Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios

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

Contribution to the design of VANET routing protocols for realistic urban environments

One of the main concerns of the cities' administration is mobility management. In Intelligent Transportation Systems (ITS), pedestrians, vehicles and public transportation systems could share information and react to any situation in the city. The information sensed by vehicles could be useful for other vehicles and for the mobility authorities. Vehicular Ad hoc Networks (VANETs) make possible the communication between vehicles (V2I) and also between vehicles and fixed infrastructure (V2I) managed by the city's authorities. In addition, VANET routing protocols minimize the use of fixed infrastructure since they employ multi-hop V2V communication to reach reporting access points of the city. This thesis aims to contribute in the design of VANET routing protocols to enable reporting services (e.g., vehicular traffic notifications) in urban environments. The first step to achieve this global objective has been the study of components and tools to mimic a realistic VANET scenario. Moreover, we have analyzed the impact of the realism of each one of those components in the simulation results. Then, we have improved the Address Resolution procedure in VANETs by including it in the routing signaling messages. Our approach simplifies the VANET operation and increases the packet delivery ratio as consequence. Afterwards, we have tackled the issue of having duplicate packets in unicast communications and we have proposed routing filters to lower their presence. This way we have been able to increase the available bandwidth and reduce the average packet delay with a slight increase of the packet losses. Besides, we have proposed a Multi-Metric Map aware routing protocol (MMMR) that incorporates four routing metrics (distance, trajectory, vehicle density and available bandwidth) to take the forwarding decisions. With the aim of increasing the number of delivered packets in MMMR, we have developed a Geographical Heuristic Routing (GHR) algorithm. GHR integrates Tabu and Simulated Annealing heuristic optimization techniques to adapt its behavior to the specific scenario characteristics. GHR is generic because it could use any geographical routing protocol to take the forwarding decisions. Additionally, we have designed an easy to implement forwarding strategy based on an extended topology information area of two hops, called 2-hops Geographical Anycast Routing (2hGAR) protocol. Results show that controlled randomness introduced by GHR improves the default operation of MMMR. On the other hand, 2hGAR presents lower delays than GHR and higher packet delivery ratio, especially in high density scenarios. Finally, we have proposed two mixed (integer and linear) optimization models to detect the best positions in the city to locate the Road Side Units (RSUs) which are in charge of gathering all the reporting information generated by vehicles.Una de las principales preocupaciones en la administración de las ciudades es la gestión de la movilidad de sus vehículos, debido a los problemas de tráfico como atascos y accidentes. En los sistemas inteligentes de transporte (SIT), peatones, vehículos y transporte público podrán compartir información y adaptarse a cualquier situación que suceda en la ciudad. La información obtenida por los sensores de los vehículos puede ser útil para otros vehículos y para las autoridades de movilidad. Las redes ad hoc vehiculares (VANETs) hacen posible la comunicación entre los propios vehículos (V2V) y entre vehículos y la infraestructura fija de la red de la ciudad (V2I). Asimismo, los protocolos de encaminamiento para redes vehiculares minimizan el uso de infraestructura fija de red, ya que los protocolos de encaminamiento VANET emplean comunicaciones multisalto entre vehículos para encaminar los mensajes hasta los puntos de acceso de la red en la ciudad. El objetivo de esta tesis doctoral es contribuir en el diseño de protocolos de encaminamiento en redes ad hoc vehiculares para servicios de notificaciones (p.ej. reportes del estado del tráfico) en entornos urbanos. El primer paso para alcanzar este objetivo general ha sido el estudio de componentes y herramientas para simular un escenario realista de red ad hoc vehicular. Además, se ha analizado el impacto del nivel de realismo de cada uno de los componentes de simulación en los resultados obtenidos. Así también, se ha propuesto un mecanismo de resolución de direcciones automático y coherente para redes VANET a través del uso de los propios mensajes de señalización de los protocolos de encaminamiento. Esta mejora simplifica la operación de una red ad hoc vehicular y como consecuencia aumenta la tasa de recepción de paquetes. A continuación, se ha abordado el problema de la aparición inesperada de paquetes de datos duplicados en una comunicación punto a punto. Para ello, se ha propuesto el filtrado de paquetes duplicados a nivel del protocolo de encaminamiento. Esto ha producido un incremento del ancho disponible en el canal y una reducción del retardo medio en la trasmisión de un paquete, a costa de un mínimo aumento de la pérdida de paquetes. Por otra parte, hemos propuesto un protocolo de encaminamiento multi-métrica MMMR (Multi-Metric Map-aware Routing protocol), el cual incorpora cuatro métricas (distancia al destino, trayectoria, densidad de vehículos y ancho de banda) en las decisiones de encaminamiento. Con el objetivo de aumentar la tasa de entrega de paquetes en MMMR, hemos desarrollado un algoritmo heurístico de encaminamiento geográfico denominado GHR (Geographical Heuristic Routing). Esta propuesta integra las técnicas de optimización Tabu y Simulated Annealing, que permiten a GHR adaptarse a las características específicas del escenario. Adicionalmente, hemos propuesto 2hGAR (2-hops Geographical Anycast Routing), un protocolo de encaminamiento anycast que emplea información de la topología de red a dos saltos de distancia para tomar la decisión de encaminamiento de los mensajes. Los resultados muestran que la aleatoriedad controlada de GHR en su operación mejora el rendimiento de MMMR. Asimismo, 2hGAR presenta retardos de paquete menores a los obtenidos por GHR y una mayor tasa de paquetes entregados, especialmente en escenarios con alta densidad de vehículos. Finalmente, se han propuesto dos modelos de optimización mixtos (enteros y lineales) para detectar los mejores lugares de la ciudad donde ubicar los puntos de acceso de la red, los cuales se encargan de recolectar los reportes generados por los vehículos.Postprint (published version

An Evolutionary Computational Approach for Designing Micro Hydro Power Plants

Micro Hydro Power Plants (MHPP) constitute an effective, environmentally-friendly solution to deal with energy poverty in rural isolated areas, being the most extended renewable technology in this field. Nevertheless, the context of poverty and lack of qualified manpower usually lead to a poor usage of the resources, due to the use of thumb rules and user experience to design the layout of the plants, which conditions the performance. For this reason, the development of robust and efficient optimization strategies are particularly relevant in this field. This paper proposes a Genetic Algorithm (GA) to address the problem of finding the optimal layout for an MHPP based on real scenario data, obtained by means of a set of experimental topographic measurements. With this end in view, a model of the plant is first developed, in terms of which the optimization problem is formulated with the constraints of minimal generated power and maximum use of flow, together with the practical feasibility of the layout to the measured terrain. The problem is formulated in both single-objective (minimization of the cost) and multi-objective (minimization of the cost and maximization of the generated power) modes, the Pareto dominance being studied in this last case. The algorithm is first applied to an example scenario to illustrate its performance and compared with a reference Branch and Bound Algorithm (BBA) linear approach, reaching reductions of more than 70% in the cost of the MHPP. Finally, it is also applied to a real set of geographical data to validate its robustness against irregular, poorly sampled domains.Agencia Española de Cooperación Internacional para el Desarrollo 2014 / ACDE / 00601

Dissimilarity Metric Based on Local Neighboring Information and Genetic Programming for Data Dissemination in Vehicular Ad Hoc Networks (VANETs)

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios

Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transportation Systems 2013

Challenges arising from an increasing traffic demand, limited resource availability and growing quality expectations of the customers can only be met successfully, if each transport mode is regarded as an intelligent transportation system itself, but also as part of one intelligent transportation system with “intelligent” intramodal and intermodal interfaces. This topic is well reflected in the Third International Conference on “Models and Technologies for Intelligent Transportation Systems” which took place in Dresden 2013 (previous editions: Rome 2009, Leuven 2011). With its variety of traffic management problems that can be solved using similar methods and technologies, but with application specific models, objective functions and constraints the conference stands for an intensive exchange between theory and practice and the presentation of case studies for all transport modes and gives a discussion forum for control engineers, computer scientists, mathematicians and other researchers and practitioners. The present book comprises fifty short papers accepted for presentation at the Third Edition of the conference. All submissions have undergone intensive reviews by the organisers of the special sessions, the members of the scientific and technical advisory committees and further external experts in the field. Like the conference itself the proceedings are structured in twelve streams: the more model-oriented streams of Road-Bound Public Transport Management, Modelling and Control of Urban Traffic Flow, Railway Traffic Management in four different sessions, Air Traffic Management, Water Traffic and Traffic and Transit Assignment, as well as the technology-oriented streams of Floating Car Data, Localisation Technologies for Intelligent Transportation Systems and Image Processing in Transportation. With this broad range of topics this book will be of interest to a number of groups: ITS experts in research and industry, students of transport and control engineering, operations research and computer science. The case studies will also be of interest for transport operators and members of traffic administration

“Are we in this together?” : embedding social identity detection in drones improves emergency coordination

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders’ decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors’ talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system

“Are we in this together?”: embedding social identity detection in drones improves emergency coordination

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders’ decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors’ talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system