Finding Self-Similarities in Opportunistic People Networks

Chen, Ling-Jyh; Chen, Yung-Chin; Sun, Tony; Sreedevi, Paruvelli; Chen, Kuan-Ta; Yu, Chen-Hung; Chu, Hao-Hua

doi:10.1109/infcom.2007.266

Cited by 15 publications

(15 citation statements)

References 15 publications

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“…The experimental study by Chen et al (2007) is one of the first works, which seeks to find self-similarity in wireless AP association patterns. Although, it is well recognised that Internet traffic exhibit self-similarities (Garrett and Willinger, 1994;Leland et al, 1994;Paxson and Floyd, 1995;Crovella and Bestavros, 1997).…”

Section: Related Workmentioning

confidence: 99%

On the characterisation of vehicular mobility in a large-scale public transport network

Ahmed

Kanhere

2012

IJAHUC

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In this paper, we present a thorough characterisation of the spatio-temporal communication graph of a large-scale real-world public transport network. Unlike previous studies, which either use synthetic mobility traces or data from small networks (< 50 nodes), our analysis is based on the mobility patterns of a large-scale (~1200 nodes) real-world public transport network. In particular, we examine the node degree distribution, encounter patterns, periodicity and clustering behaviour -properties that are particularly relevant in the context of data forwarding in DTN. Our extensive study demonstrates that public transport networks exhibit repetitive patterns and corroborates the existence of few highly connected nodes (termed as hubs). We have also found that the degree distribution of nodes and the inter-contact durations follow the properties of power-law distributions and exhibit moderate to strong self-similarities. We provide insights on how these properties can be leveraged to design effective communication protocols for such large-scale DTN.

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Section: Related Workmentioning

confidence: 99%

On the characterisation of vehicular mobility in a large-scale public transport network

Ahmed

Kanhere

2012

IJAHUC

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“…Similar to [10][21], we assume that two participating devices 7 In the Dartmouth network trace, there were 13,888 devices in the network, but only 5,148 of them had contact with other devices. 8 The inter-contact time distributions of the UCSD and Dartmouth traces have been shown to be heavy-tailed with the parameter α equal to 0.26 and 0.47 respectively, and both of them have also been proved to be self-similar [11]. in ad hoc mode encounter a communication opportunity (i.e., a network contact) if and only if they are both associated with the same AP during the same time period.…”

Section: A Evaluating Scenariosmentioning

confidence: 99%

“…Fig. 8 shows the CDF distribution of the web document quality received by a surfer, based on different coding and data forwarding schemes, at three time points in the UCSD scenario 11 . In Fig.…”

Section: Evaluation: Web Surfingmentioning

confidence: 99%

A content-centric framework for effective data dissemination in opportunistic networks

Chen

Tseng

et al. 2008

IEEE J. Select. Areas Commun.

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Abstract-In this paper, we address the challenges of content transfer in opportunistic networks, and propose techniques to better facilitate data dissemination based on the characteristics of the content. To investigate this problem from its origins, we propose three message scheduling algorithms: Sequential Forwarding (SF), Full Interleaving (FI), and Block-based Interleaving (BI). Each algorithm is embedded in a specially tailored data dissemination technique to evaluate the benefits of applying it to different types of content and data dissemination methods. Three types of content (file, video and web) are considered and evaluated, and the dissemination methods considered are Layered Multiple Description Coding (LMDC) based and file-based. Using simulations as well as both synthetic and realistic network scenarios, we evaluate the proposed schemes in terms of latency and user perceived quality, and demonstrate how the schemes can achieve much better latency performance for file transfers. Furthermore, we show that using LMDC-based techniques leads to higher user perceived quality, since the end user is allowed to "preview" video file or web content, even before the data has been completely transferred. The effectiveness and robustness of our message scheduling algorithms and their corresponding content dissemination techniques make them ideal solutions that can go a long way toward effective data dissemination in opportunistic networks.

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“…Since then it has been observed that the experimental inter-encounter time distributions appear to undergo a transition from power-law to exponential on a timescale of about one half day [20], and the reasons behind the observed exponentially distributed inter-encounter times in simulations using random walk mobility have been thoroughly examined [4]. Other analyses have involved the uncensoring of trace data [7], and node degree distribution.…”

Section: Related Prior Workmentioning

confidence: 99%

Using persistent homology to recover spatial information from encounter traces

Walker

2008

Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing

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In order to better understand human and animal mobility and its potential effects on Mobile Ad-Hoc networks and Delay-Tolerant Networks, many researchers have conducted experiments which collect encounter data. Most analyses of these data have focused on isolated statistical properties such as the distribution of node inter-encounter times and the degree distribution of the connectivity graph.On the other hand, new developments in computational topology, in particular persistent homology, have made it possible to compute topological invariants from noisy data. These homological methods provide a natural way to draw conclusions about global structure based on collections of local information.We use persistent homology techniques to show that in some cases encounter traces can be used to deduce information about the topology of the physical space the experiment was conducted in, and detect certain changes in the space. We also show that one can distinguish between simulated encounter traces generated on a bounded rectangular grid from traces generated on a grid with the opposite edges wrapped (a toroidal grid). Finally, we have found that nontrivial topological features also appear in real experimental encounter traces, and we speculate on types of node behavior that could produce these results. This demonstrates the ability of persistent homology to detect topological features in encounter data that could be difficult to describe using traditional statistical and geometric methods. Figure 1: The topology of the space affects the type of encounter patterns that are possible. If the space is like a line, node A cannot encounter node C without one of them encountering node B. If the space is like a loop, node A and C can encounter each other without encountering node B.

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Finding Self-Similarities in Opportunistic People Networks

Cited by 15 publications

References 15 publications

On the characterisation of vehicular mobility in a large-scale public transport network

On the characterisation of vehicular mobility in a large-scale public transport network

A content-centric framework for effective data dissemination in opportunistic networks

Using persistent homology to recover spatial information from encounter traces

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