Bubble rap: social-based forwarding in delay tolerant networks
26 May 2008-pp 241-250
TL;DR: BUBBLE is designed and evaluated, a novel social-based forwarding algorithm that utilizes the aforementioned metrics to enhance delivery performance and empirically shows that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social- based forwarding SimBet algorithm.
Abstract: In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs) Taking human mobility traces from the real world, we discover that human interaction is heterogeneous both in terms of hubs (popular individuals) and groups or communities We propose a social based forwarding algorithm, BUBBLE, which is shown empirically to improve the forwarding efficiency significantly compared to oblivious forwarding schemes and to PROPHET algorithm We also show how this algorithm can be implemented in a distributed way, which demonstrates that it is applicable in the decentralised environment of PSNs
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01 Jan 2010
TL;DR: A global center for commercial innovation, PARC, a Xerox company, works closely with enterprises, entrepreneurs, government program partners and other clients to discover, develop, and deliver new business opportunities.
Abstract: A global center for commercial innovation, PARC, a Xerox company, works closely with enterprises, entrepreneurs, government program partners and other clients to discover, develop, and deliver new business opportunities. PARC was incorporated in 2002 as a wholly owned subsidiary of Xerox Corporation (NYSE: XRX).
1,072 citations
18 May 2009
TL;DR: This paper is the first to study multicast in DTNs from the social network perspective, and investigates the essential difference between multicast and unicast inDTNs, and forms relay selections for multicast as a unified knapsack problem by exploiting node centrality and social community structures.
Abstract: Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly impair the effectiveness of data dissemination. Although social-based approaches can be used to address the problem, most existing solutions only focus on forwarding data to a single destination. In this paper, we are the first to study multicast in DTNs from the social network perspective. We study multicast in DTNs with single and multiple data items, investigate the essential difference between multicast and unicast in DTNs, and formulate relay selections for multicast as a unified knapsack problem by exploiting node centrality and social community structures. Extensive trace-driven simulations show that our approach has similar delivery ratio and delay to the Epidemic routing, but can significantly reduce the data forwarding cost measured by the number of relays used.
575 citations
Cites background or methods or result from "Bubble rap: social-based forwarding..."
...3 Comparison with other social-based schemes In this section, we compare the performance of our SDM scheme with other social-based forwarding schemes including SimBet [4] and BUBBLE Rap [9]....
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...1 Centrality Metric Currently, the “betweenness” centrality metric is widely used in social-based data forwarding [4, 9]....
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...Later work [9] considers node centrality in a hierarchical manner based on social community knowledge....
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...These results, together with the fact that contact frequencies between nodes in the same community are much higher than the network average level [9], ensure that the community-based destination-awareness can be maintained up-to-date and accurately at individual nodes....
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...The number of destinations for each data item is uniformly randomized in the range [3, 9]....
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17 Aug 2009
TL;DR: This work designs and implements a novel mobile social networking middleware named MobiClique, which distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content.
Abstract: We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.
400 citations
Cites background from "Bubble rap: social-based forwarding..."
...Several recent studies [9, 5, 3] propose to use various properties of the social graph such as node centrality and community structures to make efficient forwarding decisions....
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14 Mar 2010
TL;DR: To select a forwarding node, SSAR considers both users' willingness to forward and their contact opportunity, resulting in a better forwarding strategy than purely contact-based approaches and formulates the data forwarding process as a Multiple Knapsack Problem with Assignment Restrictions to satisfy user demands for selfishness and performance.
Abstract: Existing routing algorithms for Delay Tolerant Networks(DTNs) assume that nodes are willing to forward packets for others. In the real world, however, most people are socially selfish; i.e., they are willing to forward packets for nodes with whom they have social ties but not others, and such willingness varies with the strength of the social tie. Following the philosophy of design for user, we propose a Social Selfishness Aware Routing (SSAR) algorithm to allow user selfishness and provide better routing performance in an efficient way. To select a forwarding node, SSAR considers both users' willingness to forward and their contact opportunity, resulting in a better forwarding strategy than purely contact-based approaches. Moreover, SSAR formulates the data forwarding process as a Multiple Knapsack Problem with Assignment Restrictions (MKPAR) to satisfy user demands for selfishness and performance. Trace-driven simulations show that SSAR allows users to maintain selfishness and achieves better routing performance with low transmission cost.
374 citations
Cites background or methods or result from "Bubble rap: social-based forwarding..."
...We use the same parameters as in [2], and age the delivery predictability upon every contact as done in [6]....
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...been used to compare against several previous works [6]....
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...Recently, several algorithms [6], [7], [28], [29] use social metrics calculated from contacts....
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TL;DR: This article considers the term ICNs as Delay/Disruption Tolerant Networks (DTNs) for the purpose of generalization, since DTNs have been envisioned for different applications with a large number of proposed routing algorithms.
Abstract: The introduction of intelligent devices with short range wireless communication techniques has motivated the development of Mobile Ad hoc NETworks (MANETs) during the last few years. However, traditional end-to-end based routing algorithms designed for MANETs are not much robust in the challenged networks suffering from frequent disruption, sparse network density and limited device capability. Such challenged networks, also known as Intermittently Connected Networks (ICNs) adopt the Store-Carry-Forward (SCF) behavior arising from the mobility of mobile nodes for message relaying. In this article, we consider the term ICNs as Delay/Disruption Tolerant Networks (DTNs) for the purpose of generalization, since DTNs have been envisioned for different applications with a large number of proposed routing algorithms. Motivated by the great interest from the research community, we firstly review the existing unicasting issue of DTNs because of its extensive research stage. Then, we also address multicasting and anycasting issues in DTNs considering their perspectives. A detail survey based on our taxonomy over the period from 2006 to 2010 is not only provided but also a comparison is given. We further identify the remaining challenges and open issues followed by an evaluation framework proposed for routing in DTNs. Finally, we summarize our contribution with three future research topics highlighted.
349 citations
Cites background from "Bubble rap: social-based forwarding..."
...Although BUBBLE makes use of the distributed computation to ensure message diffusion, it requires the knowledge about the address and the social group of destination, which is unfair to the algorithms requiring only the address....
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...Motivated by this shortcoming, BUBBLE [53] combines the knowledge of community structure with the centrality of each node to make routing decision....
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...Bubble [53] Community, Centrality 1 Not Mentioned Not Mentioned Not Mentioned...
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References
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TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Abstract: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
12,882 citations
01 Mar 1977
TL;DR: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced in this paper, which define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication.
Abstract: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced. These measures define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication. They may be used to index centrality in any large or small network of symmetrical relations, whether connected or unconnected.
8,026 citations
TL;DR: After defining a set of new characteristic quantities for the statistics of communities, this work applies an efficient technique for exploring overlapping communities on a large scale and finds that overlaps are significant, and the distributions introduced reveal universal features of networks.
Abstract: A network is a network — be it between words (those associated with ‘bright’ in this case) or protein structures. Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of1,2,3,4. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins5,6, industrial sectors7 and groups of people8,9) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.
5,217 citations
01 Jan 2000
TL;DR: This work introduces Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery and achieves eventual delivery of 100% of messages with reasonable aggregate resource consumption in a number of interesting scenarios.
Abstract: Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100% of messages with reasonable aggregate resource consumption in a number of interesting scenarios.
4,355 citations
"Bubble rap: social-based forwarding..." refers result in this paper
...proposed epidemic routing, which is similar to the “oblivio us” flooding scheme we evaluated in this paper [27]....
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TL;DR: General principles that govern the structure and behaviour of modules may be discovered with help from synthetic sciences such as engineering and computer science, from stronger interactions between experiment and theory in cell biology, and from an appreciation of evolutionary constraints.
Abstract: Cellular functions, such as signal transmission, are carried out by 'modules' made up of many species of interacting molecules Understanding how modules work has depended on combining phenomenological analysis with molecular studies General principles that govern the structure and behaviour of modules may be discovered with help from synthetic sciences such as engineering and computer science, from stronger interactions between experiment and theory in cell biology, and from an appreciation of evolutionary constraints
3,604 citations
"Bubble rap: social-based forwarding..." refers background in this paper
...communities, by looking for similar relation has also been studied in social network research such as World Wide Web [7], biological networks [9], social networks [25], and the Internet [20]....
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