Fast flooding over Manhattan

Clementi, Andrea E. F.; Monti, Angelo; Silvestri, Riccardo

doi:10.1145/1835698.1835784

Cited by 11 publications

(11 citation statements)

References 25 publications

(50 reference statements)

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“…Among them, we emphasize the study of other infection processes such as the Susceptible-Infective-Susceptible model [11]. A further open issue is to extend the analysis of the parsimonious flooding to other explicit models of MANET such as the random way-point model [16,9].…”

Section: Discussionmentioning

confidence: 99%

Parsimonious Flooding in Geometric Random-Walks

Clementi

Silvestri

2011

Lecture Notes in Computer Science

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• We analyze the Parsimonious-Flooding Protocol on Mobile Opportunistic Networks yielded by the Geometric Random-Walk Model.• For the first time, we provide analytical bounds on the completion time. Such bounds are optimal for a wide range of the network parameters.• Departing significantly from previous analysis of the standard flooding protocol, our proof technique determines the geometric shape of the information spreading (i.e. the infection wave) over the time. June 3, 2014 AbstractWe study the epidemic process yielded by the k-Flooding Protocol in geometric Mobile Ad-Hoc Networks. We consider n agents on a square performing independent random walks. At any time step, every active agent informs every non-informed agent which is within distance R from it. An informed agent is active only for k time steps. Initially, a source agent is informed and we look at the completion time of the protocol. We prove optimal bounds on the completion time of the process. Our method of analysis provides a clear picture of the geometric shape of the information spreading over the time.

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Section: Discussionmentioning

confidence: 99%

Parsimonious Flooding in Geometric Random-Walks

Clementi

Silvestri

2011

Lecture Notes in Computer Science

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“…On the other hand, for any path from the source to a destination and for any node on such a path, since the degree of the node is constant, with high probability after a constant number of rounds the message will proceed in the right direction (towards the destination), hence will arrive after O(D) rounds with high probability. Finally, we note that one may also use the wide variety of results already available in the literature on flooding times or global network outreach [33], [30], [34], [35]. Such bounds may come in handy when using the results in this paper to extend the various models to multiple messages, correlated data and side information.…”

Section: Definitionmentioning

confidence: 95%

Network Coding Based Information Spreading in Dynamic Networks With Correlated Data

Cohen

Haeupler

Avin

et al. 2015

IEEE J. Select. Areas Commun.

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Abstract-In this work, we design and analyze information spreading algorithms for dynamic networks with correlated data. In these networks, either the data to be distributed, the data already available at the nodes, or both, are correlated. Moreover, nodes' availability and connectivity is dynamic -a scenario typical for wireless networks.Our contribution is twofold. First, although coding schemes for correlated data have been studied extensively, the focus has been on characterizing the rate region in static networks. In an information spreading scheme, however, nodes may communicate by continuously exchanging packets according to some underlying communication model. The main figure of merit is the stopping time -the time required until nodes can successfully decode. While information spreading schemes, such as gossip, are practical, distributed and scalable, they have only been studied for uncorrelated data. We close this gap by providing techniques to analyze network-coded information spreading in dynamic networks with correlated data.Second, we give a clean framework for oblivious dynamic network models that in particular applies to a multitude of wireless network and communication scenarios. We specify a general setting for the data model, and give tight bounds on the stopping times of network coded protocols in this wide range of settings. En route, we analyze the capacities seen by nodes under a network-coded information spreading protocol, a previously unexplored question.We conclude with extensive simulations, clearly validating the key trends and phenomena predicted in the analysis.

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“…Information dissemination in dynamic networks is usually realized by flooding or similar approaches, 2,4,12,31,32 which is necessary to theoretically guarantee the delivery of information to all nodes. The algorithm proposed by Clementi et al 20 is based on the EMDG model with consideration of edge birth rate and death rate.…”

Section: Related Workmentioning

confidence: 99%

Cluster-based efficient information dissemination in dynamic networks

Yang

Yong

et al. 2018

International Journal of Distributed Sensor Networks

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Dynamic network is an abstraction of networks with frequent topology changes arising from node mobility or other reasons. In this article, we first propose a dynamic network model, named (T, L)-HiNet, to extend existing dynamic network models with clusters. (T, L)-HiNet includes several properties defining the stability of cluster hierarchy in a dynamic network, including cluster head set, cluster member set and the connections among them. Based on (T, L)-HiNet, we design several hierarchical information dissemination algorithms for different scenarios of dynamics. Furthermore, we extend the (T, L)-HiNet model and corresponding algorithms in two directions, that is, stability of cluster head set and cluster size. The correctness of our algorithms is proved rigorously, and their performance is evaluated via both numerical analysis and simulations. The results show that compared with the algorithm recently proposed by Kuhn et al., our design can significantly reduce communication cost and also time cost.

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Fast flooding over Manhattan

Cited by 11 publications

References 25 publications

Parsimonious Flooding in Geometric Random-Walks

Parsimonious Flooding in Geometric Random-Walks

Network Coding Based Information Spreading in Dynamic Networks With Correlated Data

Cluster-based efficient information dissemination in dynamic networks

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