Wireless ad hoc networks connectivity assessment and relay node deployment

Cheng, Min‐Yuan; Ling, Yi; Sadler, Brian M.

doi:10.1109/glocom.2014.7036841

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Given a network graph, consider the problem of identifying bottlenecks where traffic may funnel through a small subset of nodes and create excess delays in the overall network performance. We build on prior work for bottleneck identification based solely on the network topology [21], [22], briefly reviewed next. We use a running example throughout this section, illustrated in Figures 1, 2, and 3.…”

Section: A Networking Bottlenecks and The Z-laplacianmentioning

confidence: 99%

“…To find and quantify the primary network bottleneck under different MAC protocols, we use the more flexible transformed graph W for definitions (see Table I). For the base-case Laplacian in (21), we simply set W = A.…”

Section: A Networking Bottlenecks and The Z-laplacianmentioning

confidence: 99%

“…In the simplest setting [21], [22], we assume that all communications are orthogonal, with no multi-user interference or additional delay, and that each vertex always has a packet to transmit (a fully saturated network traffic model). Recalling our notation from Section II, the network graph is G = {V, E, A}, and elements of A are denoted a ij .…”

Section: A Networking Bottlenecks and The Z-laplacianmentioning

confidence: 99%

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Graph Filters and the <italic>Z</italic>-Laplacian

Yan

Sadler

Drost

et al. 2017

IEEE J. Sel. Top. Signal Process.

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In network science, the interplay between dynamical processes and the underlying topologies of complex systems has led to a diverse family of models with different interpretations. In graph signal processing, this is manifested in the form of different graph shifts and their induced algebraic systems. In this paper, we propose the unifying Z-Laplacian framework, whose instances can act as graph shift operators. As a generalization of the traditional graph Laplacian, the Z-Laplacian spans the space of all possible Z-matrices, i.e., real square matrices with nonpositive off-diagonal entries. We show that the Z-Laplacian can model general continuous-time dynamical processes, including information flows and epidemic spreading on a given graph. It is also closely related to general nonnegative graph filters in the discrete time domain. We showcase its flexibility by considering two applications. First, we consider a wireless communications networking problem modeled with a graph, where the framework can be applied to model the effects of the underlying communications protocol and traffic. Second, we examine a structural brain network from the perspective of low-to high-frequency connectivity.

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Section: A Networking Bottlenecks and The Z-laplacianmentioning

confidence: 99%

Section: A Networking Bottlenecks and The Z-laplacianmentioning

confidence: 99%

Section: A Networking Bottlenecks and The Z-laplacianmentioning

confidence: 99%

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Graph Filters and the <italic>Z</italic>-Laplacian

Yan

Sadler

Drost

et al. 2017

IEEE J. Sel. Top. Signal Process.

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“…Cheng et al [13] proposes a method to improve wireless connectivity in ad hoc networks using a partitioning technique based on the conductance of a network. They use convex optimization to maximize the number of connections between the communicating nodes on different sides of the partition.…”

Section: Previous Workmentioning

confidence: 99%

Adaptive Region Construction for Efficient Use of Radio Propagation Maps

Ramakrishnaiah¹,

Muknahallipatna²,

Kubichek³

2017

JCC

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The efficient construction of contours of a radio propagation map is crucial in using radio propagation maps in a number of real-time communication and network applications. In this research work, we first propose an adaptive region construction (ARC) technique capable of constructing contours of different resolutions of a radio propagation map. Next, the process of implementing the ARC technique for real-time execution on a GPU is presented. The drawbacks of the first implementation using only the global memory are discussed, and optimization techniques to improve the performance are discussed and implemented. Simulations are performed with varying sizes of radio propagation maps, and the suitability of the ARC technique for real-time operation is presented. A speedup of 25× is achieved with the shared version of the GPU compared to the sequential CPU implementation. Also, the contour constructed using the ARC technique is compared to that constructed using the convex hull approach demonstrating the higher accuracy of the contour from the ARC technique.

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