Mercedes Valdes-Vela

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.

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.

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.

Stem water potential estimation of drip-irrigated early-maturing peach trees under Mediterranean conditions

Seasonal Ψstem is a useful diagnostic tool for peach tree irrigation management.The autumn rainfall events point to the resilient behaviour of the peach cultivar studied.The soil water content was the main contributor to Ψstem estimation.Ψstem was estimated by regression equation of soil water content, GDH and VPDm data. In the last decade deficit irrigation strategies allowed growers to deal with water shortages, while monitoring stem water potential (Ψstem) is deemed essential for maximising fruit yield and quality. However, because of the intensive labour involved in measuring Ψstem, alternative methods are desirable. The experiment described was conducted in Murcia (Spain) with adult peach trees (Prunus persica (L.) Batsch cv. Flordastar) submitted to different drip irrigation treatments, measuring Ψstem with a pressure chamber and the soil water content with a neutron probe. Agro-meteorological variables were recorded. Seasonal patterns of stem water potential provide a useful diagnostic tool for irrigation management in peach trees. Rainfall events and the meteorological conditions prevailing in autumn pointed to the resilient nature of the peach cultivar studied. Fitting Ψstem by linear regression analysis as a function of soil and atmosphere yielded a significant correlation, with the soil water content being the main contributor to estimating Ψstem. Linear regression analysis highlighted the importance of considering plant water status as a function of the peach tree cultivar, the atmospheric conditions in which it develops and the soil water conditions resulting from irrigation. A multiple linear regression equation based on soil water content in the soil profile, mean daily air vapour pressure deficit (VPDm) and growing degree hours (GDH) data explained 72% of the variance in Ψstem, and is proposed as an alternative to the field measurement of Ψstem.

An application of a fuzzy classifier extracted from data for collision avoidance support in road vehicles

Road traffic collisions are an outstanding problem in current developed societies. This paper presents a solution to support collision avoidance based on the timely detection of the vehicle maneuvers. Since the longitudinal interaction among vehicles, with the commonly known car-following behavior, is one of the most important causes of crashes, it was decided to focus on longitudinal maneuvers, identifying the maneuvering states of cruise, accelerating or decelerating and stop. The classification is carried out by means of fuzzy rules extracted from navigational data. Therefore, in our proposal no extra sensors are needed apart from two commonly installed for navigation purposes: the odometry of the vehicle and an accelerometer. The system was tested with low-cost sensors showing good results when compared to the literature of the field.

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.

Neuro-fuzzy Based Maneuver Detection for Collision Avoidance in Road Vehicles

The issue of collision avoidance in road vehicles has been investigated from many different points of view. An interesting approach for Road Vehicle Collision Assistance Support Systems (RVCASS) is based on the creation of a scene of the vehicles involved in a potentially conflictive traffic situation. This paper proposes a neuro-fuzzy approach for dynamic classification of the vehicles roles in a scene. For that purpose, different maneuver state models for longitudinal movements of road vehicles have been defined, and a prototype has been equipped with INS (Inertial Navigation Systems) and GPS (Global Positioning System) sensors. Trials with real data show the suitability of the proposed neuro-fuzzy approach for solving support to the problem under consideration.

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.

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.

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.