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A. Ferreira

Bio: A. Ferreira is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Ad hoc wireless distribution service & Vehicular ad hoc network. The author has an hindex of 1, co-authored 1 publications receiving 229 citations.

Papers
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Journal ArticleDOI
TL;DR: It is shown how the modeling of time-changes unsettles old questions and allows for new insights into central problems in networking, such as routing metrics, connectivity, and spanning trees.
Abstract: Wireless technologies and the deployment of mobile and nomadic services are driving the emergence of complex ad hoc networks that have a highly dynamic behavior. Modeling such dynamics and creating a reference model on which results could be compared and reproduced, was stated as a fundamental issue by a recent NSF workshop on networking. In this article we show how the modeling of time-changes unsettles old questions and allows for new insights into central problems in networking, such as routing metrics, connectivity, and spanning trees. Such modeling is made possible through evolving graphs, a simple combinatorial model that helps capture the behavior or dynamic networks over time.

241 citations


Cited by
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Journal ArticleDOI
TL;DR: This article captures the state of the art in routing protocols in DTNs with three main approaches: the tree approach, the space and time approach, and the modified shortest shortest path approach.
Abstract: n the last few years, there has been much research activity in mobile, wireless, ad hoc networks (MANET). MANETs are infrastructure-less, and nodes in the networks are constantly moving. In MANETs, nodes can directly communicate with each other if they enter each others' communication range. A node can terminate packets or forward packets (serve as a relay). Thus, a packet traverses an ad hoc network by being relayed from one node to another, until it reaches its destination. As nodes are moving, this becomes a challenging task, since the topology of the network is in constant change. How to find a destination, how to route to that destination, and how to insure robust communication in the face of constant topology change are major challenges in mobile ad hoc networks. Routing in mobile ad hoc networks is a well-studied topic. To accommodate the dynamic topology of mobile ad hoc networks, an abundance of routing protocols have recent-For all these routing protocols, it is implicitly assumed that the network is connected and there is a contemporaneous end-to-end path between any source and destination pair. However, in a physical ad hoc network, the assumption that there is a contemporaneous end-to-end path between any source and destination pair may not be true, as illustrated below. In MANETs, when nodes are in motion, links can be obstructed by intervening objects. When nodes must conserve power, links are shut down periodically. These events result in intermittent connectivity. At any given time, when no path exists between source and destination, network partition is said to occur. Thus, it is perfectly possible that two nodes may never be part of the same connected portion of the network. Figure 1 illustrates the time evolving behavior in intermittent-ABSTRACT Recently there has been much research activity in the emerging area of intermittently connected ad hoc networks and delay/disruption tolerant networks (DTN). There are different types of DTNs, depending on the nature of the network environment. Routing in DTNs is one of the key components in the DTN architecture. Therefore, in the last few years researchers have proposed different routing protocols for different types of DTNs. In this article we capture the state of the art in routing protocols in DTNs. We categorize these routing protocols based on information used. For deter-ministic time evolving networks, three main approaches are discussed: the tree approach, the space and time approach, and the modified shortest …

861 citations

Journal ArticleDOI
TL;DR: This paper presents a hierarchical classification of TVGs; each class corresponds to a significant property examined in the distributed computing literature, and examines how TVGs can be used to study the evolution of network properties, and proposes different techniques, depending on whether the indicators for these properties are atemporal or temporal.
Abstract: The past few years have seen intensive research efforts carried out in some apparently unrelated areas of dynamic systems – delay-tolerant networks, opportunistic-mobility networks and social networks – obtaining closely related insights. Indeed, the concepts discovered in these investigations can be viewed as parts of the same conceptual universe, and the formal models proposed so far to express some specific concepts are the components of a larger formal description of this universe. The main contribution of this paper is to integrate the vast collection of concepts, formalisms and results found in the literature into a unified framework, which we call time-varying graphs TVGs. Using this framework, it is possible to express directly in the same formalism not only the concepts common to all those different areas, but also those specific to each. Based on this definitional work, employing both existing results and original observations, we present a hierarchical classification of TVGs; each class corresponds to a significant property examined in the distributed computing literature. We then examine how TVGs can be used to study the evolution of network properties, and propose different techniques, depending on whether the indicators for these properties are atemporal as in the majority of existing studies or temporal. Finally, we briefly discuss the introduction of randomness in TVGs.

466 citations

Journal ArticleDOI
TL;DR: A simple but powerful model, the time-ordered graph, is presented, which reduces a dynamic network to a static network with directed flows, which enables it to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case.
Abstract: Many networks are dynamic in that their topology changes rapidly---on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

276 citations

Posted Content
TL;DR: The main contribution of this paper is to review and integrate the collection of these concepts, formalisms, and related results found in the literature into a unified coherent framework, called TVG (for timevarying graphs).
Abstract: The past few years have seen intensive research efforts carried out in some apparently unrelated areas of dynamic systems -- delay-tolerant networks, opportunistic-mobility networks, social networks -- obtaining closely related insights. Indeed, the concepts discovered in these investigations can be viewed as parts of the same conceptual universe; and the formal models proposed so far to express some specific concepts are components of a larger formal description of this universe. The main contribution of this paper is to integrate the vast collection of concepts, formalisms, and results found in the literature into a unified framework, which we call TVG (for time-varying graphs). Using this framework, it is possible to express directly in the same formalism not only the concepts common to all those different areas, but also those specific to each. Based on this definitional work, employing both existing results and original observations, we present a hierarchical classification of TVGs; each class corresponds to a significant property examined in the distributed computing literature. We then examine how TVGs can be used to study the evolution of network properties, and propose different techniques, depending on whether the indicators for these properties are a-temporal (as in the majority of existing studies) or temporal. Finally, we briefly discuss the introduction of randomness in TVGs.

253 citations

Journal ArticleDOI
TL;DR: This column surveys some recent work on dynamic network algorithms, focusing on the effect that model parameters such as the type of adversary, the network diameter, and the graph expansion can have on the performance of algorithms.
Abstract: The study of dynamic networks has come into popularity recently, and many models and algorithms for such networks have been suggested. In this column we survey some recent work on dynamic network algorithms, focusing on the effect that model parameters such as the type of adversary, the network diameter, and the graph expansion can have on the performance of algorithms. We focus here on high-level models that are not induced by some specific mobility pattern or geographic model (although much work has gone into geographic models of dynamic networks, and we touch upon them briefly in Section 2). Dynamic network behavior has long been studied in distributed computing literature, but initially it was modeled as a fault in the network; as such, it was typically bounded, either in duration or in the number of nodes affected (or both). For example, in the general omission-fault model, if two nodes that could once communicate can no longer send messages to each other, this is treated as a failure of one of the nodes, and the number of faulty nodes is assumed to be bounded. Another example is self-stabilizing algorithms, which are guaranteed to function correctly only when changes to the network have stopped [16]. These models are appropriate for modeling unreliable static networks, but they are not appropriate for mobile and ad hoc networks, where changes are unbounded in number and occur continually. In the sequel we survey several models for dynamic networks, both random and adversarial, and algorithms for these models. The literature on dynamic networks is vast, and this column is not intended as a comprehensive survey. We have chosen to focus on models and algorithms that exhibit the following properties.

233 citations