Plausible Mobility: Inferring Movement from Contacts

We address the difficult question of inferring plausible node mobility based only on information from wireless contact traces. Working with mobility information allows richer protocol simulations, particularly in dense networks, but requires complex set-ups to measure, whereas contact information is easier to measure but only allows for simplistic simulation models. In a contact trace a lot of node movement information is irretrievably lost so the original positions and velocities are in general out of reach. We propose a fast heuristic algorithm, inspired by dynamic force-based graph drawing, capable of inferring a plausible movement from any contact trace, and evaluate it on both synthetic and real-life contact traces. Our results reveal that (i) the quality of the inferred mobility is directly linked to the precision of the measured contact trace, and (ii) the simple addition of appropriate anticipation forces between nodes leads to an accurate inferred mobility.Comment: 8 pages, 8 figures, 1 tabl

On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks

We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data diffusion in ad-hoc networks, Ad Hoc Netw. (2011

Development of a trace generation and analysis software for random mobility models.

No abstract available.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b130305

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

Assessing drone trajectory error and improving flightpath predictability

The rapid growth of the drone industry aims to develop applications and implement them in a wide range of areas. This includes busy urban areas for services such as surveillance, deliveries and monitoring. In this context, it is essential to have an excellent design of the airspace. This thesis focuses on the analysis of a dataset from the Very Large Demonstration project of CORUSXUAM, which contributes to the U-Space mission of developing a safe, sustainable, efficient and fully digitalized airspace for integrated Urban Air Mobility which does not interfere with current ATM operations. The dataset includes flight plans, telemetry, and U-space predictions for 72 drone flights. The analysis involves comparing intended trajectories with actual flight paths to identify factors contributing to deviations from the flight plan and computing relevant performance parameters to assess the adherence of the drones to the flight plan. This is done with the use of dynamic time warping algorithms in order to establish a link between the telemetry points and the flight plan, which sets the basis for the next section of the project. Having processed the data, during this project we develop machine learning models to predict telemetry parameters based on the input flight plan. Several models are tested and evaluated to find the most suitable one for our objective. The project also involves visualizing and interpreting the data to gain insights of the drone performance and adherence to the flight plan. Position prediction opens up a new area of research and in this project the approach is to use an alternative method to define the spacing and size of the airways that compose the flight plans so as to dictate safety areas to prevent any possible conflict that could appear in future flights in a busy area if the spacing were to be below the thresholds. The results of this study demonstrate a successful progression from raw data to a comprehensive analysis, offering valuable insights for evaluating drone performance and predicting flight times. The development of various data visualization functions enabled efficient and effective interpretation of the data. While the obtained results with the available dataset are remarkable, the potential for further improvement lies mainly in acquiring a larger dataset with more features and samples, which would enhance the performance of the machine learning models and yield even more accurate predictions.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Analysis of multi-resolution data aggregation using push-assisted random walks in mobile ad-hoc network (MANET)

ABSTRACT Analysis of Multi-Resolution Data Aggregation using Push-assisted Random Walks in Mobile Ad-hoc Networks (MANETs) Sowmya Srinivasapura Devaraja Data Aggregation in Mobile Ad-hoc Network (MANET) has proven challenging because of changing topology. Structure-based models like tree-based, cluster-based and chain-based have high maintenance cost. In earlier works, different forms of biased random walks have been verified to be effective without need for structure maintenance. The key idea in the protocol was to use one or more tokens that are circulated using biased random walks to effectively compute the data aggregation. One such protocol is EZ-AG that uses Push-assisted Self-Repelling Random Walks . A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often. While self-repelling random walks visit all nodes in the network much faster than plain random walks, they tend to slow down when most of the nodes are already visited. It\u27s verified that a single step push phase at each node can significantly speed up the aggregation and eliminate the slow down. Results have been verified that EZ-AG achieves aggregation in only O (N) time and messages. When the network is quite large, obtaining only one aggregate may not be sufficient. It will be more useful to provide distance-sensitive multi-resolution aggregates of data. The contribution in this project is, we have analyzed the Hierarchical EZ-AG proposed to provide multi-resolution results. We show that aggregates for nearby regions are obtained at faster rate in comparison to the farther region. The idea is to introduce the tokens in the network at distinct levels, execute EZ-AG protocol and obtain localized data aggregation output at distinct levels. Existing techniques for hierarchical aggregations require O (N log5.4 (N)) messages. Hierarchical EZ-AG outperforms these techniques by aggregating with only O (N log (N)) messages. We evaluate the performance of hierarchical EZ-AG considering message overhead, token messages, number of aggregations at distinct levels, node speed and mobility. Our results are validated using simulations in network simulator, ns-3 for network ranging from 100 to 4000 nodes under different node speeds and mobility models