19,278 research outputs found
Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks
Designing high performance channel assignment schemes to harness the
potential of multi-radio multi-channel deployments in wireless mesh networks
(WMNs) is an active research domain. A pragmatic channel assignment approach
strives to maximize network capacity by restraining the endemic interference
and mitigating its adverse impact on network performance. Interference
prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being
a crucial aspect that is seldom addressed in research endeavors. In this
effort, we propose a set of intelligent channel assignment algorithms, which
focus primarily on alleviating the RCI. These graph theoretic schemes are
structurally inspired by the spatio-statistical characteristics of
interference. We present the theoretical design foundations for each of the
proposed algorithms, and demonstrate their potential to significantly enhance
network capacity in comparison to some well-known existing schemes. We also
demonstrate the adverse impact of radio co- location interference on the
network, and the efficacy of the proposed schemes in successfully mitigating
it. The experimental results to validate the proposed theoretical notions were
obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n
environments.Comment: Accepted @ ICACCI-201
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Optimal Approximation Algorithms for Multi-agent Combinatorial Problems with Discounted Price Functions
Submodular functions are an important class of functions in combinatorial
optimization which satisfy the natural properties of decreasing marginal costs.
The study of these functions has led to strong structural properties with
applications in many areas. Recently, there has been significant interest in
extending the theory of algorithms for optimizing combinatorial problems (such
as network design problem of spanning tree) over submodular functions.
Unfortunately, the lower bounds under the general class of submodular functions
are known to be very high for many of the classical problems.
In this paper, we introduce and study an important subclass of submodular
functions, which we call discounted price functions. These functions are
succinctly representable and generalize linear cost functions. In this paper we
study the following fundamental combinatorial optimization problems: Edge
Cover, Spanning Tree, Perfect Matching and Shortest Path, and obtain tight
upper and lower bounds for these problems.
The main technical contribution of this paper is designing novel adaptive
greedy algorithms for the above problems. These algorithms greedily build the
solution whist rectifying mistakes made in the previous steps
An exact general remeshing scheme applied to physically conservative voxelization
We present an exact general remeshing scheme to compute analytic integrals of
polynomial functions over the intersections between convex polyhedral cells of
old and new meshes. In physics applications this allows one to ensure global
mass, momentum, and energy conservation while applying higher-order polynomial
interpolation. We elaborate on applications of our algorithm arising in the
analysis of cosmological N-body data, computer graphics, and continuum
mechanics problems.
We focus on the particular case of remeshing tetrahedral cells onto a
Cartesian grid such that the volume integral of the polynomial density function
given on the input mesh is guaranteed to equal the corresponding integral over
the output mesh. We refer to this as "physically conservative voxelization".
At the core of our method is an algorithm for intersecting two convex
polyhedra by successively clipping one against the faces of the other. This
algorithm is an implementation of the ideas presented abstractly by Sugihara
(1994), who suggests using the planar graph representations of convex polyhedra
to ensure topological consistency of the output. This makes our implementation
robust to geometric degeneracy in the input. We employ a simplicial
decomposition to calculate moment integrals up to quadratic order over the
resulting intersection domain.
We also address practical issues arising in a software implementation,
including numerical stability in geometric calculations, management of
cancellation errors, and extension to two dimensions. In a comparison to recent
work, we show substantial performance gains. We provide a C implementation
intended to be a fast, accurate, and robust tool for geometric calculations on
polyhedral mesh elements.Comment: Code implementation available at https://github.com/devonmpowell/r3
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