10 research outputs found

    Optimal RoadSide Units Distribution Approach in Vehicular Ad hoc Network

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    A vehicular ad hoc network is a particular type of ad hoc mobile network. It is characterized by high mobility and frequent disconnection between vehicles. For this, the roadside units (RSUs) deployment permits to enhance the network connectivity. The objective of this work is to provide an optimized RSUs placement for enhancing the network connectivity and maximizing the accident coverage with reducing the deployment cost. In this paper, we propose our approach called Optimized RoadSide units Deployment (ORSD). The proposed approach comprises a two-step, in the first step, ORSD finds the RSUs candidate locations based on network density and connectivity. We calculated the connectivity of each segment based on speed and arrival information’s.  The second step permit to find the optimal solution of our proposed objective function. The objective function permits to enhance the network connectivity and maximizing the accident coverage.  To find the optimal solution of our objective function is an NP-complete problem of order o(n²) .  Therefore, we propose to solve this problem in two phases, so that it becomes a simple linear problem to solve. The ORSD is proposed for urban and high way scenarios. The extensive simulation study is conducted in order to assess the effectiveness of the proposed approach. We use the Simulator of Urban MObility (SUMO) for generating different traffic scenarios. We develop scripts to extract different information as density, speed and travel time in each segment. Then, we develop an algorithm to calculate connectivity probability for each segment. Then, we implement our objective function to finds optimal RSUs positions in terms of connectivity, accident cover and cost

    A Centrality-based RSU Deployment Approach for Vehicular Ad Hoc Networks

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    International audienceThis paper studies the RSU deployment problem in a 2-D road scenario of a vehicular ad hoc network. To optimize RSU deployment, we introduce the notion of centrality in a social network to RSU deployment, and use it to measure the importance of an RSU position candidate in RSU deployment. Based on the notion of centrality, we propose a centrality-based RSU deployment approach and formulate the RSU deployment problem as a linear programing problem with the objective to maximize the total centrality of all position candidates selected for RSU deployment under the constraint of a given deployment budget. To solve the formulated problem, we analogize the problem to a 0-1 Knapsack problem and thus employ a 0-1 Knapsack algorithm to solve the problem. In the analogy, the budget in the RSU deployment problem is analogous to the bag's capacity in the Knapsack problem, the cost of deploying an RSU is analogous to an item's weight, and the centrality of a position candidate is analogous to an item's value. Simulation results show that the proposed centrality-based deployment approach can effectively improve the efficiency of the RSU deployment in terms of the coverage time ratio as compared to a random deployment approach

    A Traffic-Aware Approach for Enabling Unmanned Aerial Vehicles (UAVs) in Smart City Scenarios

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    In smart cities, vehicular applications require high computation capabilities and low-latency communication. Edge computing offers promising solutions for addressing these requirements because of several features, such as geo-distribution, mobility, low latency, heterogeneity, and support for real-time interactions. To employ network edges, existing fixed roadside units can be equipped with edge computing servers. Nevertheless, there are situations where additional infrastructure units are required to handle temporary high traffic loads during public events, unexpected weather conditions, or extreme traffic congestion. In such cases, the use of flying roadside units are carried by unmanned aerial vehicles (UAVs), which provide the required infrastructure for supporting traffic applications and improving the quality of service. UAVs can be dynamically deployed to act as mobile edges in accordance with traffic events and congestion conditions. The key benefits of this dynamic approach include: 1) the potential for characterizing the environmental requirements online and performing the deployment accordingly, and 2) the ability to move to another location when necessary. We propose a traffic-aware method for enabling the deployment of UAVs in vehicular environments. Simulation results show that our proposed method can achieve full network coverage under different scenarios without extra communication overhead or delay

    Optimal Roadside Units Placement In Urban Areas For Vehicular Networks

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    The most important component of a vehicular ad hoc network (VANET), besides VANET-enabled vehicles, is roadside units (RSUs). The effectiveness of a VANET largely depends on the density and location of these RSUs. During the initial stages of VANET, it will not be possible to deploy a large number of RSUs either due to the low market penetration of VANET-enabled vehicles or due to the deployment cost of RSUs. There is, therefore, a need to optimally place a limited number of RSUs in a given region in order to achieve maximum performance. In this paper, we present two different optimization methods for placement of a limited number of RSUs in an urban region: an analytical Binary Integer Programming (BIP) method and a novel Balloon Expansion Heuristic (BEH) method. BIP method utilizes branch and bound approach to find an optimal analytical solution whereas BEH method uses balloon expansion analogy to find an optimal or near optimal solution. Our evaluations show that both methods perform optimally or near optimally compared with the exhaustive method. Further, BEH method is more versatile and performs better than BIP method in terms of computational cost and scalability. © 2012 IEEE

    Optimal Roadside Units Placement in Urban Areas for Vehicular Networks

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    Abstract — The most important component of a vehicular ad hoc network (VANET), besides VANET-enabled vehicles, is roadside units (RSUs). The effectiveness of a VANET largely depends on the density and location of these RSUs. During the initial stages of VANET, it will not be possible to deploy a large number of RSUs either due to the low market penetration of VANET-enabled vehicles or due to the deployment cost of RSUs. There is, therefore, a need to optimally place a limited number of RSUs in a given region in order to achieve maximum performance. In this paper, we present two different optimization methods for placement of a limited number of RSUs in an urban region: an analytical Binary Integer Programming (BIP) method and a novel Balloon Expansion Heuristic (BEH) method. BIP method utilizes branch and bound approach to find an optimal analytical solution whereas BEH method uses balloon expansion analogy to find an optimal or near optimal solution. Our evaluations show that both methods perform optimally or near optimally compared with the exhaustive method. Further, BEH method is more versatile and performs better than BIP method in terms of computational cost and scalability. Keywords-VANET; roadside unit; initial deployment stage; optimization; placement; urban areas I

    Reliable Message Dissemination in Mobile Vehicular Networks

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    Les réseaux véhiculaires accueillent une multitude d’applications d’info-divertissement et de sécurité. Les applications de sécurité visent à améliorer la sécurité sur les routes (éviter les accidents), tandis que les applications d’info-divertissement visent à améliorer l'expérience des passagers. Les applications de sécurité ont des exigences rigides en termes de délais et de fiabilité ; en effet, la diffusion des messages d’urgence (envoyés par un véhicule/émetteur) devrait être fiable et rapide. Notons que, pour diffuser des informations sur une zone de taille plus grande que celle couverte par la portée de transmission d’un émetteur, il est nécessaire d’utiliser un mécanisme de transmission multi-sauts. De nombreuses approches ont été proposées pour assurer la fiabilité et le délai des dites applications. Toutefois, ces méthodes présentent plusieurs lacunes. Cette thèse, nous proposons trois contributions. La première contribution aborde la question de la diffusion fiable des messages d’urgence. A cet égard, un nouveau schéma, appelé REMD, a été proposé. Ce schéma utilise la répétition de message pour offrir une fiabilité garantie, à chaque saut, tout en assurant un court délai. REMD calcule un nombre optimal de répétitions en se basant sur l’estimation de la qualité de réception de lien dans plusieurs locations (appelées cellules) à l’intérieur de la zone couverte par la portée de transmission de l’émetteur. REMD suppose que les qualités de réception de lien des cellules adjacentes sont indépendantes. Il sélectionne, également, un nombre de véhicules, appelés relais, qui coopèrent dans le contexte de la répétition du message d’urgence pour assurer la fiabilité en multi-sauts. La deuxième contribution, appelée BCRB, vise à améliorer REMD ; elle suppose que les qualités de réception de lien des cellules adjacentes sont dépendantes ce qui est, généralement, plus réaliste. BCRB utilise les réseaux Bayésiens pour modéliser les dépendances en vue d’estimer la qualité du lien de réception avec une meilleure précision. La troisième contribution, appelée RICS, offre un accès fiable à Internet. RICS propose un modèle d’optimisation, avec une résolution exacte optimale à l'aide d’une technique de réduction de la dimension spatiale, pour le déploiement des passerelles. Chaque passerelle utilise BCRB pour établir une communication fiable avec les véhicules.Vehicular networks aim to enable a plethora of safety and infotainment applications. Safety applications aim to preserve people's lives (e.g., by helping in avoiding crashes) while infotainment applications focus on enhancing the passengers’ experience. These applications, especially safety applications, have stringent requirements in terms of reliability and delay; indeed, dissemination of an emergency message (e.g., by a vehicle/sender involved in a crash) should be reliable while satisfying short delay requirements. Note, that multi-hop dissemination is needed to reach all vehicles, in the target area, that may be outside the transmission range of the sender. Several schemes have been proposed to provide reliability and short delay for vehicular applications. However, these schemes have several limitations. Thus, the design of new solutions, to meet the requirement of vehicular applications in terms of reliability while keeping low end-to-end delay, is required. In this thesis, we propose three schemes. The first scheme is a multi-hop reliable emergency message dissemination scheme, called REMD, which guarantees a predefined reliability , using message repetitions/retransmissions, while satisfying short delay requirements. It computes an optimal number of repetitions based on the estimation of link reception quality at different locations (called cells) in the transmission range of the sender; REMD assumes that link reception qualities of adjacent cells are independent. It also adequately selects a number of vehicles, called forwarders, that cooperate in repeating the emergency message with the objective to satisfy multi-hop reliability requirements. The second scheme, called BCRB, overcomes the shortcoming of REMD by assuming that link reception qualities of adjacent cells are dependent which is more realistic in real-life scenarios. BCRB makes use of Bayesian networks to model these dependencies; this allows for more accurate estimation of link reception qualities leading to better performance of BCRB. The third scheme, called RICS, provides internet access to vehicles by establishing multi-hop reliable paths to gateways. In RICS, the gateway placement is modeled as a k-center optimisation problem. A space dimension reduction technique is used to solve the problem in exact time. Each gateway makes use of BCRB to establish reliable communication paths to vehicles

    Experimental verification of multi-antenna techniques for aerial and ground vehicles’ communication

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    Controle distribuído de tráfego baseado em veículos conectados

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    Although advanced traffic management systems can deal with the heterogeneous traffic flows approaching of intersections, their performances are compromised, when the traffic volume is not distributed uniformly. To evenly distribute the traffic flow, an advanced driver information system should be aware of the traffic control operations. However, such requirement can not ultimately be satisfied due to the gaps in state of the art in advanced traffic management systems. Therefore, this study proposes a distributed traffic control system, in which agents embedded in connected vehicles, traffic signals, urban elements and a traffic control center interact with each other to provide a greater traffic fluidity. Therefore, the agents depend strongly on a heterogeneous vehicular network. In this sense, this study also proposes a heterogeneous vehicular network whose communication protocol can satisfy the communication requirements of intelligent transportation systems service applications. According to the results obtained from simulations, the distributed traffic control system was able to maximize the flow of vehicles and the mean speed of the vehicles, and minimize the wait time, travel time, fuel consume and emissions (CO, CO2, HC, NOx and PMx).Por mais que sistemas avançados de gerenciamento de tráfego consigam lidar com o problema da heterogeneidade dos fluxos de tráfego das vias que incidem nas interseções de uma rede viária, estes têm o seu desempenho comprometido, quando o volume de tráfego da rede viária não é distribuído de maneira uniforme. Para tratar este problema, um sistema avançado de informações ao motorista deve ter total ciência do estado de operação de um sistema avançado de gerenciamento de tráfego. No entanto, este requisito não pode ser completamente satisfeito, devido `a existência de lacunas existentes no estado da arte de sistemas avançados de gerenciamento de tráfego e sistemas avançados de informações ao motorista, em específico, a cooperação entre estes dois tipos de sistemas. Por isso, este trabalho propõe um sistema de controle distribuído de tráfego, em que agentes embutidos em veículos conectados, sinalizações semafóricas, elementos urbanos e um centro de controle de tráfego interagem uns com os outros, a fim de promover uma maior fluidez do tráfego veicular. Para tanto, os agentes dependem fortemente de uma rede veicular heterogênea. O trabalho também propõe uma rede veicular heterogênea cujo protocolo de comunicação é capaz de satisfazer os requisitos de comunicação de aplicações de serviços de sistemas inteligentes de transporte. De acordo com os resultados obtidos por meio de simulações, o sistema de controle distribuído de tráfego foi capaz de maximizar o fluxo de veículos e a velocidade média dos veículos, e minimizar o tempo de espera, número de paradas, tempo de viagem, consumo de combustível e emissões (CO, CO2, HC, NOx e PMx)

    AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks

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    [EN] Vehicular ad hoc network (VANET) is a mobile and wireless network that consists of connected vehicles, and stationary nodes called roadside units (RSUs) placed on the aboard of roads to improve traffic safety and to ensure drivers' and passengers' comfort. However, deploying RSUs is one of the most important challenges in VANETs due to the involved placement, configuration, and maintenance costs in addition to the network connectivity. This study focuses on the issue of deploying a set of RSUs that is able to maximize network coverage with a reduced cost. In this paper, we propose a new formulation of RSUs deployment issue as a maximum intersection coverage problem through a graph-based modeling. Moreover, we propose a new bio-inspired RSU placement system called Ant colony optimization system for RSU deployment in VANET (AC-RDV). AC-RDV is based on the idea of placing RSUs within the more popular road intersections, which are close to popular places like touristic and commercial areas. Since RSU deployment problem is considered as NP-Hard, AC-RDV inspires by the foraging behavior of real ant colonies to discover the minimum number of RSU intersections that ensures the maximum network connectivity. After a set of simulations and comparisons against traditional RSU placement strategies, the results obtained showed the effectiveness of the proposed AC-RDV in terms of number of RSUs placed, the average area coverage, the average connectivity and the overlapping ratio.Guerna, A.; Bitam, S.; Tavares De Araujo Cesariny Calafate, CM. (2021). AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks. Peer-to-Peer Networking and Applications. 14(2):627-643. https://doi.org/10.1007/s12083-020-01011-3S627643142Mejri MN, Ben-Othman J, Hamdi M (2018) Survey on security challenge and possible cryptographic solutions. Vehic Commun 1(2):53–66Hanshi SM, Wan T, Kadhum MM, Bin-Salem AA (2018) Review of geographic forwarding strategies for inter-vehicular communications from mobility and environment perspectives. Vehic Commun 14:64–79Bitam S, Mellouk A (2014) Routing for vehicular Ad Hoc networks, Bio-Inspired Routing Protocols for Vehicular Ad Hoc Networks, Wiley EditionMuhammad M, Safdar GA (2018) Survey on existing authentication issues for cellular-assisted V2X communication. Vehic Commun 12:50–65Wang Z, Zheng J, Wu Y, Mitton N (2017) A centrality-based RSU deployment approach for vehicular ad hoc networks. In : 2017 IEEE International Conference on Communications (ICC). IEEE, p 1–5Liu H, Ding S, Yang L, Yang T (2014) A -based strategy for roadside units placement in vehicular ad hoc networks. Int J Hybrid Info Technol 7(1):91–108Trullols O, Fiore M, Casetti C, Chiasserini C, Ordinas JB (2010) Planning roadside infrastructure for information dissemination in intelligent transportation systems. Comput Commun 33(4):432–442Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Courier CorporationHromkovič J (2013) Algorithmics for hard problems: introduction to combinatorial optimization, randomization, approximation, and heuristics, ed: Springer Science & Business MediaJo Y, Jeong J (2016) RPA: Road-Side Units Placement Algorithm for Multihop Data Delivery in Vehicular Networks. In: IEEE 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp 262–266Chi J, Jo Y, Park H, Park S (2013) Intersection-priority based optimal RSU allocation for VANET, in: Fifth International Conference on Ubiquitous and Future Networks (ICUFN), pp 350–355Dorigo M, Birattari M, Blum C, Gambardella LM, Mondada F, Stützle T (2004) Ant Colony Optimization and Swarm Intelligence, In: 6th International Conference (ANTS), Belgium, ed: Springer, 5217Guerna A, Bitam S (2019) GICA: An evolutionary strategy for roadside units deployment in vehicular networks,in: International Conference on Networking and Advanced Systems (ICNAS). IEEE, p 1–6Liya X, Chuanhe H, Peng L, Junyu Z (2013) A Randomized Algorithm for Roadside Units Placement in Vehicular Ad Hoc, In: IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks MSN, pp 193–197Liu C, Huang H, Du H (2017) Optimal RSUs deployment with delay bound along highways in VANET. J Comb Optim 33(4):1168–1182Aslam B, Amjad F, Zou CC (2012) Optimal roadside units placement in urban areas for vehicular networks. In: IEEE Symposium on Computers and Communications ISCC, pp 423–429Patil P, Gokhale A (2013) Voronoi-based placement of road-side units to improve dynamic resource management in Vehicular Ad Hoc Net-works. In: International Conference on Collaboration Technologies and Systems (CTS), pp 389–396Ghorai C, Banerjee I (2018) A constrained Delaunay triangulation based RSUs deployment strategy to cover a convex region with obstacles for maximizing communications probability between V2I. Vehic Commun 13:89–103Cavalcante ES, Aquino AL, Pappa GL (2012) Roadside unit deployment for information dissemination in a VANET: an evolutionary approach, In: Proceedings of the 14th annual conference companion on genetic and evolutionary computation, ACM, New York, pp. 27–34Sarubbi JFM, Vieira D, Wanner E, Silva CM (2016) A GRASP-based heuristic for allocating the roadside infrastructure maximizing the number of distinct vehicles experiencing contact opportunities. IEEE Symposium on Network Operations and Management (NOMS), pp 1187–1192Kim D, Velasco Y, Wang W, Uma RN, Hussain R, Lee S (2017) A new comprehensive RSU installation strategy for cost-efficient VANET deployment. IEEE Trans Veh Technol 66(5):4200–4211Irit D, Safra S (2005) On the hardness of approximating minimum vertex cover. Annals Math: 439–485Srinivanas M, Patnaik LM (1994) Genetic algorithms: A survey. Computer 27(6):17–26Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithms for discrete optimization. Art&Life 5(2):137–172Jovanovic R, Tuba M (2011) An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem. Appl Soft Comput 11(8):5360–5366Reis AB, Sargento S, Neves F (2014) Deploying roadside units in sparse vehicular networks: What really works and what does not. IEEE Trans Veh Technol 63(6):2794–2806Lessing L, Dumitrescu I, Stützle T (2004) A comparison between ACO algorithms for the set covering problem, in: International Workshop on ant colony optimization and swarm, ed: Springer, Berlin, pp 1–12Zaki MJ, Meira W Jr (2014) Data mining and analysis fundamental concept and algorithms. Cambridge University Press, Cambridg
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