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Citizen in Sensor Networks

Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers

herausgegeben von: Jordi Nin, Daniel Villatoro

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book constitutes the proceedings of the Second International Conference on Citizen Sensor Networks, CitiSens 2013, held in Barcelona, Spain, in September 2013. The 8 papers presented in this volume were carefully reviewed and selected from 16 submissions. The topics covered are: trajectory mining, smart cities, multi-agents systems, networks simulation, smart sensors and clustering or data anonymization.

Inhaltsverzeichnis

Frontmatter

Trajectory Mining

Frontmatter

Actions in Context: System for People with Dementia

Abstract

In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities - ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios.

Àlex Pardo, Albert Clapés, Sergio Escalera, Oriol Pujol

Transportation Planning Based on GSM Traces: A Case Study on Ivory Coast

Abstract

In this work we present an analysis process that exploits mobile phone transaction (trajectory) data to infer a transport demand model for the territory under monitoring. In particular, long-term analysis of individual call traces are performed to reconstruct systematic movements, and to infer an origin-destination matrix. We will show a case study on Ivory Coast, with emphasis on its major urbanization Abidjan. The case study includes the exploitation of the inferred mobility demand model in the construction of a transport model that projects the demand onto the transportation network (obtained from open data), and thus allows an understanding of current and future infrastructure requirements of the country.

Mirco Nanni, Roberto Trasarti, Barbara Furletti, Lorenzo Gabrielli, Peter Van Der Mede, Joost De Bruijn, Erik De Romph, Gerard Bruil

From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns

Abstract

This paper proposes and experiments new techniques to detect urban mobility patterns and anomalies by analyzing trajectories mined from publicly available geo-positioned social media traces left by the citizens (namely Twitter). By collecting a large set of geo-located tweets characterizing a specific urban area over time, we semantically enrich the available tweets with information about its author – i.e. a resident or a tourist – and the purpose of the movement – i.e. the activity performed in each place.

We exploit mobility data mining techniques together with social network analysis methods to aggregate similar trajectories thus pointing out hot spots of activities and flows of people together with their variations over time. We apply and validate the proposed trajectory mining approaches to a large set of trajectories built from the geo-positioned tweets gathered in Barcelona during the Mobile World Congress 2012 (MWC2012), one of the greatest events that affected the city in 2012.

Lorenzo Gabrielli, Salvatore Rinzivillo, Francesco Ronzano, Daniel Villatoro

Incentivising Crowdsourced Parking Solutions

Abstract

The problem of finding parking slots imposes both societal and infrastructural issues in modern cities. It is a daily hurdle that affects millions of people, but existing approaches fail to solve this conundrum. Thus, there is an urgent demand for reputable, motivated, and replicable solutions that can be used by cities of any size. We are proposing an experiment to analyse the interplay between incentive mechanisms, user participation, and the truthfulness of reports. For that, we are developing the “wePark application” based on concepts of crowd sourcing and social regulation. As a differential, we are examining alternative methods to motivate adoption, such as reciprocity, reputation, altruism, and money. In this paper, we analyse the requirements of the solution, propose a development test bed, and an experimental environment for this study.

Andrew Koster, Fernando Koch, Ana L. C. Bazzan

Transportation Networks

Frontmatter

Crowdsensing Simulation Using ns-3

Abstract

A crowdsensing network is a sensor network in which sensors are users that sense the environment and send the obtained data using, for instance, their smartphones. The performance of such sensor networks depends heavily on the mobility of the users and their willingness to collaborate. It is hard to obtain a stable set of users to evaluate such kinds of sensor networks and, for that reason, studies of crowdsensing networks are scarce. In this paper, we describe how the ns-3 network simulator can be used to simulate some crowdsensing networks with specific characteristics by using the mobility properties of network nodes together with the wireless interface in ad hoc network mode. We model the identification of network nodes with users of the crowdsensing network and we define how to simulate user sensing capabilities. Finally, we present a simulation example for a specific crowdsensing network where users report incidents in the public rail transport.

Cristian Tanas, Jordi Herrera-Joancomartí

On the Use of Social Trajectory-Based Clustering Methods for Public Transport Optimization

Abstract

Public transport optimisation is becoming everyday a more difficult and challenging task, because of the increasing number of transportation options as well as the increase of users. Many research contributions about this issue have been recently published under the umbrella of the smart cities research. In this work, we sketch a possible framework to optimize the tourist bus in the city of Barcelona. Our framework will extract information from Twitter and other web services, such as Foursquare to infer not only the most visited places in Barcelona, but also the trajectories and routes that tourist follow. After that, instead of using complex geospatial or trajectory clustering methods, we propose to use simpler clustering techniques as \(k\)-means or DBScan but using a real sequence of symbols as a distance measure to incorporate in theclustering process the trajectory information.

Jordi Nin, David Carrera, Daniel Villatoro

Migration Movements

Frontmatter

Tracking Human Migration from Online Attention

Abstract

The dynamics behind human migrations are very complex. Economists have intensely studied them because of their importance for the global economy. However, tracking migration is costly, and available data tends to be outdated. Online data can be used to extract proxies for migration flows, and these proxies would not be meant to replicate traditional measurements but are meant to complement them. We analyze a random sample of a microblogging service popular in Brazil (more than 13M posts and 22M reposts) and accurately predict the total number of migrants in 35 Brazilian cities. These results are so accurate that they have promising implications in monitoring emerging economies.

Carmen Vaca-Ruiz, Daniele Quercia, Luca Maria Aiello, Piero Fraternali

Data Anonymization

Frontmatter

Beyond Multivariate Microaggregation for Large Record Anonymization

Abstract

Microaggregation is one of the most commonly employed microdata protection methods. The basic idea of microaggregation is to anonymize data by aggregating original records into small groups of at least \(k\) elements and, therefore, preserving \(k\)-anonymity. Usually, in order to avoid information loss, when records are large, i.e., the number of attributes of the data set is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. This is called multivariate microaggregation. By using this technique, the information loss after collapsing several values to the centroid of their group is reduced. Unfortunately, with multivariate microaggregation, the \(k\)-anonymity property is lost when at least two attributes of different blocks are known by the intruder, which might be the usual case.

In this work, we present a new microaggregation method called one dimension microaggregation (\(Mic1D-k\)). With \(Mic1D-k\), the problem of \(k\)-anonymity loss is mitigated by mixing all the values in the original microdata file into a single non-attributed data set using a set of simple pre-processing steps and then, microaggregating all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.

Jordi Nin

Backmatter

Titel: Citizen in Sensor Networks
herausgegeben von: Jordi Nin
Daniel Villatoro
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-04178-0
Print ISBN: 978-3-319-04177-3
DOI: https://doi.org/10.1007/978-3-319-04178-0