ray (CR) particles and
a thermal plasma. The thermal plasma is treated as a fluid, obeying equations
of ideal MHD, while CRs are treated as relativistic Lagrangian particles
subject to the Lorentz force. Backreaction from CRs to the gas is included in
the form of momentum and energy feedback. In addition, we include the
electromagnetic feedback due to CR-induced Hall effect that becomes important
when the electron-ion drift velocity of the background plasma induced by CRs
approaches the Alfv\'en velocity. Our method is applicable on scales much
larger than the ion inertial length, bypassing the microscopic scales that must
be resolved in conventional PIC methods, while retaining the full kinetic
nature of the CRs. We have implemented and tested this method in the Athena MHD
code, where the overall scheme is second-order accurate and fully conservative.
As a first application, we describe a numerical experiment to study particle
acceleration in non-relativistic shocks. Using a simplified prescription for
ion injection, we reproduce the shock structure and the CR energy spectra
obtained with more self-consistent hybrid-PIC simulations, but at substantially
reduced computational cost. We also show that the CR-induced Hall effect
reduces the growth rate of the Bell instability and affects the gas dynamics in
the vicinity of the shock front. As a step forward, we are able to capture the
transition of particle acceleration from non relativistic to relativistic
regimes, with momentum spectrum connecting smoothly through
the transition, as expected from the theory of Fermi acceleration.Comment: 24 pages, 15 figures, accepted for publication in Ap
- …