Fuzzy Modelling for Human Dynamics Based on Online Social Networks

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities

A Cooperative Approach to Traffic Congestion Detection With Complex Event Processing and VANET

Currently, distributed traffic information systems have come up as one of the most important approaches for detecting traffic flow problems on a road. For that purpose, they usually make use of the location information that vehicles share among them through periodical messages that are transmitted across a vehicular ad hoc network (VANET). This paper puts forward an event-driven architecture (EDA) as a novel mechanism to get insight into VANET messages to detect different levels of traffic jams; furthermore, it also takes into account environmental data that come from external data sources, such as weather conditions. The proposed EDA has been developed through the complex-event-processing technology. Simulation tests show that the proposed mechanism can detect traffic congestions, which involve different numbers of lanes and lengths with short delay

Vehicle Maneuver Detection with Accelerometer-Based Classification

In the mobile computing era, smartphones have become instrumental tools to develop innovative mobile context-aware systems. In that sense, their usage in the vehicular domain eases the development of novel and personal transportation solutions. In this frame, the present work introduces an innovative mechanism to perceive the current kinematic state of a vehicle on the basis of the accelerometer data from a smartphone mounted in the vehicle. Unlike previous proposals, the introduced architecture targets the computational limitations of such devices to carry out the detection process following an incremental approach. For its realization, we have evaluated different classification algorithms to act as agents within the architecture. Finally, our approach has been tested with a real-world dataset collected by means of the ad hoc mobile application developed

Applicability of Big Data Techniques to Smart Cities Deployments

This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.</p