Airborne Directional Networking: Topology Control Protocol Design

This research identifies and evaluates the impact of several architectural design choices in relation to airborne networking in contested environments related to autonomous topology control. Using simulation, we evaluate topology reconfiguration effectiveness using classical performance metrics for different point-to-point communication architectures. Our attention is focused on the design choices which have the greatest impact on reliability, scalability, and performance. In this work, we discuss the impact of several practical considerations of airborne networking in contested environments related to autonomous topology control modeling. Using simulation, we derive multiple classical performance metrics to evaluate topology reconfiguration effectiveness for different point-to-point communication architecture attributes for the purpose of qualifying protocol design elements

Data driven wireless network design : a multi-level modeling approach

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Distance Distributions and Proximity Estimation Given Knowledge of the Heterogeneous Network Layout

Today's heterogeneous wireless network (HWN) is a collection of ubiquitous wireless networking elements (WNEs) that support diverse functional capabilities and networking purposes. In such a heterogeneous networking environment, proximity estimation will play a key role for the seamless support of emerging applications that span from the direct exchange of localized traffic between homogeneous WNEs (peer-to-peer communications) to positioning for autonomous systems using location information from the ubiquitous HWN infrastructure. Since most of the existing wireless networking technologies enable the direct (or indirect) estimation of the distances and angles between their WNEs, the integration of such spatial information is a natural solution for robustly handling the unprecedented demand for proximity estimation between the myriads of WNEs. In this paper, we develop an analytical framework that integrates existing knowledge of the HWN layout to enable proximity estimation between WNE supporting different radio access technologies (RATs). In this direction, we derive closed-form expressions for the distance distribution between two tagged WNEs given partial (or full) knowledge of the HWN topology. The derived expressions enable us to analyze how different levels of location-awareness affect the performance of proximity estimation between WNEs that are not necessarily capable of communicating directly. Optimal strategies for the deployment of WNEs, as means of maximizing the probability of successful proximity estimation between two WNEs of interest, are presented, and useful guidelines for the design of location-aware proximity estimation in the nowadays HWN are drawn

Transportation planning via location-based social networking data : exploring many-to-many connections

textToday’s metropolitan areas see changes in populations and land development occurring at faster rates than transportation planning can be updated. This dissertation explores the use of a new dataset from the location-based social networking spectrum to analyze origin-destination travel demand within Austin, TX. A detailed exploration of the proposed data source is conducted to determine its overall capabilities with respect to the Austin area demographics. A new methodology is proposed for the creation of origin-destination matrices using a peer-to-peer modeling structure. This methodology is compared against a previously examined and more traditional approach, the doubly-constrained gravity model, to understand the capabilities of both models with various friction functions. Each method is examined within the constructs of the study area’s existing origin-destination matrix by examining the coincidence ratios, mean errors, mean absolute errors, frequency ratios, swap ratios, trip length distributions, zonal trip generation and attraction heat maps, and zonal origin-destination flow patterns. Through multiple measures, this dissertation provides initial interpretations of the robust Foursquare data collected for the Austin area. Based upon the data analytics performed, the Foursquare data source is shown to be capable of providing immensely detailed spatial-temporal data that can be utilized as a supplementary data source to traditional transportation planning data collection methods or in conjunction with other data sources, such as social networking platforms. The examination of the proposed peer-to-peer methodology presented within this dissertation provides a first look at the potential of many-to-many modeling for transportation planning. The peer-to-peer model was found to be superior to the doubly-constrained gravity model with respect to intrazonal trips. Furthermore, the peer-to-peer model was found to better estimate productions, attractions, and zone to zone movements when a linear function was used for long trips, and was computationally more proficient for all models examined.Civil, Architectural, and Environmental Engineerin