Using Neighborhood Beyond One Hop in Disruption-Tolerant Networks

Most disruption-tolerant networking (DTN) protocols available in the literature have focused on mere contact and intercontact characteristics to make forwarding decisions. Nevertheless, there is a world behind contacts: just because one node is not in contact with some potential destination, it does not mean that this node is alone. There may be interesting end-to-end transmission opportunities through other nearby nodes. Existing protocols miss such possibilities by maintaining a simple contact-based view of the network. In this paper, we investigate how the vicinity of a node evolves through time and whether such information can be useful when routing data. We observe a clear tradeoff between routing performance and the cost for monitoring the neighborhood. Our analyses suggest that limiting a node's neighborhood view to three or four hops is more than enough to significantly improve forwarding efficiency without incurring prohibitive overhead.Comment: 5 pages, 5 figures, 1 tabl

Propriétés et impact du voisinage dans les réseaux mobiles opportunistes

Les réseaux opportunistes (DTN) permettent d'utiliser de nouveaux vecteurs de transmissions. Avant de pouvoir profiter de toutes les capacités des DTN, nous devons nous pencher sur la compréhension de ce nouveau paradigme. De nombreuses propriétés des réseaux DTN sont maintenant reconnues, cependant les relations entre un noeud du réseau et son voisinage proche ne semblent pas encore avoir été passée au crible. Souvent, la présence de noeuds voisins proches mais pas directement lié par le contact est ignorée. Dans cette thèse, nous montrons à quel point considérer les noeuds à proximité nous aide à améliorer les performances DTNs.En identifiant le paradoxe binaire dans les DTN, nous montrons que les caractérisations actuelles ne sont pas suffisantes pour bénéficier de toutes les possibilités de transmission dans les DTN. Nous proposons une définition formelle du voisinage pour les DTNs avec le k-vicinity''. Nous étudions les caractérisations temporelles du k-vicinity avec différentes données. Ensuite, nous nous concentrons sur l'étude de l'organisation interne du k-vicinity. Nous avons crée le Vicinity Motion qui permet d'obtenir un modèle markovien à partir de n'importe quelle trace de contact. Nous en extrayions trois mouvements principaux: la naissance, la mort et les mouvements séquentiels. Grâce aux valeurs du Vicinity Motion, nous avons pu créer un générateur synthétique de mouvements de proximité nommé TiGeR. Enfin, nous posons la question de la prévisibilité des distances entre deux noeuds du k-vicinity. En utilisant le savoir emmagasiné dans le Vicinity Motion, nous mettons au point une heuristique permettant de prédire les futures distances entre deux noeuds.The networking paradigm uses new information vectors consisting of human carried devices is known as disruption-tolerant networks (DTN) or opportunistic networks. We identify the binary assertion issue in DTN. We notice how most DTNs mainly analyze nodes that are in contact. So all nodes that are not in contact are in intercontact. Nevertheless, when two nodes are not in contact, this does not mean that they are topologically far away from one another. We propose a formal definition of vicinities in DTNs and study the new resulting contact/intercontact temporal characterization. Then, we examine the internal organization of vicinities using the Vicinity Motion framework. We highlight movement types such as birth, death, and sequential moves. We analyze a number of their characteristics and extract vicinity usage directions for mobile networks. Based on the vicinity motion outputs and extracted directions, we build the TiGeR that simulates how pairs of nodes interact within their vicinities. Finally, we inquire about the possibilities of vicinity movement prediction in opportunistic networks. We expose a Vicinity Motion-based heuristic for pairwise shortest distance forecasting. We use two Vicinity Motion variants called AVM and SVM to collect vicinity information. We find that both heuristics perform quite well with performances up to 99% for SVM and around 40% for AVM.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF

Caractérisation en diptyque de l'intercontact pour les réseaux à connectivité intermittente

International audienceLa plupart des études sur les réseaux à connectivité intermittente se basent sur une notion duale de contact et d'intercontact: deux noeuds sont dit en contact lorsqu'ils sont à portée mutuelle sinon, ils sont en intercontact. Cette définition binaire de l'intercontact n'offre pas assez de précision et ne permet pas de refléter l'immense variété des situations existantes lorsque deux noeuds ne sont pas en contact. Dans notre étude, nous montrons les faiblesses d'une caractérisation binaire de l'intercontact et définissons l'intercontact n-aire afin de combler ses défauts. Nous montrons les atouts d'une telle vision et défendons son adoption pour la conception de protocoles opportunistes

The Strength of Vicinity Annexation in Opportunistic Networking

International audienceMost disruption-tolerant networking protocols available have focused on mere contact and intercontact characteristics to make forwarding decisions. We propose to relax such a simplistic approach and include multi-hop opportunities by annexing a node's vicinity to its network vision. We investigate how the vicinity of a node evolves through time and whether such information is useful when routing data. By analyzing a modified version of the pure WAIT forwarding strategy, we observe a clear tradeoff between routing performance and cost for monitoring the neighborhood. By observing a vicinity-aware WAIT strategy, we emphasize how the pure WAIT misses interesting end-to-end transmission opportunities through nearby nodes. Our analyses also suggest that limiting a node's neighborhood view to four hops is enough to improve forwarding efficiency while keeping control overhead low

Fine-Grained Intercontact Characterization in Disruption-Tolerant Networks

International audienceSo far, efforts attempting to characterize the spa- tiotemporal nature of disruption-tolerant networks (DTN) have relied on the dual notion of contacts and intercontacts. A contact happens when two nodes are within communication range of each other. An intercontact is simply defined as the dual of a contact, i.e., when two nodes are not in communication range of each other. We refer to this model as "binary". Although the binary characterization allows understanding the main interac- tion properties of the network, it is not sufficient to capture a plethora of situations beyond the binary hypothesis. In this paper, we investigate the structural properties of the network when nodes are not in contact but do have a contemporaneous path connecting them. We first introduce the notion of n- ary intercontact and, to defend its adoption, we quantify the proportion of nodes bearing this new intercontact notion in well-known datasets available to the community. Surprisingly, we observe that most pairs of nodes are nearby (within a few hops) for significant amounts of time when not directly in contact. Finally, we compare the impact of our proposal with the classic intercontact definition and give incentives toward using the n-ary characterization to leverage new communication opportunities

Vicinity-based DTN Characterization

International audienceWe relax the traditional definition of contact and intercontact times by bringing the notion of vicinity into the game. We propose to analyze disruption-tolerant networks (DTN) under the assumption that nodes are in k-contact when they remain within a few hops from each other and in k-intercontact otherwise (where k is the maximum number of hops characterizing the vicinity). We make interesting observations when analyzing several real-world and synthetic mobility traces. We detect a number of unexpected behaviors when analyzing k-contact distributions; in particular, we observe that in some datasets the average k-contact time decreases as we increase k. In fact, we observe that many nodes spend a non-negligible amount of time in each other's vicinity without coming into direct contact. We also show that a small k (typically between 3 and 4) is sufficient to capture most communication opportunities

Examining Vicinity Dynamics in Opportunistic Networks

Poster at ACM MSWiM 2013International audienceModeling the dynamics of opportunistic networks generally relies on the dual notion of contacts and intercontacts between nodes. We advocate the use of an extended view in which nodes track their vicinity (within a few hops) and not only their direct neighbors. Contrary to existing approaches in the literature in which contact patterns are derived from the spatial mobility of nodes, we directly address the topological properties avoiding any intermediate steps. To the best of our knowledge, this paper presents the first study to ever focus on vicinity motion. We apply our method to several real-world and synthetic datasets to extract interesting patterns of vicinity. We provide an original workflow and intuitive modeling to understand a node's surroundings. Then, we highlight two main vicinity chains behaviors representing all the datasets we observed. Finally, we identify three main types of movements (birth, death, and sequential). These patterns represent up to 87% of all observed vicinity movements

Propriétés et impact du voisinage dans les réseaux mobiles opportunistes

The networking paradigm uses new information vectors consisting of human carried devices is known as disruption-tolerant networks (DTN) or opportunistic networks. We identify the binary assertion issue in DTN. We notice how most DTNs mainly analyze nodes that are in contact. So all nodes that are not in contact are in intercontact. Nevertheless, when two nodes are not in contact, this does not mean that they are topologically far away from one another. We propose a formal definition of vicinities in DTNs and study the new resulting contact/intercontact temporal characterization. Then, we examine the internal organization of vicinities using the Vicinity Motion framework. We highlight movement types such as birth, death, and sequential moves. We analyze a number of their characteristics and extract vicinity usage directions for mobile networks. Based on the vicinity motion outputs and extracted directions, we build the TiGeR that simulates how pairs of nodes interact within their vicinities. Finally, we inquire about the possibilities of vicinity movement prediction in opportunistic networks. We expose a Vicinity Motion-based heuristic for pairwise shortest distance forecasting. We use two Vicinity Motion variants called AVM and SVM to collect vicinity information. We find that both heuristics perform quite well with performances up to 99% for SVM and around 40% for AVM.Les réseaux opportunistes (DTN) permettent d'utiliser de nouveaux vecteurs de transmissions. Avant de pouvoir profiter de toutes les capacités des DTN, nous devons nous pencher sur la compréhension de ce nouveau paradigme. De nombreuses propriétés des réseaux DTN sont maintenant reconnues, cependant les relations entre un noeud du réseau et son voisinage proche ne semblent pas encore avoir été passée au crible. Souvent, la présence de noeuds voisins proches mais pas directement lié par le contact est ignorée. Dans cette thèse, nous montrons à quel point considérer les noeuds à proximité nous aide à améliorer les performances DTNs.En identifiant le paradoxe binaire dans les DTN, nous montrons que les caractérisations actuelles ne sont pas suffisantes pour bénéficier de toutes les possibilités de transmission dans les DTN. Nous proposons une définition formelle du voisinage pour les DTNs avec le ``k-vicinity''. Nous étudions les caractérisations temporelles du k-vicinity avec différentes données. Ensuite, nous nous concentrons sur l'étude de l'organisation interne du k-vicinity. Nous avons crée le Vicinity Motion qui permet d'obtenir un modèle markovien à partir de n'importe quelle trace de contact. Nous en extrayions trois mouvements principaux: la naissance, la mort et les mouvements séquentiels. Grâce aux valeurs du Vicinity Motion, nous avons pu créer un générateur synthétique de mouvements de proximité nommé TiGeR. Enfin, nous posons la question de la prévisibilité des distances entre deux noeuds du k-vicinity. En utilisant le savoir emmagasiné dans le Vicinity Motion, nous mettons au point une heuristique permettant de prédire les futures distances entre deux noeuds

Uncovering vicinity properties in disruption-tolerant networks

International audienceMost existing proposals in the area of disruption-tolerant networking rely on the binaryassertion that when two nodes are not in contact, they are necessarily in intercontact. Sucha monolithic notion is, in our opinion, too limitative. In this paper, we advocate the use of theneighborhood of a node beyond one hop to help design more efficient communication solutions.We provide a formal definition of j-vicinity and associated measures, namely j-contactand j-intercontact. These measures allow better understanding the proximity betweennodes as they are not restrained solely to the direct contact situation.Weobserve unexpectedbehaviors in j-contact distributions and point out their dependency on node density. Wealso observe that a significant share of pairs of nodes spend a non-negligible amount of timein each other’s vicinity without coming into direct contact.We showthen that using a small j(between 2 and 4) is enough to capture a significant amount of communication possibilitiesthat are neglected by existing approaches. Finally, we provide a rule of thumb to derive thepopulation in the j-vicinity by observing only the direct contacts of a node