Aurora González-Vidal

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.

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.

Human Mobility Prediction Based on Social Media with Complex Event Processing

The combination of mobile and social media sensors is foreseen to become a crucial course of action so as to comprehensively capture and understand the movement of people in large spatial regions. In that sense, the present work describes a novel personal location predictor that makes use of these two types of sensors. Firstly, it extracts the mobility models of an area capturing aspects related to particular users along with crowd-based features on the basis of geotagged tweets. Unlike previous approaches, the proposed solution mines such models in an online manner so that no previous off-line training is required. Then, on the basis of such models, a predictor able to forecast the next activity and position of a user is developed. Finally, the described approach is tested by using Twitter datasets from two different cities.

Human mobility analysis based on social media and fuzzy clustering

A better understanding of the movement of a city aids to the efficient adaptation of the energy consumption to the necessities of citizens. For this purpose, the use of clustering algorithms applied to large amounts of geo-tagged data generated in social-networks is foreseen to become an interesting course of action. This will help to comprehensively capture and understand the movement of people in large spatial regions. Due to the nature of this kind of data (with high levels of uncertainty and noise) soft-computing owns the necessary characteristics to extract accurate mobility models. The present work introduces a novel approach to extract personal mobility patterns by means of the fuzzy c-means (FCM) algorithm. A preliminary study with a real Twitter database is also included.

TOWARDS ANTICIPATE DETECTION OF COMPLEX EVENT PROCESSING RULES WITH PROBABILISTIC MODELLING

Nowadays, Big Data implies not only the need of processing high volume of data, but also do it in a timely manner. In this scope, the Complex Event Processing (CEP) paradigm has arisen as a prominent real-time rule-based solution. Due to its reactive nature, a CEP system might suffer from slight delays in the activation of its rules that could not be desirable in certain environments. As a result, the present work introduces a novel mechanism that intends to anticipate the activation of event-based rules and, thus, come up with even faster CEP systems. This is achieved by means of a probabilistic modelling of each rule’s precondition. Finally, the proposal includes a preliminary evaluation so as to show its suitability.

Human Mobility Modelling Based on Dense Transit Areas Detection with Opportunistic Sensing

With the advent of smartphones, opportunistic mobile crowdsensing has become an instrumental approach to perceive large-scale urban dynamics. In this context, the present work presents a novel approach based on such a sensing paradigm to automatically identify and monitor the areas of a city comprising most of the human transit. Unlike previous approaches, the system performs such detection in real time at the same time the opportunistic sensing is carried out. Furthermore, a novel multilayered grill partitioning to represent such areas is stated. Finally, the proposal is evaluated by means of a real-world dataset.