Building a reference combinatorial model for MANETs
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.
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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
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
Cites background from "Building a reference combinatorial ..."
...The lack of relationship, with regard to connectivity, between G and its footprint G is even stronger: the fact that G ¼ ðV ;EÞ is connected does not even imply that G is ‘connected over time’, as illustrated in Figure 3....
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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
Cites background from "Building a reference combinatorial ..."
...[54] talk of graphs over time; Ferreira [29] views the dynamic of the system in terms of a sequence of static graphs, called an evolving graph; Flocchini et al....
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...in [19] to investigate how relations between TVGs properties and feasibility of algorithms could be formally established, based on a combination of evolving graphs [29] and graph relabelings [56]....
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...At one extreme, each interval could correspond to the smallest time unit (in discrete-time systems), or to the time between any two consecutive modification of the graph; in these cases the whole sequence becomes equivalent to the model of evolving graph [29]....
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...The idea of representing a dynamic graph as a sequence of static graphs, mentioned in the conclusion of [37], was brought to life in [29] as a combinatorial model called evolving graphs....
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...All the concepts and definitions advanced so far are based on or imply such a notion, as expressed even by the choices of names; e.g., Kempe et al. [46] talk of a temporal network (G, λ) where λ is a time-labeling of the edges, that associates punctual dates to represent dated interactions; Leskovec et al. [54] talk of graphs over time; Ferreira [29] views the dynamic of the system in terms of a sequence of static graphs, called an evolving graph; Flocchini et al. [31] and Tang et al. [67] independently employ the term time-varying graphs; Kostakos uses the term temporal graph [50]; etc....
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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
Cites background from "Building a reference combinatorial ..."
...In the literature, such dynamic graphs have also been termed evolving graphs [3, 17, 18, 21]....
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References
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Book•
01 Jan 1990TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Abstract: From the Publisher:
The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Like the first edition,this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects.
In its new edition,Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity,and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.
As in the classic first edition,this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further,the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.
Each chapter presents an algorithm,a design technique,an application area,or a related topic. The chapters are not dependent on one another,so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally,the new edition offers a 25% increase over the first edition in the number of problems,giving the book 155 problems and over 900 exercises thatreinforcethe concepts the students are learning.
21,651 citations
01 Jul 2003
TL;DR: The important role that mobile ad hoc networks play in the evolution of future wireless technologies is explained and the latest research activities in these areas are reviewed, including a summary of MANETs characteristics, capabilities, applications, and design constraints.
Abstract: Mobile ad hoc networks (MANETs) represent complex distributed systems that comprise wireless mobile nodes that can freely and dynamically self-organize into arbitrary and temporary, ‘‘ad-hoc’’ network topologies, allowing people and devices to seamlessly internetwork in areas with no pre-existing communication infrastructure, e.g., disaster recovery environments. Ad hoc networking concept is not a new one, having been around in various forms for over 20 years. Traditionally, tactical networks have been the only communication networking application that followed the ad hoc paradigm. Recently, the introduction of new technologies such as the Bluetooth, IEEE 802.11 and Hyperlan are helping enable eventual commercial MANET deployments outside the military domain. These recent evolutions have been generating a renewed and growing interest in the research and development of MANET. This paper attempts to provide a comprehensive overview of this dynamic field. It first explains the important role that mobile ad hoc networks play in the evolution of future wireless technologies. Then, it reviews the latest research activities in these areas, including a summary of MANETs characteristics, capabilities, applications, and design constraints. The paper concludes by presenting a set of challenges and problems requiring further research in the future. � 2003 Elsevier B.V. All rights reserved.
1,430 citations
26 Mar 2000
TL;DR: This work develops the broadcast incremental power algorithm, and adapt it to multicast operation as well, and demonstrates that this algorithm provides better performance than algorithms that have been developed for the link-based, wired environment.
Abstract: The wireless networking environment presents formidable challenges to the study of broadcasting and multicasting problems. After addressing the characteristics of wireless networks that distinguish them from wired networks, we introduce and evaluate algorithms for tree construction in infrastructureless, all-wireless applications. The performance metric used to evaluate broadcast and multicast trees is energy-efficiency. We develop the broadcast incremental power algorithm, and adapt it to multicast operation as well. This algorithm exploits the broadcast nature of the wireless communication environment, and addresses the need for energy-efficient operation. We demonstrate that our algorithm provides better performance than algorithms that have been developed for the link-based, wired environment.
1,149 citations
TL;DR: In this paper, the authors considered the problem of finding the maximal amount of goods that can be transported from one node to another in a given number T of time periods, and how does one ship in order to achieve this maximum?
Abstract: A network, in which two integers tij the traversal time and cij the capacity are associated with each arc PiPj, is considered with respect to the following question. What is the maximal amount of goods that can be transported from one node to another in a given number T of time periods, and how does one ship in order to achieve this maximum? A computationally efficient algorithm for solving this dynamic linear-programming problem is presented. The algorithm has the following features a The only arithmetic operations required are addition and subtraction b In solving for a given time period T, optimal solutions for all lesser time periods are a by-product c The constructed optimal solution for a given T is presented as a relatively small number of activities chain-flows which are repeated over and over until the end of the T periods. Hence, in particular, hold-overs at intermediate nodes are not required d Arcs which serve as bottlenecks for the flow are singled out, as well as the time periods in which they act as such e In solving the problem for successive values of T, stabilization on a set of chain-flows seec above eventually occurs, and an a priori bound on when stabilization occurs can be established. The fact that there exist solutions to this problem which have the simple form described in c is remarkable, since other dynamic linear-programming problems that have been studied do not enjoy this property.
567 citations
01 Feb 2002
TL;DR: H Handbook of Internet Computing pdf eBook copy write by good Handbook of Wireless Networks and Mobile Computing Google Books.
Abstract: If you want to get Handbook of Internet Computing pdf eBook copy write by good Handbook of Wireless Networks and Mobile Computing Google Books. Mobile Computing General. Handbook of Algorithms for Wireless Networking and Mobile Computing by Azzedine Boukerche (Editor). Call Number: TK 5103.2. CITS4419 Mobile and Wireless Computing software projects related to wireless networks, (2) write technical reports and documentation for complex computer.
532 citations
Additional excerpts
...[1] [1] [2] [3] [4] [1 -2] [1-3] [1-4] [1-3] [1-2] [1] S A B C D E F G H n Figure 4....
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