Antonio F. Skarmeta-Gomez

A complex event processing approach to perceive the vehicular context

Nowadays, most people are used to driving their own vehicles to accomplish certain routines like commuting, go shopping, and the like. Taking into account the increasing number of sensors vehicles are provided with, the present work states that it is possible to perceive the context of a vehicle by processing and fusioning the data of some of them. As a result, an on-board context-aware application that processes the usual itineraries of the Ego Vehicle as part of the vehicular context has been implemented. Particularly, the system follows a Complex Event Processing (CEP) approach, and it is able to detect the vehicular occupancy along with the meaningful points of the frequent itineraries whereby a density-based-cluster algorithm. Test results from simulations and real environments show the accuracy of the system when it comes to detect different types of itineraries.

Teaching Advanced Concepts in Computer Networks: VNUML-UM Virtualization Tool

In the teaching of computer networks the main problem that arises is the high price and limited number of network devices the students can work with in the laboratories. Nowadays, with virtualization we can overcome this limitation. In this paper, we present a methodology that allows students to learn advanced computer network concepts through hands-on experience with the VNUML-UM virtualization tool, which is offered freely as a resource for the practical teaching of mobility, load balancing, and high availability. To verify the utility of using the VNUML-UM virtualization tool in the teaching of advanced computer network concepts, we have performed some opinion polls to the students during the last three academic years. The obtained results confirm that our students agree that the VNUML-UM enables an enhanced learning process of the different concepts and their practical skills. This perception is also confirmed by the final marks obtained by the students, which have considerably improved along the years. To the best our knowledge, this paper presents the first experience that provides results on the use of virtualization to teach advanced concepts in the field of computer networks.

A complex event processing approach to detect abnormal behaviours in the marine environment

Over the last years, many data-sources have become available to monitor the marine traffic. This has motivated the development of support systems to automatically detect vessels’ behaviours of interest. The present work states a novel approach in this domain following the Complex Event Processing (CEP) paradigm. As a proof of concept, a CEP-based system has been developed to timely detect a set of vessel’s abnormal behaviours by performing an event-based processing of Automatic Identification System data. Experiments based on real-world and synthetic data proved the suitability and feasibility of the proposal.

KAMU: providing advanced user privacy in Kerberos multi-domain scenarios

In Next Generation Networks, Kerberos is becoming a key component to support authentication and key distribution for Internet application services. However, for this purpose, Kerberos needs to rectify certain deficiencies, especially in the area of privacy, which allow an eavesdropper to obtain information of the services users are accessing. This paper presents a comprehensive privacy framework that guarantees user anonymity, service access unlinkability and message exchange unlinkability in Kerberos both in single-domain and multi-domain scenarios. This proposal is based on different extensibility mechanisms already defined for Kerberos, which facilitate its adoption in already deployed systems. Apart from evaluating our proposal in terms of performance to prove its lightweight nature, we demonstrate its capability to work in perfect harmony with a widely used anonymous communication system like Tor.

Big data for IoT services in smart cities

This paper analyzes the benefits of big data for smart cities and the potential of the knowledge discovery from sensed data. Big data enables real-time systems monitoring, management, optimization and anticipation. In this work we present some examples of applications of big data analysis in two scenarios of smart cities. One of them describes the services provided in the SmartCampus of the University of Murcia. The second example is focused on a tram service scenario where thousands of transit-card transactions should be processed. The results obtained after applying the most appropriate big data techniques in both scenarios show how it is possible to provide efficiently services like the management of the energy consumption and comfort in buildings, and the transport congestion in the context of smart cities.

Towards human mobility extraction based on social media with Complex Event Processing

Social media has enabled a new breed of soft sensors that enriches the IoT paradigm with new forms of data. The present work introduces a novel approach for personal mobility mining that combines these new data-sources with built-in sensors of a smart-phone in order to timely extract personal mobility pattens by means of the Complex Event Processing (CEP) approach. Unlike previous solutions, the present work profits from both the textual and location data of social-network sites by also dealing with the actual scarcity of geo-tagged documents in those sites. Finally, a preliminary study of the feasibility of our proposal is stated.

Online Urban Mobility Detection Based on Velocity Features

The study of the mobility models that arise from the city dynamics has become instrumental to provide new urban services. In this context, many proposals applied an off-line learning on historical data. However, at the dawn of the Big Data era, there is an increasing need for systems and architectures able to process data in a timely manner. The present work introduces a novel approach for online mobility model detection along with a new concept for trajectory abstraction based on velocity features. Finally, the proposal is evaluated with a real-world dataset.

Classification of Spatio-Temporal Trajectories Based on Support Vector Machines

Within the mobility mining discipline, several solutions for the classification of spatio-temporal trajectories have been proposed. However, they usually do not fully consider the particularities of trajectories from human-generated data like online social networks. For that reason, this work introduces a novel classifier based on Support Vector Machines (SVM), which fits the low resolution of this type of geographic data. This solution is applied in a use case for the detection of tourist mobility exhibiting quite promising results.

Online route prediction based on clustering of meaningful velocity-change areas

Personal route prediction has emerged as an important topic within the mobility mining domain. In this context, many proposals apply an off-line learning process before being able to run the on-line prediction algorithm. The present work introduces a novel framework that integrates the route learning and the prediction algorithm in an on-line manner. By means of a thin-client and server architecture, it also puts forward a new concept for route abstraction based on the detection of spatial regions where certain velocity features of routes frequently change. The proposal is evaluated by real-world and synthetic datasets and compared with a well-established mechanism by exhibiting quite promising results.

Tram-Based Mobility Mining with Event Processing of Transit-Card Transactions

The mining of transit-card transactions is nowadays an interesting solution for human mobility detection in urban areas. However, most existing approaches in this domain do not face current needs of most stakeholders that already require to extract meaningful knowledge from these transactions in real time. In this context, the present work introduces a novel method based on Complex Event Processing and fuzzy clustering to extract different profiles of travellers of a public transit system at the same time smart-card transactions are generated. Experiments in a real tram system scenario shows the suitability of the proposal.