Natural computing for vehicular networks

La presente tesis aborda el diseño inteligente de soluciones para el despliegue de redes vehiculares ad-hoc (vehicular ad hoc networks, VANETs). Estas son redes de comunicación inalámbrica formada principalmente por vehículos y elementos de infraestructura vial. Las VANETs ofrecen la oportunidad para desarrollar aplicaciones revolucionarias en el ámbito de la seguridad y eficiencia vial. Al ser un dominio tan novedoso, existe una serie de cuestiones abiertas, como el diseño de la infraestructura de estaciones base necesaria y el encaminamiento (routing) y difusión (broadcasting) de paquetes de datos, que todavía no han podido resolverse empleando estrategias clásicas. Es por tanto necesario crear y estudiar nuevas técnicas que permitan de forma eficiente, eficaz, robusta y flexible resolver dichos problemas. Este trabajo de tesis doctoral propone el uso de computación inspirada en la naturaleza o Computación Natural (CN) para tratar algunos de los problemas más importantes en el ámbito de las VANETs, porque representan una serie de algoritmos versátiles, flexibles y eficientes para resolver problemas complejos. Además de resolver los problemas VANET en los que nos enfocamos, se han realizado avances en el uso de estas técnicas para que traten estos problemas de forma más eficiente y eficaz. Por último, se han llevado a cabo pruebas reales de concepto empleando vehículos y dispositivos de comunicación reales en la ciudad de Málaga (España). La tesis se ha estructurado en cuatro grandes fases. En la primera fase, se han estudiado los principales fundamentos en los que se basa esta tesis. Para ello se hizo un estudio exhaustivo sobre las tecnologías que emplean las redes vehiculares, para así, identificar sus principales debilidades. A su vez, se ha profundizado en el análisis de la CN como herramienta eficiente para resolver problemas de optimización complejos, y de cómo utilizarla en la resolución de los problemas en VANETs. En la segunda fase, se han abordado cuatro problemas de optimización en redes vehiculares: la transferencia de archivos, el encaminamiento (routing) de paquetes, la difusión (broadcasting) de mensajes y el diseño de la infraestructura de estaciones base necesaria para desplegar redes vehiculares. Para la resolución de dichos problemas se han propuesto diferentes algoritmos CN que se clasifican en algoritmos evolutivos (evolutionary algorithms, EAs), métodos de inteligencia de enjambre (swarm intelligence, SI) y enfriamiento simulado (simulated annealing, SA). Los resultados obtenidos han proporcionado protocolos de han mejorado de forma significativa las comunicaciones en VANETs. En la tercera y última fase, se han realizado experimentos empleando vehículos reales circulando por las carreteras de Málaga y que se comunicaban entre sí. El principal objetivo de estas pruebas ha sido el validar las mejoras que presentan los protocolos que se han optimizado empleando CN. Los resultados obtenidos de las fases segunda y tercera confirman la hipótesis de trabajo, que la CN es una herramienta eficiente para tratar el diseño inteligente en redes vehiculares

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic

Comunicación eficiente entre vehículos aplicando un algoritmo multi-objetivo paralelo

El encaminamiento de datos en redes vehiculares presenta un desafío debido a los frecuentes e inesperados cambios de topología. En este estudio presentamos un algoritmo de optimización multiobjetivo paralelo para tratar este tipo de problemas con el fin de maximizar la cantidad de datos que se intercambian y minimizar los tiempos de transmisión. Esta forma de abordarlo es novedosa porque en este dominio la mayoría de trabajos de optimización emplean técnicas secuenciales y/o mono-objetivas, hecho que limita su eficacia. Los resultados experimentales muestran que el algoritmo optimizado mejora de forma significativa a las otras propuestas en el estado del arte. Además, la aplicación de nuestro modelo paralelo obtiene una eficiencia computacional mayor de un 86%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Beca FPU (AP2010-3108) otorgada por el Gobierno de España. Proyecto TIN 2011-28194 (roadME) del Ministerio de Economía y Competitividad y fondos FEDER dentro y el proyecto número 8.06/5.47.4142 en colaboración con la VSB-Technical University de Ostrava

A Multi-Objectif Genetic Algorithm-Based Adaptive Weighted Clustering Protocol in VANET

International audience—Vehicular Ad hoc NETwork (VANET) is the main component that is used recently for the development of Intelligent Transportation Systems (ITSs). VANET has a highly dynamic and portioned network topology due to the constant and rapid movement of vehicles. Recently, the clustering algorithms are widely used as the control schemes to make VANET topology less dynamic for MAC, routing and security protocols. An efficient clustering algorithm must take into consideration all the necessary information related to node mobility. In this paper, we propose an Adaptive Weighted Clustering Protocol (AWCP), specially designed for vehicular networks, which takes the highway ID, direction of vehicles, position, speed and the number of neighbors vehicles into account in order to enhance the network topology stability. However, the multiple control parameters of our AWCP, make parameter tuning a non-trivial problem. In order to optimize AWCP protocol, we define a multi-objective problem whose inputs are the AWCPs parameters and whose objectives are: providing stable cluster structure as possible, maximizing data delivery rate, and reducing the clustering overhead. We then face this multi-objective problem with the the Multi-Objective Genetic Algorithm (MOGA). We evaluate and compare its performance with other multi-objective optimization techniques: Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Differential Evolution (MODE). The experiments analysis reveal that NSGA-II improves the results of MOPSO and MODE in terms of the spacing, spread, and ratio of non-dominated solutions and generational distance metrics used for comparison

Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version

High Speed Vehicle Detection in Vehicular Ad-hoc Network

Millions of people are dying in car accidents. Accidents occur mainly due to the alcoholic nature of the driver or due to high speed of vehicle. So, by controlling the high speed of vehicle we could decrease the number of accidents. In this thesis we propose the mechanism for detecting high speed vehicle in Vehicular Ad Hoc Network (VANET). The proposed scheme uses the position of a vehicle to compute its average speed. In order to know the surrounding condition, each vehicle periodically broadcasts its position, timing information in its communication range. When a vehicle enters the radio range of an Road Side Unit (RSU), the RSU receives the position and timing information of the vehicle and send it to the Central Server. The Central Server computes the average speed of the vehicle using time and position information received from the RSUs. When a vehicle is found to have more speed than the speed limit of the specific region, the Central Server broadcasts this information to all RSUs in its range. When the vehicle again comes into the range of an RSU, it informs the vehicle to drive within the speed limit using a warning message. When a vehicle is found violating the speed limit a number of times more than a threshold, then Central Server forwards this violation information to the Certification Authority (CA). Based on the threshold, the CA takes appropriate action on the high speed vehicle. In order to evaluate the performance of our scheme, we have used Veins hybrid simulator which works on OMNeT++ as a network simulator, Simulation of Urban mobility (SUMO) as traffic simulator. Traci is used for interlinking network simulator with road traffic simulator. By doing simulation with these simulators we have compared our scheme with that of other scheme

Modeling and simulation of routing protocol for ad hoc networks combining queuing network analysis and ANT colony algorithms

The field of Mobile Ad hoc Networks (MANETs) has gained an important part of the interest of researchers and become very popular in last few years. MANETs can operate without fixed infrastructure and can survive rapid changes in the network topology. They can be studied formally as graphs in which the set of edges varies in time. The main method for evaluating the performance of MANETs is simulation. Our thesis presents a new adaptive and dynamic routing algorithm for MANETs inspired by the Ant Colony Optimization (ACO) algorithms in combination with network delay analysis. Ant colony optimization algorithms have all been inspired by a specific foraging behavior of ant colonies which are able to find, if not the shortest, at least a very good path connecting the colony’s nest with a source of food. Our evaluation of MANETs is based on the evaluation of the mean End-to-End delay to send a packet from source to destination node through a MANET. We evaluated the mean End-to-End delay as one of the most important performance evaluation metrics in computer networks. Finally, we evaluate our proposed ant algorithm by a comparative study with respect to one of the famous On-Demand (reactive) routing protocols called Ad hoc On-Demand Distance Vector (AODV) protocol. The evaluation shows that, the ant algorithm provides a better performance by reducing the mean End-to-End delay than the AODV algorithm. We investigated various simulation scenarios with different node density and pause times. Our new algorithm gives good results under certain conditions such as, increasing the pause time and decreasing node density. The scenarios that are applied for evaluating our routing algorithm have the following assumptions: 2-D rectangular area, no obstacles, bi-directional links, fixed number of nodes operate for the whole simulation time and nodes movements are performed according to the Random Waypoint Mobility (RWM) or the Boundless Simulation Area Mobility (BSAM) model. KEYWORDS: Ant Colony Optimization (ACO), Mobile Ad hoc Network (MANET), Queuing Network Analysis, Routing Algorithms, Mobility Models, Hybrid Simulation

AN ADAPTIVE INFORMATION DISSEMINATION MODEL FOR VANET COMMUNICATION

Vehicular ad hoc networks (VANETs) have been envisioned to be useful in road safety and many commercial applications. The growing trend to provide communication among the vehicles on the road has provided the opportunities for developing a variety of applications for VANET. The unique characteristics of VANET bring about new research challenges