Opportunistic communication leverages communication opportunities arising by chance to provide significant performance benefit and even enable communication where it would be impossible otherwise. The goal of this dissertation is to optimize opportunistic communication to achieve good performance in wireless networks. A key challenge in optimizing opportunistic communication arises from dynamic and incidental nature of communication. Complicated wireless interference patterns, high mobility, and frequent fluctuations in wireless medium make the optimization even harder. This dissertation proposes a series of optimization frameworks that systematically optimizes opportunistic communication to achieve good performance in wireless mesh networks and vehicular networks. We make the following three major contributions: First, we develop novel algorithms, techniques, and protocols that optimize opportunistic communication of wireless mesh network to achieve good, predictable user performance. Our framework systematically optimizes end-to-end performance (e.g., total throughput). It yields significant improvement over existing routing schemes. We also show that it is robust against inaccuracy introduced by dynamic network conditions. Second, we propose a novel overlay framework to exploit inter-flow network coding in opportunistic routing. In this framework, an overlay network performs inter-flow coding to effectively reduce traffic imposed on the underlay network, and an underlay network uses optimized opportunistic routing to provide efficient and reliable overlay links. We show that inter-flow coding together with opportunistic routing and rate-limiting brings significant performance benefit. Finally, we develop a novel optimization framework in vehicular networks to effectively leverage opportunistic contacts between vehicles and access points (APs). We develop a new mobility prediction algorithm and an optimization algorithm to determine an efficient replication scheme that exploit the synergy among Internet connectivity, local wireless connectivity, mesh network connectivity, and vehicular relay connectivity. Based on our framework, we develop a practical system that enables high-bandwidth content distribution and demonstrate the effectiveness of our approach using simulation, emulation, and testbed experiments. / text

One of the most challenging issues facing vehicular networks lies in the design of an efficient MAC protocol due to mobile nature of nodes, delay constraints for safety applications and interference. In this thesis, I propose an efficient Multichannel QoS Cognitive MAC (MQOG). MQOG assesses the quality of channel prior to transmission employing dynamic channel allocation and negotiation algorithms to achieve significant increase in channel reliability, throughput and delay constraints while simultaneously addressing Quality of Service. The uniqueness of MQOG lies in making use of the free unlicensed bands. To consider fair effective sharing of resources I propose a Mobility Based Dynamic Transmit Opportunity (MoByToP) while modifying the 802.11e TXOP (Transmit Opportunity). The proposed protocols were implemented in OMNET++ 4.1, and extensive experiments demonstrated a faster and more efficient reception of safety messages compared to existing VANet MAC Protocols. Finally, improvements in delay, packet delivery ratios and throughput were noticed.

One of the most challenging issues facing vehicular networks lies in the design of an efficient MAC protocol due to mobile nature of nodes, delay constraints for safety applications and interference. In this thesis, I propose an efficient Multichannel QoS Cognitive MAC (MQOG). MQOG assesses the quality of channel prior to transmission employing dynamic channel allocation and negotiation algorithms to achieve significant increase in channel reliability, throughput and delay constraints while simultaneously addressing Quality of Service. The uniqueness of MQOG lies in making use of the free unlicensed bands. To consider fair effective sharing of resources I propose a Mobility Based Dynamic Transmit Opportunity (MoByToP) while modifying the 802.11e TXOP (Transmit Opportunity). The proposed protocols were implemented in OMNET++ 4.1, and extensive experiments demonstrated a faster and more efficient reception of safety messages compared to existing VANet MAC Protocols. Finally, improvements in delay, packet delivery ratios and throughput were noticed.

Intelligent transportation systems aim to provide innovative applications and services relating to traffic management and enable ease of access to information for various system users. The intent to utilize the excessive on-board resources in the transportation system, along with the latest computing resource management technology in conventional clouds, has cultivated the concept of the Vehicular Cloud. Evolved from Vehicular Networks, the vehicular cloud can be formed by vehicles autonomously, and provides a large number of applications and services that can benefit the entire transportation system, as well as drivers, passengers, and pedestrians. However, due to high traffic mobility, the vehicular cloud is built on dynamic physical resources; as a result, it experiences several inherent challenges, which increase the complexity of its implementations. Having a detailed picture of the number of vehicles, as well as their time of availability in a given region through a model, works as a critical stepping stone for enabling vehicular clouds, as well as any other system involving vehicles moving over the traffic network. The number of vehicles represents the amount of computation capabilities available in this region and the navigation time indicates the period of validity for a specific compute node. Therefore, in this thesis, we carry out a comprehensive stochastic analysis of several traffic characteristics related to the implementation of vehicular cloud inside a road segment by adopting proper traffic models. According to the analytical results, we demonstrate the feasibility of running a certain class of applications or services on the vehicular cloud, even for highly dynamic scenarios. Specifically, two kinds of traffic scenarios are modeled: free-flow traffic and queueing-up traffic. We use a macroscopic traffic model to investigate the free-flow traffic and analyze the features such as traffic density, the number of vehicles and their residence time. Also, we utilize the queueing theory to model the queueing-up traffic; the queue length and the waiting time in the queue are analyzed. The results show the boundaries on enabling vehicular cloud, allowing to determine a range of parameters for simulating vehicular clouds.

Recently, according to people's requirements for safe and congestion-free driving in the public transportation system, the intelligent transportation system (ITS) has been widely concerned. To achieve a safe and time-saving driving experience in ITS, various data sharing methods are proposed to provide traffic information for drivers to perceive their surrounding driving environment. However, the high dynamic characteristic of the vehicular network (VNET) results in a challenging environment for establishing stable communication among vehicles. To face this challenge, a Cellular network-assisted Reliable Traffic-Aware Routing protocol (CRTAR) is proposed in this thesis to provide support for vehicle’s data routing process in a heterogeneous vehicular-cellular network environment. In the method, city-wide traffic information, i.e., traffic density and data transmission density of the road segments, is introduced into vehicle's data routing process to assist the vehicle in selecting the optimal data transmission route to deliver data packets. To further improve the stability of inter-vehicle communication, the link lifetime between vehicles is also considered to select the next forwarder that can establish relatively robust communication. CRTAR takes advantage of the reliability and low-latency features of the communication technology in the cellular network and combines the cellular network with VNET to achieve real-time and reliable Vehicle-to-Infrastructure (V2I) communication. Meanwhile, it realizes the Vehicle-to-Vehicle (V2V) communication by the Dedicated Short Range Communication (DSRC) to mitigate the overload of backbone resources caused by using the cellular network. To be specific, in the method, vehicles can request city-wide traffic information via the cellular network from a cloud service that is connected to the remote data center located in the traffic management agency without latency. According to the real-time traffic information, the source vehicle can execute the data routing process with a global view of the system to calculate the data transmission route that has sufficient transmission resources to the target vehicle. The source vehicle then transmits data to the target via the vehicles in the calculated transmission route. During the forwarding process, vehicles prefer to forward the data packet to the next vehicle with a longer link lifetime. Furthermore, effective backup and recovery strategies are designed for route maintenance. The effectiveness of CRTAR is further verified by conducting simulation experiments.

No description available.

In intelligent transportation systems, the cooperation between vehicles and the road side units is essential to bring these systems to fruition. Vehicular ad hoc networks (VANETs) are a promising technology to enable the communications among vehicles on one hand and between vehicles and road side units on the other hand. However, it is a challenging task to develop a reliable routing algorithm for VANETs due to the high mobility and the frequent changes of the network topology. Communication links are highly vulnerable to disconnection in VANETs; hence, the routing reliability of these ever-changing networks needs to be paid special attention. In this paper, we propose a new vehicular reliability model to facilitate the reliable routing in VANETs. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified time period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road. We extend the well-known ad hoc on-demand distance vector (AODV) routing protocol to propose our reliable routing protocol AODV-R. Simulation results demonstrate that AODV-R outperforms significantly the AODV routing protocol in terms of better delivery ratio and less link failures while maintaining a reasonable routing control overhead.

No description available.

Vehicular ad hoc Networks (VANETs) allow vehicles to form a self-organized network. VANETs are likely to be widely deployed in the future, given the interest shown by industry in self-driving cars and satisfying their customers various interests. Problems related to Mobile ad hoc Networks (MANETs) such as routing, security, etc.have been extensively studied. Even though VANETs are special type of MANETs, solutions proposed for MANETs cannot be directly applied to VANETs because all problems related to MANETs have been studied for small networks. Moreover, in MANETs, nodes can move randomly. On the other hand, movement of nodes in VANETs are constrained to roads and the number of nodes in VANETs is large and covers typically large area. The following are the contributions of the thesis. Secure, authenticated, privacy preserving message dissemination in VANETs: When vehicles in VANET observe phenomena such as accidents, icy road condition, etc., they need to disseminate this information to vehicles in appropriate areas so the drivers of those vehicles can take appropriate action. When such messages are disseminated, the authenticity of the vehicles disseminating such messages should be verified while at the same time the anonymity of the vehicles should be preserved. Moreover, to punish the vehicles spreading malicious messages, authorities should be able to trace such messages to their senders when necessary. For this, we present an efficient protocol for the dissemination of authenticated messages. Incentive-based architecture for vehicular cloud: Due to the advantages such as exibility and availability, interest in cloud computing has gained lot of attention in recent years. Allowing vehicles in VANETs to store the collected information in the cloud would facilitate other vehicles to retrieve this information when they need. In this thesis, we present a secure incentive-based architecture for vehicular cloud. Our architecture allows vehicles to collect and store information in the cloud; it also provides a mechanism for rewarding vehicles that contributing to the cloud. Privacy preserving message dissemination in VANETs: Sometimes, it is sufficient to ensure the anonymity of the vehicles disseminating messages in VANETs. We present a privacy preserving message dissemination protocol for VANETs.

Les technologies Big Data gagnent de plus en plus d’attentions de communautés de recherches variées, surtout depuis que les données deviennent si volumineuses, qu’elles posent de réels problèmes, et que leurs traitements ne sont maintenant possibles que grâce aux grandes capacités de calculs des équipements actuels. De plus, les réseaux véhiculaires, aussi appelés VANET pour Vehicular Ad-hoc Networks, se développent considérablement et ils constituent une part de plus en plus importante du marché du véhicule. La topologie de ces réseaux en constante évolution est accompagnée par des données massives venant d’un volume croissant de véhicules connectés.Dans cette thèse, nous discutons dans notre première contribution des problèmes engendrés par la croissance rapide des VANET, et nous étudions l’adaptation des technologies liées aux Big Data pour les VANET. Ainsi, pour chaque étape clé du Big Data, nous posons le problème des VANET.Notre seconde contribution est l’extraction des caractéristiques liées aux VANET afin d’obtenir des données provenant de ceux-ci. Pour ce faire, nous discutons de comment établir des scénarios de tests, et comment émuler un environnement afin, dans un premier temps, de tester une implémentation dans un environnement contrôlé, avant de pouvoir effectuer des tests dans un environnement réel, afin d’obtenir de vraies données provenant des VANET.Pour notre troisième contribution, nous proposons une approche originale de la modélisation du comportement de conducteur. Cette approche est basée sur un algorithme permettant d’extraire des représentants d’une population, appelés exemplaires, en utilisant un concept de densité locale dans un voisinage. / Big Data is gaining lots of attentions from various research communities as massive data are becoming real issues and processing such data is now possible thanks to available high-computation capacity of today’s equipment. In the meanwhile, it is also the beginning of Vehicular Ad-hoc Networks (VANET) era. Connected vehicles are being manufactured and will become an important part of vehicle market. Topology in this type of network is in constant evolution accompanied by massive data coming from increasing volume of connected vehicles in the network.In this thesis, we handle this interesting topic by providing our first contribution on discussing different aspects of Big Data in VANET. Thus, for each key step of Big Data, we raise VANET issues.The second contribution is the extraction of VANET characteristics in order to collect data. To do that, we discuss how to establish tests scenarios, and to how emulate an environment for these tests. First we conduct an implementation in a controlled environment, before performing tests on real environment in order to obtain real VANET data.For the third contribution, we propose an original approach for driver's behavior modeling. This approach is based on an algorithm permitting extraction of representatives population, called samples, using a local density in a neighborhood concept.