Fernando Terroso-Saenz

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.

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.

An open IoT platform for the management and analysis of energy data

Abstract Buildings are key players when looking at end-use energy demand. It is for this reason that during the last few years, the Internet of Things (IoT) has been considered as a tool that could bring great opportunities for energy reduction via the accurate monitoring and control of a large variety of energy-related agents in buildings. However, there is a lack of IoT platforms specifically oriented towards the proper processing, management and analysis of such large and diverse data. In this context, we put forward in this paper the IoT Energy Platform (IoTEP) which attempts to provide the first holistic solution for the management of IoT energy data. The platform we show here (that has been based on FIWARE) is suitable to include several functionalities and features that are key when dealing with energy quality insurance and support for data analytics. As part of this work, we have tested the platform IoTEP with a real use case that includes data and information from three buildings totalizing hundreds of sensors. The platform has exceed expectations proving robust, plastic and versatile for the application at hand.

Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings

Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.

Towards Energy Efficiency Smart Buildings Models Based on Intelligent Data Analytics

Abstract This work presents how to proceed during the processing of all available data coming from smart buildings to generate models that predict their energy consumption. For this, we propose a methodology that includes the application of different intelligent data analysis techniques and algorithms that have already been applied successfully in related scenarios, and the selection of the best one depending on the value of the selected metric used for the evaluation. This result depends on the specific characteristics of the target building and the available data. Among the techniques applied to a reference building, Bayesian Regularized Neural Networks and Random Forest are selected because they provide the most accurate predictive results.

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.

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.