A Comparative Survey of VANET Clustering Techniques

© 2016 Crown. A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles - most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming - the lack of realistic vehicular channel modeling - is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated

Infrastructure-less D2D Communications through Opportunistic Networks

Mención Internacional en el título de doctorIn recent years, we have experienced several social media blackouts, which have shown how much our daily experiences depend on high-quality communication services. Blackouts have occurred because of technical problems, natural disasters, hacker attacks or even due to deliberate censorship actions undertaken by governments. In all cases, the spontaneous reaction of people consisted in finding alternative channels and media so as to reach out to their contacts and partake their experiences. Thus, it has clearly emerged that infrastructured networks—and cellular networks in particular—are well engineered and have been extremely successful so far, although other paradigms should be explored to connect people. The most promising of today’s alternative paradigms is Device-to-Device (D2D) because it allows for building networks almost freely, and because 5G standards are (for the first time) seriously addressing the possibility of using D2D communications. In this dissertation I look at opportunistic D2D networking, possibly operating in an infrastructure-less environment, and I investigate several schemes through modeling and simulation, deriving metrics that characterize their performance. In particular, I consider variations of the Floating Content (FC) paradigm, that was previously proposed in the technical literature. Using FC, it is possible to probabilistically store information over a given restricted local area of interest, by opportunistically spreading it to mobile users while in the area. In more detail, a piece of information which is injected in the area by delivering it to one or more of the mobile users, is opportunistically exchanged among mobile users whenever they come in proximity of one another, progressively reaching most (ideally all) users in the area and thus making the information dwell in the area of interest, like in a sort of distributed storage. While previous works on FC almost exclusively concentrated on the communication component, in this dissertation I look at the storage and computing components of FC, as well as its capability of transferring information from one area of interest to another. I first present background work, including a brief review of my Master Thesis activity, devoted to the design, implementation and validation of a smartphone opportunistic information sharing application. The goal of the app was to collect experimental data that permitted a detailed analysis of the occurring events, and a careful assessment of the performance of opportunistic information sharing services. Through experiments, I showed that many key assumptions commonly adopted in analytical and simulation works do not hold with current technologies. I also showed that the high density of devices and the enforcement of long transmission ranges for links at the edge might counter-intuitively impair performance. The insight obtained during my Master Thesis work was extremely useful to devise smart operating procedures for the opportunistic D2D communications considered in this dissertation. In the core of this dissertation, initially I propose and study a set of schemes to explore and combine different information dissemination paradigms along with real users mobility and predictions focused on the smart diffusion of content over disjoint areas of interest. To analyze the viability of such schemes, I have implemented a Python simulator to evaluate the average availability and lifetime of a piece of information, as well as storage usage and network utilization metrics. Comparing the performance of these predictive schemes with state-of-the-art approaches, results demonstrate the need for smart usage of communication opportunities and storage. The proposed algorithms allow for an important reduction in network activity by decreasing the number of data exchanges by up to 92%, requiring the use of up to 50% less of on-device storage, while guaranteeing the dissemination of information with performance similar to legacy epidemic dissemination protocols. In a second step, I have worked on the analysis of the storage capacity of probabilistic distributed storage systems, developing a simple yet powerful information theoretical analysis based on a mean field model of opportunistic information exchange. I have also extended the previous simulator to compare the numerical results generated by the analytical model to the predictions of realistic simulations under different setups, showing in this way the accuracy of the analytical approach, and characterizing the properties of the system storage capacity. I conclude from analysis and simulated results that when the density of contents seeded in a floating system is larger than the maximum amount which can be sustained by the system in steady state, the mean content availability decreases, and the stored information saturates due to the effects of resource contention. With the presence of static nodes, in a system with infinite host memory and at the mean field limit, there is no upper bound to the amount of injected contents which a floating system can sustain. However, as with no static nodes, by increasing the injected information, the amount of stored information eventually reaches a saturation value which corresponds to the injected information at which the mean amount of time spent exchanging content during a contact is equal to the mean duration of a contact. As a final step of my dissertation, I have also explored by simulation the computing and learning capabilities of an infrastructure-less opportunistic communication, storage and computing system, considering an environment that hosts a distributed Machine Learning (ML) paradigm that uses observations collected in the area over which the FC system operates to infer properties of the area. Results show that the ML system can operate in two regimes, depending on the load of the FC scheme. At low FC load, the ML system in each node operates on observations collected by all users and opportunistically shared among nodes. At high FC load, especially when the data to be opportunistically exchanged becomes too large to be transmitted during the average contact time between nodes, the ML system can only exploit the observations endogenous to each user, which are much less numerous. As a result, I conclude that such setups are adequate to support general instances of distributed ML algorithms with continuous learning, only under the condition of low to medium loads of the FC system. While the load of the FC system induces a sort of phase transition on the ML system performance, the effect of computing load is more progressive. When the computing capacity is not sufficient to train all observations, some will be skipped, and performance progressively declines. In summary, with respect to traditional studies of the FC opportunistic information diffusion paradigm, which only look at the communication component over one area of interest, I have considered three types of extensions by looking at the performance of FC: over several disjoint areas of interest; in terms of information storage capacity; in terms of computing capacity that supports distributed learning. The three topics are treated respectively in Chapters 3 to 5.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Claudio Ettori Casetti.- Secretario: Antonio de la Oliva Delgado.- Vocal: Christoph Somme

La dissémination de contenus dans les réseaux véhiculaires

Les réseaux véhiculaires constituent une catégorie de réseaux sans fil mobiles à part entière et présentent l'originalité de permettre aux véhicules de communiquer les uns avec les autres mais aussi avec l'infrastructure quand elle existe. L'apparition des réseaux véhiculaires s'est accompagnée de l'apparition d'une myriade et variété d'applications potentielles allant de la sécurité à la gestion du trafic routier en passant par les applications de divertissement et de confort des usagers de la route. Ces applications ont suscité beaucoup d'intérêt de la part des chercheurs, des constructeurs des automobiles et des opérateurs des télécommunications. Les applications d'information et de divertissement, pour lesquelles une grande quantité de contenus peut exister, exigent que les contenus engendrés soient propagés au travers des véhicules et/ou de l'infrastructure jusqu'à atteindre les utilisateurs intéressés tout en respectant les durées de vie potentiellement limitées des contenus. La dissémination de contenus pour ce type d'applications reste un défi majeur en raison de plusieurs facteurs tels que la présence de beaucoup de contenus, la connectivité très intermittente mais encore les intérêts potentiellement hétérogènes des utilisateurs. C'est à cette thématique que nous sommes intéressés dans cette thèse; Tout d'abord, nous nous proposons une nouvelle métrique qui calcule l'utilité apportée aux utilisateurs. Elle permet de mesurer leur satisfaction par rapport aux contenus reçus. Nous la jugeons nécessaire pour évaluer les performances d'une approche de dissémination pour les applications de confort par opposition à des applications de sécurité routière. Dans un deuxième temps, nous nous concentrons sur le développement d'un nouveau protocole de dissémination, appelé I-PICK, et d'une solution de sélection des nœuds relais, appelé I-SEND, pour disséminer les contenus d'information et de divertissement en tenant compte des préférences des utilisateurs par rapport aux contenus reçus. Notre proposition est fondée sur l'échange de messages périodiques permettant l'estimation des durées de contacts et la connaissance des préférences des utilisateurs. Ces informations sont ensuite utilisées, dans un premier temps, pour effectuer un ordonnancement efficace des contenus lors de la dissémination puis choisir les relais permettant de maximiser l'utilité des utilisateurs par rapport aux contenus reçus dans un environnement caractérisé par des faibles durées de communication. Au travers de simulations nous confirmons l'efficacité de notre approche. Pour conforter le fonctionnement de nos mécanismes, nous avons implanté dans un environnement réel nos propositions I-PICK et I-SEND. Au travers d'un scénario simple, nous avons mis en évidence des risques liés à l'hétérogénéité des machines ou bien encore la difficulté du paramétrage des temporisations. Ces premiers résultats positifs montrent l'intérêt de notre technique et ouvre des pistes d'amélioration. Notre dernière contribution concerne des mécanismes de réduction du trafic cellulaire à l'aide des communications opportunistes entre les véhicules. Quand un contenu est disponible auprès d'un serveur de contenus accessible par le réseau cellulaire, il est nécessaire de proposer une méthode efficace de sélection des sources initiales qui seront choisies pour télécharger puis disséminer les contenus. Nous optons pour une solution qui pourrait reposer sur technologie SDN ainsi que sur des communications opportunistes et qui permet de choisir les sources en tant que nœuds pouvant produire un maximum d'utilité en propageant les contenus

Multi-directional Warning Message Dissemination Protocol Based on Motion Vector Clustering for VANETs

Problem. Most broadcast suppression protocols in vehicular ad hoc networks (VANET) mainly focus on one-dimensional message dissemination model for both highway and urban scenarios. Due to the non-line-of-sight (NLOS) problem occuring frequently in urban scenario, protocols mostly rely on either infrastructure or the vehicle that is passing through the intersection to forward the message in multiple directions manner. However, these one-dimensional message dissemination models fail to take into account realistic road topologies and traffic distribution. As a result, they tend to miss some possible dissemination directions. Method. Vehicles travelling on the same road share similar motion pattern due to the constraint of road topology. Each motion pattern represents a road topology as well as a potential dissemination direction. By identifying motion pattern of one-hop neighbors, the proposed motion vector protocol (MVP) enables a vehicle not only to identify potential dissemination directions without the support from infrastructure or a road map but also to make suppression decisions without any additional information from periodic beacons. Results. The total number of transmissions for simple flooding (each node broadcasts once) compared with MVP ranges respectively as follows: 90.2-269.7 and 40.6-72.3. Also, the number of saved rebroadcasts for simple flooding compared with MVP ranges respectively as follows: 0%-0% and 57%-73%. In the case of reachability, the simple flooding compared with MVP ranges 100%-100% and 100%-100% respectively. Finally, the average latency of the entire dissemination for simple flooding and MVP ranges 0.01446-0.01286s and 0.1127-0.1565s respectively. Conclusions. The experimental results show that MVP achieves high reachability, while still significantly reducing rebroadcast redundancy. One distinctive feature of MVP is that it is capable of operating on complex road topology such as a roundabout, curve road, branch road, etc., with multi-directional traffic in it

Towards efficacy and efficiency in sparse delay tolerant networks

The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people\u27s movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the ChitChat system is hybridized [70] to consider both social context and geographic information for learning the social semantics of locations so as to identify worthwhile routing opportunities to destinations and areas of interest. Network density analyses of five real-world datasets is conducted to identify sparse datasets on which to conduct simulations, finding that commonly-used datasets in past DTN research are notably dense and well connected, and suggests two rarely used datasets are appropriate for research into sparse DTNs. Finally, the Catora system is proposed to address congestive-driven degradation of service in DTNs by accomplishing two simultaneous tasks: (i) expedite the delivery of higher quality messages by uniquely ordering messages for transfer and delivery, and (ii) avoid congestion through strategic buffer management and message removal. Through dataset-driven simulations, these systems are found to outperform the state-of-the-art, with ChitChat facilitating delivery in sparse DTNs and Catora unencumbered by congestive conditions --Abstract, page iv

Achieving dynamic road traffic management by distributed risk estimation in vehicular networks

In this thesis I develop a model for a dynamic and fine-grained approach to traffic management based around the concept of a risk limit: an acceptable or allowable level of accident risk which vehicles must not exceed. Using a vehicular network to exchange risk data, vehicles calculate their current level of accident risk and determine their behaviour in a distributed fashion in order to meet this limit. I conduct experimental investigations to determine the effectiveness of this model, showing that it is possible to achieve gains in road system utility in terms of average vehicle speed and overall throughput whilst maintaining the accident rate. I also extend this model to include risk-aware link choice and social link choice, in which vehicles make routing decisions based on both their own utility and the utility of following vehicles. I develop a coupled risk estimation algorithm in which vehicles use not only their own risk calculations but also estimates received from neighbouring vehicles in order to arrive at a final risk value. I then analyse the performance of this algorithm in terms of its convergence rate and bandwidth usage and examine how to manage the particular characteristics of a vehicular ad-hoc network, such as its dynamic topology and high node mobility. I then implement a variable-rate beaconing scheme to provide a trade-off between risk estimate error and network resource usage

Resources Efficient Dynamic Clustering Algorithm for Flying Ad-Hoc Network

Unmanned Aerial Vehicles, often known as UAVs, are connected in the form of a Flying Ad-hoc Network, or FANET, and placed to use in a variety of applications to carry out efficient remote monitoring. Their great mobility has an adverse effect on their energy consumption, which in turn has a detrimental effect on the network's stability and the effectiveness of communication. To manage the very dynamic flying behavior of UAVs and to keep the network stable, novel clustering algorithms have been implemented. In this context, a novel clustering technique is developed to meet the rapid mobility of UAVs and to offer safe inter-UAV distance, reliable communication, and an extended network lifespan. It also provides a detailed analysis of the similarities and distinctions between AODV, AOMDV, DSDV, and DumbAgent.The performance of these protocols is analyzed using the NS-2 simulator. The simulation results are shown in our study with AODV, AOMDV, DSDV, and DumbAgent. The results of the simulation make it abundantly evident that the AODV routing protocol outperforms the other routing protocols DSDV, AOMDV, and DumbAgent in terms of the number of packets lost, the amount of throughput achieved, the amount of routing overhead generated, and the amount of delay

Business cases, potential new functions and technology implementation plan

D5.3.1: Business cases, and potential new functions has been renamed within D5.3.1 as follows: ‘Business cases, and potential new functions and Technology Implementation Plan’ - to include also the Technology Implementation Plan, planned in the DoW as the main outcome from T5.3.1 Exploitable Result Cases. This Deliverable includes references to the main activities to be performed in WP 5.3 Facilitation of Exploitation and has strong links to other Work packages and Sub Projects activities

Mobility-aware mechanisms for fog node discovery and selection

The recent development of delay-sensitive applications has led to the emergence of the fog computing paradigm. Within this paradigm, computation nodes present at the edge of the network can act as fog nodes (FNs) capable of processing users' tasks, thus resulting in latency reductions compared to the existing cloud-based execution model. In order to realize the full potential of fog computing, new research questions have arised, mainly due to the dynamic and heterogeneous fog computing context. This thesis focuses on the following questions in particular: How can a user detect the presence of a nearby FN? How should a user on the move adapt its FN discovery strategy, according to its changing context? How should an FN be selected , in the case of user mobility and FN mobility? These questions will be addressed throughout the different contributions of this thesis. The first contribution consists in proposing a discovery solution allowing a user to become aware of the existence of a nearby FN. Using our solution, the FN advertizes its presence using custom WiFi beacons, which will be detected by the user via a scan process. An implementation of this approach has been developed and its evaluation results have shown that it results in a non-negligible energy consumption given its use of WiFi. This has led to our second contribution, which aims at improving the WiFi scan performed in our discovery approach, especially in the case of user mobility. At a first stage, this improvement consisted in embedding information about the topology of the FNs in the beacons the user receives from previous FNs. We have shown that by adapting the scan behavior based on this information, considerable energy savings can be achieved, while guaranteeing a high discovery rate. However, as this approach is associated with a restrictive FN topology structure, we proposed a different alternative, at a second stage. This alternative leverages the history of cellular context information as an indicator allowing the user to infer whether an FN may be present in its current location. If so, the scan will be enabled. Otherwise, it is disabled. The simulation results comparing different classification algorithms have shown that a sequence-based model, such as a hidden-Markov model is able to effectively predict the FN presence in the current user location. While the previous approaches have focused on a sparse FN deployment, our third contribution considers a high density of FNs. Consequently, as there are multiple nearby FNs that can process the user's tasks, it is important to derive a suitable FN selection strategy. This strategy should consider the time-varying set of FNs caused by the user's mobility. Besides, it should minimize the number of switches from one FN to another, in order to maintain a good quality of service. With these considerations in mind, we have shown that an adaptive greedy approach, that selects an FN having a good-enough delay estimate, achieves the best results. Finally, unlike the previous contribution, where the focus has been on FN selection when the user is mobile, our final contribution deals with mobile vehicular FNs (VFNs). Given the mobility of such VFNs, it is important to make the most of their resources, since they are only available for a short time at a given area. So, we propose that, in order to select an appropriate VFN for a given task, a reference roadside unit (RSU) responsible for task assignment can use advice from a neighbor RSU. This advice consists in the VFN that will result in the lowest delay for the current task, based on the experience of the neighbor RSU. The results have shown that, using the provided advice, the reference RSU can observe significant delay reductions. All in all, the proposed contributions have addressed various problems that may arise in a fog computing context and the obtained results can be used to guide the development of the building blocks of future fog computing solutions.El recent desenvolupament d'aplicacions IoT ha comportat l'aparició del paradigma de fog computing. Dins d'aquest paradigma, els nodes de càlcul presents a la vora de la xarxa poden actuar com a “fog nodes'' (FN) capaços de processar les tasques dels usuaris, produint així reduccions de latència en comparació amb el model d'execució basat en núvol. Per assolir tot el potencial del fog computing, han sorgit noves qüestions de recerca, principalment a causa del context dinàmic i heterogeni de fog computing. Aquesta tesi se centra especialment en les qüestions següents: Com pot un usuari detectar la presència d'un FN? Com hauria d’adaptar un usuari en moviment la seva estratègia de descobriment de FN, segons el seu context? Com s’ha de seleccionar un FN, en el cas de la mobilitat dels usuaris i la mobilitat FN? Aquestes preguntes s’abordaran al llarg de les diferents aportacions d’aquesta tesi. La primera contribució consisteix a proposar una solució de descobriment que permeti a l'usuari detectar l’existència d’un FN proper. Mitjançant la nostra solució, un FN anuncia la seva presència mitjançant beacons Wi-Fi personalitzats, que seran detectats per l'usuari mitjançant un procés d’exploració. S'ha desenvolupat una implementació d'aquest enfocament i els seus resultats d’avaluació han demostrat que resulta en un consum d'energia menyspreable donat el seu ús del Wi-Fi. Això ha suposat la nostra segona contribució, que té com a objectiu millorar l’exploració Wi-Fi, especialment en el cas de la mobilitat dels usuaris. En una primera fase, aquesta millora va consistir a incorporar informació sobre la topologia dels FN en les beacons que rep l'usuari dels FN anteriors. Hem demostrat que mitjançant l'adaptació del comportament d'escaneig basat en aquesta informació es pot aconseguir un estalvi considerable d’energia, alhora que es garanteix un índex elevat de descobriment. Tanmateix, com aquest enfocament s'associa a una estructura de topologia FN restrictiva, vam proposar una alternativa diferent, en una segona etapa. Aquesta alternativa aprofita la història de la informació del context cel·lular com a indicador que permet a l'usuari deduir si un FN pot estar present en la seva ubicació. En cas afirmatiu, l'exploració estarà habilitada. Els resultats de la simulació comparant diferents algoritmes de classificació han demostrat que un model basat en seqüències, com un model HMM, és capaç de predir eficaçment la presència de FNs a la ubicació actual de l'usuari. Si bé els enfocaments anteriors s’han centrat en un desplegament escàs de FNs, la nostra tercera contribució considera una alta densitat d'FNs. En conseqüència, com que hi ha múltiples FNs propers que poden processar les tasques de l'usuari, és important derivar una estratègia de selecció de FN adequada. Aquesta estratègia hauria de tenir en compte el conjunt variable de temps causat per la mobilitat de l'usuari. A més, hauria de minimitzar el nombre de canvis d'un FN a un altre, per mantenir una bona qualitat del servei. Tenint en compte aquestes consideracions, hem demostrat que un enfocament codiciós adaptatiu, que selecciona un FN amb una estimació de retard suficient, aconsegueix els millors resultats. Finalment, a diferència de l'aportació anterior, on l'atenció s'ha fixat en la selecció d'FN quan l'usuari és mòbil, la nostra contribució final tracta sobre les FNs per a vehicles mòbils (VFNs). Tenint en compte la mobilitat d’aquests VFNs, és important aprofitar al màxim els seus recursos, ja que només estan disponibles per a un temps curt. Així doncs, proposem que, per seleccionar un VFN adequat per a una tasca, una unitat RSU responsable de l'assignació de tasques pot utilitzar consells d'un RSU veí. Aquest consell consisteix en escollir el VFN que suposarà el menor retard de la tasca actual, en funció de l’experiència del RSU veí. Els resultats han demostrat que ..