A Self-Organization Framework for Wireless Ad Hoc Networks as Small Worlds

Motivated by the benefits of small world networks, we propose a self-organization framework for wireless ad hoc networks. We investigate the use of directional beamforming for creating long-range short cuts between nodes. Using simulation results for randomized beamforming as a guideline, we identify crucial design issues for algorithm design. Our results show that, while significant path length reduction is achievable, this is accompanied by the problem of asymmetric paths between nodes. Subsequently, we propose a distributed algorithm for small world creation that achieves path length reduction while maintaining connectivity. We define a new centrality measure that estimates the structural importance of nodes based on traffic flow in the network, which is used to identify the optimum nodes for beamforming. We show, using simulations, that this leads to significant reduction in path length while maintaining connectivity.Comment: Submitted to IEEE Transactions on Vehicular Technolog

A model of assessment of collateral damage on power grids based on complex network theory

As power grids are gradually adjusted to fit into a smart grid paradigm, a common problem is to identify locations where it is most beneficial to introduce distributed generation. In order to assist in such a decision, we work on a graph model of a regional power grid, and propose a method to assess collateral damage to the network resulting from a localized failure. We perform complex network analysis on multiple instances of the network, looking for correlations between estimated damages and betweenness centrality indices, attempting to determine which model is best suited to predict features of our network

Street centrality and land use intensity in Baton Rouge, Louisiana

This paper examines the relationship between street centrality and land use intensity in Baton Rouge, Louisiana. Street centrality is calibrated in terms of a node's closeness, betweenness and straightness on the road network. Land use intensity is measured by population (residential) and employment (business) densities in census tracts, respectively and combined. Two CIS-based methods are used to transform data sets of centrality (at network nodes) and densities (in census tracts) to one unit for correlation analysis. The kernel density estimation (KDE) converts both measures to raster pixels, and the floating catchment area (FCA) method computes average centrality values around census tracts. Results indicate that population and employment densities are highly correlated with street centrality values. Among the three centrality indices, closeness exhibits the highest correlation with land use densities, straightness the next and betweenness the last. This confirms that street centrality captures location advantage in a city and plays a crucial role in shaping the intraurban variation of land use intensity. (C) 2010 Elsevier Ltd. All rights reserved