Susel Fernandez

Ontology Alignment Architecture for Semantic Sensor Web Integration

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entitys names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall.

Distributed Approach for SmartGrids Reconfiguration Based on the OSPF Routing Protocol

Smart grids (SG) are essential for efficient management and monitoring of electric power networks. One of the most important tasks in SG focuses on fault detection and automatic network reconfiguration. This process allows minimizing power losses and load balancing in distribution networks. In this paper, an adaptation of the open shortest path first (OSPF) routing protocol to accomplish the network reconfiguration task is proposed. The algorithm is intended to run in secondary substation nodes over an agent-based distributed architecture. The proposed algorithm has been tested on the IEEE 123 modified node test feeder and on an actual grid deployed by an electrical distribution company. Moreover, a performance comparison with a typical centralized reconfiguration algorithm is carried out.

Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors.

A Fuzzy Rule-Based System for Ontology Mapping

Ontologies are a crucial tool for formally specifying the vocabulary and the concepts of agent platforms, so, to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. The treatment of uncertainty plays a key role in the ontology mapping, as the degree of overlapping between concepts can not be represented logically. This paper aims to provide mechanisms to support experts in the first steps of the ontology mapping process using fuzzy logic techniques to determine the similarity between concepts from different ontologies. For each pair of concepts, two types of similarity are calculated: the first using the Jaccard coefficient, based on relevant documents taken from the web, and the second based on the linguistic relationship of concepts. Finally, the similarity is calculated through a fuzzy rule-based system. The ideas presented in this work are validated using two real-world ontologies.

Driver classification for intelligent transportation systems using fuzzy logic

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element that affects road safety is driver behavior, because driver errors are usually the principal cause of traffic accidents. Therefore, understanding and modeling human driver behavior is extremely important for the safety of the road transportation. In this paper, a Fuzzy rule-based system to classify the drivers in different profiles according to their behavior is proposed. The system will be integrated in intelligent transportation architecture, which can be used to predict and avoid traffic accidents and to optimize the routing management.

Architecture for Intelligent Transportation System Based in a General Traffic Ontology

Intelligenttransportationsystems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is that vehicles can exchange information not only among themselves but with other elements in the road infrastructure through different applications. For the success of this exchange of information, a common framework of knowledge that allows interoperability is needed. In this paper an ontology-based system to provide roadside assistance is proposed, providing drivers making decisions in different situations, taking into account information on different traffic-related elements such as routes, traffic signs, traffic regulations and weather elements.

Multi-layer agent-based architecture for smart grid reconfiguration

Smart grids are an essential tool for efficient management and monitoring of electric power. In this area, distributed programming techniques help to optimize the production and distribution of electrical energy in order to balance supply and demand to ensure better services. In this paper an agent-based distributed architecture for supervision and control of Smart Grids is proposed. The information comes from the equipment installed in the secondary substations of distribution networks of medium and low voltage. It is exchanged through a real-time middleware and processed through a multi-agent system. One of the most important tasks implemented in the multi-agent system focuses on fault detection and automatic network reconfiguration. This process allows minimizing power losses and loading balancing in the distribution network. To accomplish this task Particle Swarm Optimization (PSO) algorithm is applied.

Multi-layer agent-based architecture for smart grid monitoring

Today smart grids are an essential tool for efficient management and monitoring of electric power. Computer technologies are used to optimize the production and distribution of electrical energy in order to balance supply and demand between producers and consumers and ensure better services. In this paper we propose an ontology-based distributed architecture for supervision and control of Smart Grids. The information comes from the equipment installed in the secondary substations of distribution networks of medium and low voltage. It is exchanged through a real-time middleware and processed through a multi-agent system.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Reconfiguration Algorithm to Reduce Power Losses in Offshore HVDC Transmission Lines

This paper proposes the use of the Particle Swarm Optimization algorithm to reconfigure a High Voltage Direct Current transmission system in order to reduce losses. The transmission system consists of several offshore wind farms that generate energy which is sent to the grid through several HVDC transmission lines. The proposed algorithm modify the interconnection between the lines with the purpose of reduce the power losses in the lines.