An Analysis of a Real Mobility Trace Based on Standard Mobility Metrics

Better understanding mobility, being it from pedestrians or any other moving object, is practical and insightful. Practical due to its applications to the fundamentals of communication, with special attention to wireless communication. Insightful because it might pinpoint the pros and cons of how we are moving, or being moved, around. There are plenty of studies focused on mobility in mobile wireless networks, including the proposals of several synthetic mobility models. Getting real mobility traces is not an easy task, but there has been some efforts to provide traces to the public through repositories. Synthetic mobility models are usually analyzed through mobility metrics, which are designed to capture mobility subtleties. This work research on the applicability of some representative mobility metrics for real traces analysis. To achieve that goal, a case study is accomplished with a dataset of mobility traces of taxi cabs in the city of Rome/Italy. The results suggest that the mobility metrics under consideration are capable of capturing mobility properties which would otherwise require more sophisticated analytical approaches

An?lise de mobilidade em redes ad hoc m?veis baseada em tra?os reais e m?tricas de mobilidade

Synthetic mobility traces generated by Mobility Models were extensively used for metrics and protocols evaluation within MANTEs field over the years. The present work aims to analyze a real mobility traces data set through mobility metrics whose are divided in five perspectives: time, space, graph, distance and velocity. The analyzis assembles an attempt of gathering information from the data set in the MANTEs panorama, and also, identify wich mobility models have a similar behavior in comparison of those who were studied. At the end, some questions are raised up about working on this field with real data.Tra?os sint?ticos gerados por Modelos de Mobilidade foram extensivamente utilizados na avalia??o de m?tricas e protocolos dentro do contexto de redes ad hoc m?veis ao longo do tempo. O presente trabalho se prop?s analisar um conjunto tra?os reais de mobilidade por meio de m?tricas que se dividem em cinco aspectos: tempo, espa?o, grafo, dist?ncia e velocidade. A an?lise compreende uma tentativa de extrair informa??es destes dados sob uma perspectiva de rede ad hoc m?vel, e tamb?m identificar os modelos de mobilidade cujos tra?os apresentam comportamentos semelhantes aos tra?os reais estudados. Por fim s?o levantadas algumas quest?es acerca de se trabalhar com tra?os reais neste escopo

Differentiating population spatial behaviour using a standard feature set

Moving through space, consuming services at locations, transitioning and dwelling are all aspects of spatial behavior that can be recorded with unprecedented ease and accuracy using the GPS and other sensor systems on commodity smartphones. Collection of GPS data is becoming a standard experimental method for studies ranging from public health interventions to studying the browsing behavior of large non-human mammals. However, the millions of records collected in these studies do not lend themselves to traditional geographic analysis. GPS records need to be reduced to a single feature or combination of features, which express the characteristic of interest. While features for spatial behavior characterization have been proposed in different disciplines, it is not always clear which feature should be appropriate for a specific dataset. The substantial effort on subjective selection or design of feature may or may not lead to an insight into GPS datasets. In this thesis we describe a feature set drawn from three different mathematical heritages: buffer area, convex hull and its variations from activity space, fractal dimension of the recorded GPS traces, and entropy rate of individual paths. We analyze these features against six human mobility datasets. We show that the standard feature set could be used to distinguish disparate human mobility patterns while single feature could not distinguish them alone. The feature set can be efficiently applied to most datasets, subject to the assumptions about data quality inherent in the features

Shortest paths and centrality in uncertain networks

Computing the shortest path between a pair of nodes is a fundamental graph primitive, which has critical applications in vehicle routing, finding functional pathways in biological networks, survivable network design, among many others. In this work, we study shortest-path queries over uncertain networks, i.e., graphs where every edge is associated with a probability of existence. We show that, for a given path, it is #P-hard to compute the probability of it being the shortest path, and we also derive other interesting properties highlighting the complexity of computing the Most Probable Shortest Paths (MPSPs). We thus devise sampling-based efficient algorithms, with end-to-end accuracy guarantees, to compute the MPSP. As a concrete application, we show how to compute a novel concept of betweenness centrality in an uncertain graph using MPSPs. Our thorough experimental results and rich real-world case studies on sensor networks and brain networks validate the effectiveness, efficiency, scalability, and usefulness of our solution

Mechanisms for improving information quality in smartphone crowdsensing systems

Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs. Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii

Previsão eficiente do posicionamento futuro de nós em redes móveis

The ability to predict where nodes might be in the near future may enable several new applications in a mobile ad hoc network (MANET). For example, content may be generated for an approaching potential consumer in pervasive computing scenarios or traffic jams may be predicted and prevented. We introduce PheroCast, a lightweight algorithm to do online predictions of a node’s future position based on its previous movement history. PheroCast, however, does not take into account the variations in the movement pattern along the day, using any previous history in the same way. For example, in a scenario where the same node travels every morning, but seldom in the evening, PheroCast would give the same or more weight to the data from the morning when predicting an evening trip, which would likely lead to a wrong prediction. Due to this limitation, we developed the Time of Day PheroCast, or ToD-PheroCast, an extended version of the original algorithm which takes the time of the day into account while making predictions, giving more emphasis to the history of movement within similar time windows. Finally, we evaluate the performance in three scenarios: (i) prediction of the position of buses in a metropolis, which are expected to have very regular mobility pattern; (ii) Taxis in a metropolis, which should lead to low accuracy predictions; and (iii) mobility of people interacting with wireless networks, that used traces collected by the author’s research group. Our evaluations show that ToD-PheroCast is up to 4.41% better than PheroCast in the bus scenario, in which it achieved over 85% accuracy in its predictions, and 0.72% better in the taxi scenario, in which the algorithm achieved up to 89.17% accuracy. Finally, in the wireless scenario, ToD-PheroCast achieved 81.02% accuracy. These results show that not only forecasting is possible in such scenarios, but that it may be done with high accuracy, online, and in a lightweight manner.Dissertação (Mestrado)A habilidade de prever onde nós podem estar em um futuro próximo, pode possibilitar novas aplicações em Redes Ad-Hoc Móveis (MANET). Por exemplo, o conteúdo pode ser gerado para um consumidor em potencial em cenários de computação pervasiva ou congestionamentos de tráfego podem ser previstos e prevenidos. Neste trabalho introduzimos dois algoritmos para previsão de posição futura de nós em uma rede móvel, PheroCast e ToD-Pherocast. O algoritmo de Previsão Baseada em Feromônios (PheroCast) é um algoritmo leve para realizar predições online da posição futura de um nó baseado em seu histórico de movimentação. PheroCast, no entanto, não leva em consideração variações no padrão de movimentação ao longo do dia, usando o histórico do passado da mesma forma. Por exemplo, em um cenário em que o mesmo nó viaja todos os dias de manhã, mas raramente à tarde, PheroCast dará o mesmo ou mais peso para o dado da manhã enquanto estiver prevendo a viagem à tarde, o que provavelmente levaria a uma previsão errada. Devido a tal limitação, desenvolvemos o Time of Day Pherocast, ou ToD-Pherocast, uma versão estendida do algoritmo original que leva em consideração o horário da viagem para gerar as predições, dando mais ênfase à história do movimento em horários similares. Finalmente, apresentamos uma avaliação de desempenho considerando três cenários: (i) previsão da posição de ônibus, cujo comportamento esperado é regular; (ii) previsão de posição de táxis, que aparentemente levaria a baixa taxa de acertos; e (iii) mobilidade de pessoas em relação redes sem fio, que usou rastros coletados pelo grupo de pesquisa do autor. Nossas avaliações mostram que o ToD-PheroCast é até 4.41% melhor que o PheroCast no cenário dos ônibus, em que alcançou acurácia de mais de 85%, e 0.72% melhor no cenário dos táxis, alcançando uma acurácia de até 89.17%. Finalmente, no cenários de previsão de redes sem fio, ToD-PheroCast atingiu 81.02% de acurácia. Esses resultados mostram não só que a previsão da posição é possível nos cenários, mas que pode ser realizada rápida e acuradamente

Performance assessment of an epidemic protocol in VANET using real traces

Many vehicular ad-hoc network protocols have been validated using complex urban mobility simulators or by means of the few publicly available real mobility traces. This work presents an extensive measurement campaign of the positions of a fleet of 370 taxi cabs moving around the city of Rome, Italy. Due to its street network and its traffic conditions, Rome presents a characteristic mobility pattern representative of an ancient city with heavy road congestion, and therefor provides a valuable test case to experiment VANET protocols. We exploit these traces to run a set of experiments to assess the performance of a simple epidemic protocol that we compare with the basic random waypoint model in order to quantify how far the performance metrics are from this baseline. The results show the possible outcomes of implementing data dissemination through an opportunistic network that uses taxi cabs as an information vector