Marcello Tomasini

The availability of large localized datasets from popular social-networking sites such as Foursquare and Twitter has enabled the expansion of the field of human mobility analysis. Extracting location information from user trajectories helps to increase the understanding of human mobility and is important for improving public health, city planning, as well as crime prevention. In this work, we introduce a framework to automatically discover and classify locations based solely from geo-temporal data and without relying on contextual data, such as user explicit check-ins. We collected a dataset of 3 millions geo-tagged tweets of the New York City area to test our framework. By using a combination of clustering algorithms to accurately pinpoint location position (from a cloud of points representing tweets), and the popular time/days histograms as features to train the classifier, we were able to reach up to 53% average accuracy for the location type classification.

In recent years, we have witnessed many advances in communication technologies such as wireless devices. One of the main concerns when dealing with wireless technologies is the limitation in resources such as memory and battery due to the large amount of data exchanged nowadays. In today's societies, people and wireless devices (e.g., smartphones and tablets) are strongly coupled. Therefore, it is needed to reduce memory requirements by tracking only a finite number of sensors due to the large number of encounters that happen among people. Since these devices move with the human carrier (e.g., in their cars, bikes, or even when they walk), the encounters among these devices can be described by the social network of people. This paper has two main contributions: first, a novel metric for measuring a tie strength of a pair of nodes is proposed; second, we explore the proposed metric in estimating memory requirements in wireless sensors networks.

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Sensor Networks are becoming ubiquitous in our society due to their broad applicability to data intensive tasks such as keeping air population to safe levels, efficient communication in military applications, to mention but a few. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, phones, and even being attached to living beings such as dolphins, birds and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static and mobile nodes. When mobility is a variable one should define which mobility model is best for the infrastructure given their differences. This paper evaluates which mobility pattern is best suited to be used in a Social Network of Sensors (SNoS). We evaluate several mobility models and measure the efficiency of information flow in a SNoS if mobile sensors follow these mobility patterns. The paper provides us with a greater understanding of the benefits of mobility in realistic scenarios.

Sensor networks are gaining in importance in today's society, from air-pollution monitoring to forest-fire detection, from agriculture to battlefield communication, sensor networks are ubiquitous in many parts of the world. Furthermore, this is a world where sensors are increasingly integrated in smart phones and tablets. The use of this collection sensors as sensor-network infrastructures (towards what we call Social Networks of Sensors'', or SNoS) may be very fruitful and economically advantageous. However, it also introduces some new research challenges, one of which relates to understanding the potential impact of social mobility patterns to the performances of sensor infrastructures. This paper delves into such issue and analyses how the presence of mobile sensors moving according to social mobility patterns can impact the performances of fixed sensor network infrastructures and their design choices. Simulation results show that such integration can lead to increased efficiency of the integrated SNoS infrastructure for both sensing coverage and data delivery.