Nuria Gómez

Evaluation of V2V and V2I mesh prototypes based on a wireless sensor network

Wireless vehicular communications have been identified as one of the most important Intelligent Transportation System technologies to be developed in the coming years, from a safety as well as an energy efficiency point of view. Several standards definitions, hardware developments, research projects, use cases definitions and field operational tests are currently under deployment in order to accelerate the market availability of this technology. Nevertheless, though the possible applications of vehicular communications are defined, there is a lack of availability of completely functional communication technology to support these applications. Issues like the adaptation of communication technologies to run in a massive high speed mobile environment, the behaviour of Vehicle Ad-hoc Networks (VANET) in multihop mesh mode or routing algorithms for reliable V2V and V2I data transmission are currently open research areas. In this paper we present an evaluation of V2V and V2I prototypes based on wireless sensor mesh networks, tested in real vehicles with real communication hardware. This paper opens up a frame to solve some of the present deficiencies of vehicular communications and enable the possibility of testing safety applications in real VANET situations.

Vulnerable Road Users Detection Using V2X Communications

Vehicle to vehicle (V2V) communications allow to sharing real-time information among the vehicles that circulate on the nearby road areas. These communications systems are today in an early stage of development and the effort are focused into two directions, on one hand, the development of the communications infrastructure to support the information exchange. On the other hand, the development of Advanced Driver Assistance Systems (ADAS) as well as safety systems that takes advantages of these communications. Additionally, the V2V communications systems are mainly focused to cars and trucks, showing less concern for Vulnerable Road Users, pedestrians, motorcyclists and cyclists. In this paper a novel ADAS is presented, focused to avoid accidents that involve motorcyclist and cyclists using V2V communications, incorporating them to the vehicular networking taking into account the intrinsic features of each collective and adapting to increase their safety.

GeoNetworking based V2V Mesh Communications over WSN

An intensive effort is presently carried out in order to research and develop vehicular communication systems. Several standards, prototypes and field operational tests are under deployment all around the world, involving multiple research centers and vehicle and electronics manufacturers. However, some research lines are open today, in which vehicular communication systems with guaranteed reliability and robustness are necessary. One of these challenges is the development of an effective GeoNetworking in vehicular communications. This concept means that the vehicle ad-hoc network (VANET) bases the data package transmission in the topographical location of the different nodes (vehicles) of the network, organizing the data flow in an optimal way that covers the surroundings of each vehicle. The core of this GeoNetworking system is the GeoRouting algorithm, that supports the optimal routing of the data packages and reorganizes the network structure in function of the positions of the nodes. In this paper we present a novel GeoRouting algorithm, based in the evolution of previous results of mesh vehicular networks. This algorithm has been designed, implemented and tested in real vehicles in real roads with free flow traffic. The results suggest that the features of the designed routing algorithm can be applied as core of any kind of vehicular network to support ADAS applications.

Highway test of V2V mesh communications over WSN

Wireless vehicular communications are one of the most important technologies in the ITS field to improve the transport safety as well efficiency impact. Several standards definitions, hardware developments, research projects, use cases definitions and field operational tests are currently under deployment in order to accelerate the market availability of this technology. Nevertheless, though the possible applications of vehicular communications are defined, there is a lack of availability of completely functional communication technology to support these applications. In this paper we present the results of a V2V communications field operational test using Wireless Sensor Networks (WSN) as nodes of the VANET and three vehicles that circulate on public highways in free flow traffic situations. These results show that this communications technology is able to support continuous mesh data transmission with enough features of efficiency and reliability to be used as main data source in a big set of advanced driver assistance systems (ADAS).

Solving complex problems with a bioinspired model

Abstract Membrane systems are parallel and bioinspired systems which simulate membranes behavior when processing information. As a part of unconventional computing, P-systems are proven to be effective in solving complex problems. A software technique is presented here that obtain good results when dealing with such problems. The rules application phase is studied and updated accordingly to obtain the desired results. Certain rules are candidate to be eliminated which can make the model improving in terms of time.

Hierarchical Function Approximation with a Neural Network Model

This article presents a neural network model that permits to build a conceptual hierarchy to approximate functions over a given interval. Bio-inspired axo-axonic connections are used. In these connections the signal weight between two neurons is computed by the output of other neuron. Such arquitecture can generate polynomial expressions with lineal activation functions. This network can approximate any pattern set with a polynomial equation. This neural system classifies an input pattern as an element belonging to a category that the system has, until an exhaustive classification is obtained. The proposed model is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to propagate the activation. Each neural network is in charge of the input pattern recognition to any prototyped category, and also in charge of transmitting the activation to other neural networks to be able to continue with the approximation. [Article copies are available for purchase from InfoSci-on-Demand.com]