Intercontact times in opportunistic networks and their impact on forwarding convergence

The increasing popularity of some new mobile technologies (smartphones for example) has opened new interesting scenarios in communications because of the possibility of a device to communicate with another one without using the wireless (or wired) network interfaces but taking advantages of the mobility of all the devices. In this direction, one of the most important evolution of Mobile ad hoc networks are opportunistic networks, that are self-organizing networks where there are not any guarantee of two devices to be linked with complete multi-hop path in any time. What a node has to do to deliver a certain message, is to nd a space-time multi-hop path, that is portions of path that can carry on the message during the time until it reaches the destination. We can see an example in Figure 1: the source S has to deliver a message to the destination D; the message can arrive at D at time t3, even if in [t1,t3] S and D are not directly linked. As nodes do not have any knowledges of the network topology, but only of the destination the massage have to arrive to, this way of delivering needs at any time to make some decisions, that are to whom has to be sent message and how many copies has to be sent

Following the Right Path: Using Traces for the Study of DTNs

Contact traces collected in real situations represent a popular material for the study of a Delay Tolerant Network. Three main use cases can be defined for traces: social analysis, performance evaluation and statistical analysis. In this paper, we perform a review on the technicalities of real trace collection and processing. First, we identify several factors which can influence traces during collection, filtering or scaling, and illustrate their impact on the conclusions, based on our experience with four datasets from the literature. We subsequently propose a list of criteria to be verified each time a trace is to be used, along with recommendations on which filters to apply depending on the envisioned use case. The rationale is to provide guidelines for researchers needing to perform trace analysis in their studies

Residual Inter-Contact Time for Opportunistic Networks with Pareto Inter-Contact Time: Two Nodes Case

PDPTA'15 : The 21st International Conference on Parallel and Distributed Processing Techniques and Applications , Jul 27-30, 2015 , Las Vegas, NV, USAOpportunistic networks (OppNets) are appealing for many applications, such as wild life monitoring, disaster relief and mobile data offloading. In such a network, a message arriving at a mobile node could be transmitted to another mobile node when they opportunistically move into each other's transmission range (called in contact), and after multi-hop similar transmissions the message will finally reach its destination. Therefore, for one message the time interval from its arrival at a mobile node to the time the mobile node contacts another node constitutes an essential part of the message's whole delay. Thus, studying stochastic properties of this time interval between two nodes lays a solid foundation for evaluating the whole message delay in OppNets. Note that this time interval is within the time interval between two consecutive node contacts (called inter-contact time) and it is also referred to as residual inter-contact time. In this paper, we derive the closed-form distribution for residual inter-contact time. First, we formulate the contact process of a pair of mobile nodes as a renewal process, where the inter-contact time features the popular Pareto distribution. Then, we derive, based on the renewal theory, closed-form results for the transient distribution of residual inter-contact time and also its limiting distribution. Our theoretical results on distribution of residual inter-contact time are validated by simulations

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

SPoT: Representing the Social, Spatial, and Temporal Dimensions of Human Mobility with a Unifying Framework

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-topeer message forwarding. The current approach with human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they came up from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a exible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility. Our framework, named SPoT, is able to incorporate the three dimensions - spatial, social, and temporal - of human mobility. The way SPoT does it is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the exibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a throughout mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge

HINT - from opportunistic network characterization to application development

Delay Tolerant Networks are currently a promising alternative to infrastructure-based networks, but they have not seen a wide deployment so far. There are several ways to evaluate the performance of such networks: field trials, theoretical models, simulation, emulation or replaying contact datasets. Each one has its advantages and drawbacks in terms of material cost, realism, required time or ability to manage real nodes. However, none of them effectively addresses the needs of application developers. In this thesis, we will focus on emulation. In a first part, we will deal with possible inputs for such a system. We first propose an analytical model to predict the drop ratio in a network where nodes have a one-packet buffer. Then, taking inspiration from trace scaling approaches from the literature, we study the hypotheses and assumptions taken for real traces statistical analyses, showing their impact on the obtained probability distributions and observed network performance metrics. We then extend this study to the whole life cycle of real traces, by considering data collection, filtering and scaling. In a second part, we propose a possible architecture for a hybrid DTN emulator, using both real nodes as smartphones and virtual nodes. The main advantage here is to be able to evaluate real applications, including preexisting ones, in a DTN context, doing so as transparently as possible. We identify the limitations of existing approaches, which helps us build a list of specifications for our system. Then, we propose a system called HINT which matches these specifications. HINT is validated, and applied to the study of some examples

Mobility Increases the Data Offloading Ratio in D2D Caching Networks

Caching at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technique to accommodate the exponentially increasing mobile data traffic. While most previous works ignored user mobility, there are some recent works taking it into account. However, the duration of user contact times has been ignored, making it difficult to explicitly characterize the effect of mobility. In this paper, we adopt the alternating renewal process to model the duration of both the contact and inter-contact times, and investigate how the caching performance is affected by mobility. The data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is taken as the performance metric. We first approximate the distribution of the communication time for a given user by beta distribution through moment matching. With this approximation, an accurate expression of the data offloading ratio is derived. For the homogeneous case where the average contact and inter-contact times of different user pairs are identical, we prove that the data offloading ratio increases with the user moving speed, assuming that the transmission rate remains the same. Simulation results are provided to show the accuracy of the approximate result, and also validate the effect of user mobility.Comment: 6 pages, 5 figures, accepted to IEEE Int. Conf. Commun. (ICC), Paris, France, May 201

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