Abstract

English: road safety has become a main issue for governments and car manufacturers in the last twenty years. Thus, the concept of Intelligent Transportation Systems (ITS) appeared. This concept is used when talking about communication technologies between vehicles and infrastructure that improve transport safety, its management, environmental performance etc. Due to the high economic cost of real-life tests and experimentation, the use of simulators becomes really useful when developing ITS. Nonetheless, simulators not always include all the capabilities needed to simulate these kinds of networks. Thus, in this project the NCTUns simulator is modified in order to add new capabilities that allow users simulate ITS. Furthermore, urban and interurban scenarios are simulated in order to evaluate how the use of these networks decreases the drivers reaction time when an unexpected situation occurs and how modifications made in NCTUns work. Castellano: la seguridad en la carretera se ha convertido en un problema principal para gobiernos y fabricantes de automóviles en los últimos años. De esta forma, se creó el concepto de Sistema de Transporte Inteligente (STI), concepto utilizado al hablar sobre las tecnologías de comunicación entre vehículos e infraestructura que mejoran la seguridad vial en el transporte, su mejor gestión, su eficiencia medioambiental, etc. Debido al alto coste económico de probar STI en situaciones reales, el uso de simuladores es realmente útil a la hora de desarrollar STI. Así, en este proyecto el simulador NCTUns ha sido modificado con el objetivo de añadir nuevas posibilidades al simulador que ayuden a diseñar STI. Además, un escenario urbano y otro interurbano han sido simulados con el objetivo de evaluar como el uso de estas redes hace disminuir el tiempo de reacción del conductor cuando una situación inesperada ocurre y comprobar cómo funcionan los cambios realizados en el simulador. Català: la seguretat a la carretera ha esdevingut un problema principal pels governs i pels fabricants d'automòbils en els últims anys. D'aquesta manera, es crea el concepte de Sistema de Transport Intel'ligent (STI), concepte utilitzat en parlar sobre les tecnologies de comunicació entre vehicles i infraestructura que milloren la seguretat vial en el transport, la seva millor gestió, l'eficiència mediambiental, etc. A causa de l'alt cost econòmic de provar STI en situacions reals, l'ús de simuladors és realment útil a l'hora de desenvolupar STI. Així, en aquest projecte el simulador NCTUns ha estat modificat amb l'objectiu d'afegir noves possibilitats al simulador que ajudin a dissenyar STI a futurs usuaris. A més, un escenari urbà i un altre interurbà han estat simulats amb l'objectiu d'avaluar com l'ús d'aquestes xarxes fa disminuir el temps de reacció del conductor quan una situació inesperada succeeix i comprovar com funcionen els canvis realitzats en el simulador.

Abstract

TNO has formulated the ambition of founding a basis for the development of flexible multi-data source and multi-application (ad hoc) sensor networks. These networks are envisioned on a scale that is beyond networks for specific and separate sensor networks. These separate networks need in the future to be combined intelligently in order to assist users with real-time assessment during operations in challenging environments. For this, enabling technologies such as network architectures and model-based data-interpretation are being developed. In this context TNO has developed wireless sensor networks that consist of very small and cheap nodes. Each node can be the size of a golf ball and can run for a long time on an A4 battery. They are designed to be deployed on a massive scale (100-10k or larger). In the tactical environment they are typically lying on the ground. Because it is very difficult to test the performance of such deployments under realistic and controllable conditions, simulation is the only way to get insight in this. This paper focuses on how to study the performance of massive deployments of these sensor networks through simulation. It describes the modeling, validation and simulation of such networks. Features such as antenna patterns, radio propagation/path loss, terrain, medium access and transport protocols, routing, and simulation parallelization are discussed. Model validation experiments for the antenna pattern, the path loss propagation and communication performance for nodes lying on the ground will be discussed. It further describes the implementation of a promising energy aware MAC and transport protocol that are proposed in the literature and are suitable for congestion mitigation and cross-layer optimization. Results on timeliness of messages, bandwidth and power consumption are discussed. ©2008 IEEE.

Abstract

htmlabstractAd hoc networks are self-configuring networks of mobile nodes, connected by wireless links. If a destination node is beyond the transmission range of an origin node, then the nodes must cooperate to provide a multi-hop route. Any node can act as a sender, receiver or transit node. It is clear that it is in a node‚The interest to be a sender or receiver, but it is less clear what the value is of forwarding traffic on behalf of other nodes. The nodes should therefore be given incentives to act as transit nodes, otherwise the network would fail to function. A way to do so is by introducing for each node a credit balance, where nodes use credits to pay for the costs of sending their own traffic, and earn credits by forwarding traffic from other nodes. However, nodes that are located near the edge of the network will attract little transit traffic and earn few credits. In contrast, nodes located near the centroid of the network will attract transit traffic and earn credits. We investigate various ways of providing nodes near the edge of the network with preferential treatment in order to improve their credit balance and their throughputs. We next focus on the situation where each node can move to improve its utility expressed in terms of either credit balance or throughput. Here radio interference plays an important role, as it defines an interesting trade-off: nodes may prefer to be close together in order to reduce the power needed to transmit data, but on the other hand proximity increases radio interference, and has therefore a negative effect on connectivity. Simulation experiments reveal that the positions of the nodes converge to non-trivial optimal positions on 2D and 3D surfaces.