The strength of vicinity annexation in opportunistic networking

Phe-Neau, Tiphaine; Amorim, Marcelo Dias de; Conan, Vania

doi:10.1109/infcomw.2013.6562898

Cited by 6 publications

(5 citation statements)

References 23 publications

(9 reference statements)

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“…Hence, probing your 4-vicinity is enough to get most of the arrivals patterns in a node's surrounding. We confirm our previous finding on the optimal limit of κ-vicinity probing [13].…”

Section: Birth In the κ-Vicinitysupporting

confidence: 92%

Examining vicinity dynamics in opportunistic networks

Phe-Neau

Amorim

Campista

et al. 2013

Proceedings of the 8th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks

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Modeling 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.

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“…Hence, probing your 4-vicinity is enough to get most of the arrivals patterns in a node's surrounding. We confirm our previous finding on the optimal limit of κ-vicinity probing [13].…”

Section: Birth In the κ-Vicinitysupporting

confidence: 92%

Examining vicinity dynamics in opportunistic networks

Phe-Neau

Amorim

Campista

et al. 2013

Proceedings of the 8th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks

Self Cite

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“…We wish to point out that some recent work highlights the benefit of the vicinity knowledge in terms of routing over opportunistic networks . In particular, Tiphaine Phe‐Neau et al , demonstrate the benefit of knowing neighbor nodes located within k hops from a source and call it k −vicinity knowledge. In a certain manner, DQN lays on the same property by limiting the spread of its number of copies within a number of hops as explained in the following algorithm.…”

Section: Dqn Algorithmmentioning

confidence: 99%

DTN routing for quasi-deterministic networks with application to LEO constellations

Diana

Lochin

Franck

et al. 2015

Int. J. Satell. Commun. Network.

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International audienceWe propose a novel DTN routing algorithm, called DQN, specifically designed for quasi- deterministic networks with an application to satellite constellations. We demonstrate that our proposal efficiently forwards the information over a satellite network derived from the Orbcomm topology while keeping a low replication overhead. We compare our algorithm against other well- known DTN routing schemes and show that we obtain the lowest replication ratio with a delivery ratio of the same order of magnitude than a reference theoretical optimal routing. We also analyse the impact of terrestrial gateways density and analyse DQN performances in heterogeneous cases

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“…Phe-Neau et al considered the multi-hop communication opportunities around a node's vicinity in [29]. However, in their approach, multi-hop communication opportunities around a node are only utilized when the destination of the data is within the node's vicinity, which is basically a WAIT strategy and then it does not contain mechanisms to make the data reach more nodes and get closer to the destination.…”

Section: ) Comparisons With R3mentioning

confidence: 99%

Efficient Data Forwarding in Mobile Social Networks with Diverse Connectivity Characteristics

Zhang

Cao

2014

2014 IEEE 34th International Conference on Distributed Computing Systems

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Abstract-Mobile Social Network (MSN) with diverse connectivity characteristics is a combination of opportunistic network and mobile ad hoc network. Since the major difficulty of data forwarding is the opportunistic part, techniques designed for opportunistic networks are commonly used to forward data in MSNs. However, this may not be the best solution since they do not consider the ubiquitous existences of Transient Connected Components (TCCs), where nodes inside a TCC can reach each other by multi-hop wireless communications. In this paper, we first identify the existence of TCCs and analyze their properties based on five real traces. Then, we propose TCC-aware data forwarding strategies which exploit the special characteristics of TCCs to increase the contact opportunities and then improve the performance of data forwarding. Tracedriven simulations show that our TCC-aware data forwarding strategies outperform existing data forwarding strategies in terms of data delivery ratio and network overhead.

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The strength of vicinity annexation in opportunistic networking

Cited by 6 publications

References 23 publications

Examining vicinity dynamics in opportunistic networks

Examining vicinity dynamics in opportunistic networks

DTN routing for quasi-deterministic networks with application to LEO constellations

Efficient Data Forwarding in Mobile Social Networks with Diverse Connectivity Characteristics

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