RGIM: An Integrated Approach to Improve QoS in AODV, DSR and DSDV Routing Protocols for FANETS Using the Chain Mobility Model

Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol

A Smooth-Turn Mobility Model for Airborne Networks

In this article, I introduce a novel airborne network mobility model, called the Smooth Turn Mobility Model, that captures the correlation of acceleration for airborne vehicles across time and spatial coordinates. Effective routing in airborne networks (ANs) relies on suitable mobility models that capture the random movement pattern of airborne vehicles. As airborne vehicles cannot make sharp turns as easily as ground vehicles do, the widely used mobility models for Mobile Ad Hoc Networks such as Random Waypoint and Random Direction models fail. Our model is realistic in capturing the tendency of airborne vehicles toward making straight trajectory and smooth turns with large radius, and whereas is simple enough for tractable connectivity analysis and routing design

Routing schemes in FANETs: a survey

Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues

A novel mobility model based on semi-random circular movement in mobile ad hoc networks

National High Technology Research and Development Program of China, China [200701Z464, 2007AA01Z475]; Postdoctoral Science Foundation [20090451384]; Higher Education of China [20070698107]; Shaanxi Natural Science Foundation [20061746]When simulating a mobile ad hoc network (MANET), it is important to use a realistic mobility model to reflect the actual performance of a mobile system. The spatial distribution of node locations in a mobile model plays a key role when investigating the characteristics of a MANET. However, most existing mobility models with random and simple straight line movement lead to unrealistic scenarios and non-uniform distributions, and can not describe the actual movement of Unmanned Aerial Vehicles (UAVs) connected via a MANET. To address this issue, a novel mobility model based on semi-random circular movement (SRCM) is presented. The approximate node distribution function in SRCM is derived within a 2D disk region. The relationship between application performance and node distribution is investigated for a UAV MANET, with focus on scan coverage and network connectivity. A simulation using the NS2 tool is conducted. It is shown that the presented model with a uniform distribution performs better than the popular Random Waypoint mobility model. The SRCM model with the NS2 simulator provides a realistic way for simulation and performance evaluation of UAV MANETs. (C) 2009 Elsevier Inc. All rights reserved

可量化的移动Ad Hoc网络时空动态特性评估方法

移动模型是Ad Hoc网络区别于其他形式网络的重要标志,对其产生的动态网络特性(简称动态特性)进行评估,是研究Ad Hoc网络的协议仿真和网络相关技术(如拓扑控制和网络性能测量等)的基础性问题.在已有研究的基础上,改进了网络的模型化描述,克服了以往模型无法很好地描述相关联的时空动态特性的缺陷,并在此基础上,提出了移动模型通用的可量化时空动态特性评估方法.通过构建节点空间位置分布,建立网络拓扑时空动态特性的分析模型,深入研究了几种移动模型的动态性.提出一种圆周曲线移动模型,弥补了以往移动模型难以描述现实的曲线移动场景.仿真实验结果表明,该方法能够有效地对现有移动模型的动态性进行评估.实验结果表明,圆周曲线移动模型与以往移动模型相比,具有良好的时空动态特性

A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing

[EN] Recently, Unmanned Aerial Vehicles (UAVs) have become a cheap alternative to sense pollution values in a certain area due to their flexibility and ability to carry small sensing units. In a previous work, we proposed a solution, called Pollution-driven UAV Control (PdUC), to allow UAVs to autonomously trace pollutant sources, and monitor air quality in the surrounding area. However, despite operational, we found that the proposed solution consumed excessive time, especially when considering the battery lifetime of current multi-rotor UAVs. In this paper, we have improved our previously proposed solution by adopting a space discretization technique. Discretization is one of the most efficient mathematical approaches to optimize a system by transforming a continuous domain into its discrete counterpart. The improvement proposed in this paper, called PdUC-Discretized (PdUC-D), consists of an optimization whereby UAVs only move between the central tile positions of a discretized space, avoiding monitoring locations separated by small distances, and whose actual differences in terms of air quality are barely noticeable. We also analyze the impact of varying the tile size on the overall process, showing that smaller tile sizes offer high accuracy at the cost of an increased flight time. Taking into account the obtained results, we consider that a tile size of 100 x 100 meters offers an adequate trade-off between flight time and monitoring accuracy. Experimental results show that PdUC-D drastically reduces the convergence time compared to the original PdUC proposal without loss of accuracy, and it also increases the performance gap with standard mobility patterns such as Spiral and Billiard.This work was partially supported by the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R", the framework of the DIVINA Challenge Team, which is funded under the Labex MS2T program. Labex MS2T is supported by the French Government, through the program "Investments for the future" managed by the National Agency for Research (Reference: ANR-11-IDEX-0004-02), the "Programa de becas SENESCYT de la Republica del Ecuador", and the Research Direction of the University of Cuenca.Alvear-Alvear, Ó.; Tavares De Araujo Cesariny Calafate, CM.; Zema, N.; Natalizio, E.; Hernández-Orallo, E.; Cano, J.; Manzoni, P. (2018). A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing. Mobile Networks and Applications. 23(6):1693-1702. https://doi.org/10.1007/s11036-018-1065-4S16931702236Adam-poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A (2014) Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian Maximum entropy–LUR approaches. Environ Health Perspect 970(2013):1–19. https://doi.org/10.1289/ehp.1306566Agency U.S.E.P. (2015) Air Quality Index Available: http://cfpub.epa.gov/airnow/index.cfm?action=aqibasics.aqiAlvear O, Calafate CT, Hernández-Orallo E, Cano JC, Manzoni P (2015) Mobile Pollution Data Sensing Using UAVs The 13th International Conference on Advances in Mobile Computing and MultimediaAlvear O, Zamora W, Calafate C, Cano JC, Manzoni P (2016) An architecture offering mobile pollution sensing with high spatial resolution. J Sens:2016Alvear O, Zema NR, Natalizio E, Calafate CT (2017) Using uav-based systems to monitor air pollution in areas with poor accessibility. J Adv Transp:2017Alvear OA, Zema NR, Natalizio E, Calafate CT (2017) A chemotactic pollution-homing uav guidance system. In: 2017 13th international Wireless communications and mobile computing conference (IWCMC). IEEE, pp 2115–2120André M (2004) The artemis european driving cycles for measuring car pollutant emissions. Sci Total Environ 334:73–84Basu P, Redi J, Shurbanov V (2004) Coordinated flocking of uavs for improved connectivity of mobile ground nodes. In: 2004 IEEE Military communications conference, MILCOM, vol 3. IEEE, pp 1628–1634Biomo JDMM, Kunz T, St-Hilaire M (2014) An enhanced gauss-markov mobility model for simulations of unmanned aerial ad hoc networks. In: 2014 7th IFIP Wireless and mobile networking conference (WMNC). IEEE, pp 1–8Bouachir O, Abrassart A, Garcia F, Larrieu N (2014) A mobility model for uav ad hoc network. 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Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

[EN] Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants' concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.This work has been partially carried out in the framework of the DIVINA Challenge Team, which is funded under the Labex MS2T program. Labex MS2T is supported by the French Government, through the program "Investments for the Future" managed by the National Agency for Research (Reference: ANR-11-IDEX-0004-02). This work was also supported by the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R," the "Programa de Becas SENESCYT de la Republica del Ecuador," and the Research Direction of University of Cuenca.Alvear-Alvear, Ó.; Zema, NR.; Natalizio, E.; Tavares De Araujo Cesariny Calafate, CM. (2017). Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility. Journal of Advanced Transportation. 2017:1-14. https://doi.org/10.1155/2017/8204353S1142017Seaton, A., Godden, D., MacNee, W., & Donaldson, K. (1995). Particulate air pollution and acute health effects. The Lancet, 345(8943), 176-178. doi:10.1016/s0140-6736(95)90173-6McFrederick, Q. S., Kathilankal, J. C., & Fuentes, J. D. (2008). Air pollution modifies floral scent trails. Atmospheric Environment, 42(10), 2336-2348. doi:10.1016/j.atmosenv.2007.12.033Mage, D., Ozolins, G., Peterson, P., Webster, A., Orthofer, R., Vandeweerd, V., & Gwynne, M. (1996). Urban air pollution in megacities of the world. Atmospheric Environment, 30(5), 681-686. doi:10.1016/1352-2310(95)00219-7Mayer, H. (1999). Air pollution in cities. Atmospheric Environment, 33(24-25), 4029-4037. doi:10.1016/s1352-2310(99)00144-2Kanaroglou, P. S., Jerrett, M., Morrison, J., Beckerman, B., Arain, M. A., Gilbert, N. L., & Brook, J. R. (2005). Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach. Atmospheric Environment, 39(13), 2399-2409. doi:10.1016/j.atmosenv.2004.06.049Alvear, O., Zamora, W., Calafate, C., Cano, J.-C., & Manzoni, P. (2016). An Architecture Offering Mobile Pollution Sensing with High Spatial Resolution. Journal of Sensors, 2016, 1-13. doi:10.1155/2016/1458147Adam-Poupart, A., Brand, A., Fournier, M., Jerrett, M., & Smargiassi, A. (2014). Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches. Environmental Health Perspectives, 122(9), 970-976. doi:10.1289/ehp.1306566Pujadas, M., Plaza, J., Terés, J., Artı́ñano, B., & Millán, M. (2000). Passive remote sensing of nitrogen dioxide as a tool for tracking air pollution in urban areas: the Madrid urban plume, a case of study. Atmospheric Environment, 34(19), 3041-3056. doi:10.1016/s1352-2310(99)00509-9Eisenman, S. B., Miluzzo, E., Lane, N. D., Peterson, R. A., Ahn, G.-S., & Campbell, A. T. (2009). BikeNet. ACM Transactions on Sensor Networks, 6(1), 1-39. doi:10.1145/1653760.1653766André, M. (2004). The ARTEMIS European driving cycles for measuring car pollutant emissions. Science of The Total Environment, 334-335, 73-84. doi:10.1016/j.scitotenv.2004.04.070Hu, S.-C., Wang, Y.-C., Huang, C.-Y., & Tseng, Y.-C. (2011). Measuring air quality in city areas by vehicular wireless sensor networks. Journal of Systems and Software, 84(11), 2005-2012. doi:10.1016/j.jss.2011.06.043Dunbabin, M., & Marques, L. (2012). Robots for Environmental Monitoring: Significant Advancements and Applications. IEEE Robotics & Automation Magazine, 19(1), 24-39. doi:10.1109/mra.2011.2181683Hugenholtz, C. H., Moorman, B. J., Riddell, K., & Whitehead, K. (2012). Small unmanned aircraft systems for remote sensing and Earth science research. Eos, Transactions American Geophysical Union, 93(25), 236-236. doi:10.1029/2012eo250005Pajares, G. (2015). Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281-330. doi:10.14358/pers.81.4.281Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. doi:10.1016/j.isprsjprs.2014.02.013Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138-146. doi:10.1890/120150Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712. doi:10.1007/s11119-012-9274-5Bellvert, J., Zarco-Tejada, P. J., Girona, J., & Fereres, E. (2013). Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15(4), 361-376. doi:10.1007/s11119-013-9334-5Erman, A., Hoesel, L., Havinga, P., & Wu, J. (2008). 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Mission-based mobility models for UAV networks

Las redes UAV han atraído la atención de los investigadores durante la última década. Las numerosas posibilidades que ofrecen los sistemas single-UAV aumentan considerablemente al usar múltiples UAV. Sin embargo, el gran potencial del sistema multi-UAV viene con un precio: la complejidad de controlar todos los aspectos necesarios para garantizar que los UAVs cumplen la misión que se les ha asignado. Ha habido numerosas investigaciones dedicadas a los sistemas multi-UAV en el campo de la robótica en las cuales se han utilizado grupos de UAVs para diferentes aplicaciones. Sin embargo, los aspectos relacionados con la red que forman estos sistemas han comenzado a reclamar un lugar entre la comunidad de investigación y han hecho que las redes de UAVs se consideren como un nuevo paradigma entre las redes multi-salto. La investigación de redes de UAVs, de manera similar a otras redes multi-salto, se divide principalmente en dos categorías: i) modelos de movilidad que capturan la movilidad de la red, y ii) algoritmos de enrutamiento. Ambas categorías han heredado muchos algoritmos que pertenecían a las redes MANET, que fueron el primer paradigma de redes multi-salto que atrajo la atención de los investigadores. Aunque hay esfuerzos de investigación en curso que proponen soluciones para ambas categorías, el número de modelos de movilidad y algoritmos de enrutamiento específicos para redes UAV es limitado. Además, en el caso de los modelos de movilidad, las soluciones existentes propuestas son simplistas y apenas representan la movilidad real de un equipo de UAVs, los cuales se utilizan principalmente en operaciones orientadas a misiones, en la que cada UAV tiene asignados movimientos específicos. Esta tesis propone dos modelos de movilidad basados en misiones para una red de UAVs que realiza dos operaciones diferentes. El escenario elegido en el que se desarrollan las misiones corresponde con una región en la que ha ocurrido, por ejemplo, un desastre natural. La elección de este tipo de escenario se debe a que en zonas de desastre, la infraestructura de comunicaciones comúnmente está dañada o totalmente destruida. En este tipo de situaciones, una red de UAVs ofrece la posibilidad de desplegar rápidamente una red de comunicaciones. El primer modelo de movilidad, llamado dPSO-U, ha sido diseñado para capturar la movilidad de una red UAV en una misión con dos objetivos principales: i) explorar el área del escenario para descubrir las ubicaciones de los nodos terrestres, y ii) hacer que los UAVs converjan de manera autónoma a los grupos en los que se organizan los nodos terrestres (también conocidos como clusters). El modelo de movilidad dPSO-U se basa en el conocido algoritmo particle swarm optimization (PSO), considerando los UAV como las partículas del algoritmo, y también utilizando el concepto de valores dinámicos para la inercia, el local best y el neighbour best de manera que el modelo de movilidad tenga ambas capacidades: la de exploración y la de convergencia. El segundo modelo, denominado modelo de movilidad Jaccard-based, captura la movilidad de una red UAV que tiene asignada la misión de proporcionar servicios de comunicación inalámbrica en un escenario de mediano tamaño. En este modelo de movilidad se ha utilizado una combinación del virtual forces algorithm (VFA), de la distancia Jaccard entre cada par de UAVs y metaheurísticas como hill climbing y simulated annealing, para cumplir los dos objetivos de la misión: i) maximizar el número de nodos terrestres (víctimas) que se encuentran bajo el área de cobertura inalámbrica de la red UAV, y ii) mantener la red UAV como una red conectada, es decir, evitando las desconexiones entre UAV. Se han realizado simulaciones exhaustivas con herramientas software específicamente desarrolladas para los modelos de movilidad propuestos. También se ha definido un conjunto de métricas para cada modelo de movilidad. Estas métricas se han utilizado para validar la capacidad de los modelos de movilidad propuestos de emular los movimientos de una red UAV en cada misión.UAV networks have attracted the attention of the research community in the last decade. The numerous capabilities of single-UAV systems increase considerably by using multiple UAVs. The great potential of a multi-UAV system comes with a price though: the complexity of controlling all the aspects required to guarantee that the UAV team accomplish the mission that it has been assigned. There have been numerous research works devoted to multi-UAV systems in the field of robotics using UAV teams for different applications. However, the networking aspects of multi-UAV systems started to claim a place among the research community and have made UAV networks to be considered as a new paradigm among the multihop ad hoc networks. UAV networks research, in a similar manner to other multihop ad hoc networks, is mainly divided into two categories: i) mobility models that capture the network mobility, and ii) routing algorithms. Both categories have inherited previous algorithms mechanisms that originally belong to MANETs, being these the first multihop networking paradigm attracting the attention of researchers. Although there are ongoing research efforts proposing solutions for the aforementioned categories, the number of UAV networks-specific mobility models and routing algorithms is limited. In addition, in the case of the mobility models, the existing solutions proposed are simplistic and barely represent the real mobility of a UAV team, which are mainly used in missions-oriented operations. This thesis proposes two mission-based mobility models for a UAV network carrying out two different operations over a disaster-like scenario. The reason for selecting a disaster scenario is because, usually, the common communication infrastructure is malfunctioning or completely destroyed. In these cases, a UAV network allows building a support communication network which is rapidly deployed. The first mobility model, called dPSO-U, has been designed for capturing the mobility of a UAV network in a mission with two main objectives: i) exploring the scenario area for discovering the location of ground nodes, and ii) making the UAVs to autonomously converge to the groups in which the nodes are organized (also referred to as clusters). The dPSO-U mobility model is based on the well-known particle swarm optimization algorithm (PSO), considering the UAVs as the particles of the algorithm, and also using the concept of dynamic inertia, local best and neighbour best weights so the mobility model can have both abilities: exploration and convergence. The second one, called Jaccard-based mobility model, captures the mobility of a UAV network that has been assigned with the mission of providing wireless communication services in a medium-scale scenario. A combination of the virtual forces algorithm (VFA), the Jaccard distance between each pair of UAVs and metaheuristics such as hill climbing or simulated annealing have been used in this mobility model in order to meet the two mission objectives: i) to maximize the number of ground nodes (i.e. victims) under the UAV network wireless coverage area, and ii) to maintain the UAV network as a connected network, i.e. avoiding UAV disconnections. Extensive simulations have been performed with software tools that have been specifically developed for the proposed mobility models. Also, a set of metrics have been defined and measured for each mobility model. These metrics have been used for validating the ability of the proposed mobility models to emulate the movements of a UAV network in each mission