Exploiting social paradigms as routingstrategies in Delay Tolerant Networks:

an introduction

Claudio Fiandrinoclaudio.fiandrino@gmail.com

August 27, 2012

Abstract

The article illustrates several approaches developed in literaturethat exploit social paradigms to improve the routing in Delay TolerantNetworks (DTNs). Moreover, it gives an overview on the mobility mod-els adopted, focusing the attention to those ones useful in opportunisticnetworks.

Contents

1 Motivations 1

2 The strategies developed 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 The analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.2.1 Mobility models . . . . . . . . . . . . . . . . . . . . . 22.2.2 Routing strategies . . . . . . . . . . . . . . . . . . . . 3

Bibliography 5

1 Motivations

The application of social paradigms to DTNs it is a very well known conceptin literature and researchers largely explored this field by developing a lot ofdifferent algorithms and strategies.

The basic idea behind all, is to exploit social relations to identify humanmobility patterns; as result, socially-aware algorithms achieve better perfor-mances than non-socially-aware algorithms since to spread the informationby means of social paradigms allow to be fast (thus latency is reduced) and

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to increase the delivery ratio with a lower cost (in terms of the number ofmessage replicas).

The detection of human mobility pattern is a fundamental part since ithas been noticed that people tend to meet frequently when they have interestin common in physical places as streets, squares, conferences, etc . . . In [29]authors proved this concept by highlighting the relations between the socialgraph and the contact graph.

2 The strategies developed

2.1 Introduction

A survey on the various social approaches already used in literature is givenin [12]: in this section, a better picture of the state of the art is provided byenhancing [12].

2.2 The analysis

2.2.1 Mobility models

Some experiments to study human relations and to build social traces weredone with the help of volunteers at conferences [20] or in a campus [38].More deeply, [38] proves that the user mobility form a network. However,this studies consider just a small number of individuals. Other studies as[33, 22, 14] rely on traces already available in the Internet that contains thecontact history of taxicabs while [37] takes into account the mobility patternsof 22 341 students to efficiently design aggregation algorithms. Furthermore,the Haggle Project [40] creates a DTN by connecting human mobile devices.

In [36] authors propose a community model able to characterize the real-life mobility features. Based on this concept, [28] shows that the separationof people into two groups, friends and strangers, allows to improve the routingperformances. Moreover, they try to address security issues and they showthat people encounters have daily and weekly cycles. An improvement ofthis work has been shown in [42]; individuals, here, are divided into fourgroups: familiar, familiar stranger, friends and strangers.

Similarly to [37], [9] analysed the human mobility: their contribute is theproof that the distribution of contact and inter-contact times follow a power-law distribution with heavy-tail. On this bases, [31] developed community-based mobility models (CMM), showing that they present contact times andinter-contact times heavy-tailed. Another mobility model is presented in [13]and this model tries to mimic the behaviour of the average person. Foundedon social networks theory, authors in [32] provide an interesting mobilitymodel: individual are grouped together based on their social-relationship

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and groups are mapped on a topographical space. They also forecast thatsocial-relationship may change over time.

How mobility models affect routing performances has been studied in[2]; the analysis is based on groups and the conclusion is that social-awarerouting schemes reduces congestion and provide acceptable QoS with loweroverhead.

Some other studies rely on simplistic random mobility models (RandomWalk, Random Way Point) and they try to address problems like:

• single-copy based routing algorithms [25, 21, 8, 35];

• multi-copy based routing algorithms [34, 5];

• erasure coding based routing algorithms (they allow the use of a largenumber of relays while maintaining a constant overhead) [41, 6].

However, [9] proved that these mobility models are not suitable for DTNssince they do not have heavy-tailed distributed contact times and inter-contact times.

A work that exploits the history of the social relations among users is[3]. The developed algorithm, HiBOp, automatically learns which are themost efficient connectivity opportunities that are based on users’ movementpatterns.

2.2.2 Routing strategies

Now algorithms that try to detect which are the best individuals to forwardthe information to will be analysed.

PRoPHET [24] makes routing decision based on the delivery probability,a paradigm in which last encountered are more relevant as individuals toforward the information to. Due to this reason, it is possible to claim thatPRoPHET uses an indirect social metric because, usually, social-connectedindividuals meet themselves more frequently. SimBet [11] introduces twonew paradigms: similarity and betweenness. The former measures how twonodes are socially closed each other by comparing their mutual contacts whilethe latter estimates the centrality of a node, that is how much it is importantin the network. Bubble rap [19] uses as paradigms community and centrality:they measure the relevance of an individual locally and in a global fashionrespectively. The routing strategy forecast that messages are forwarded toindividuals with high global popularity until it is reached the destinationcommunity; once there, messages are forwarded to the individual withhighest local popularity because it has more change to get in contact withthe recipient. PeopleRank [30] takes inspiration from the famous PageRankto detect the best individual to which data is forwarded.

In [17] authors proved that the performances of SimBet and Bubble rapheavily depends on the way in which the contact aggregation (mapping) has

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been done. The same author in [18] propose a new mapping algorithm tocope previous issues that builds better contact-graphs based on concepts ofspectral graph theory and unsupervised learning.

SimBet, Bubble rap and PeopleRank look at the single individual; adifferent approach has been taken from authors of [7]: they consider theaverage inter-meeting times between groups of nodes.

Other community-based approaches are DelQue [15] and [23] in whichauthors present a distributed algorithm, LocalCom, that utilizes local informa-tion to detect communities. Moreover, they developed two schemes able tofacilitate the forwarding by control the redundancy of gateway that connectcommunities.

SimBet, Bubble rap and LocalCom are unicast algorithms. A multicastone has been proposed by [16].

Another algorithm is “SMART” [39]: it bases its own forwarding strategyon the so called travel companions, individuals that more frequently meetthe destination. In the first phase, a fixed number of copies of the messageare injected in the network destined to a travel companion and in the secondphase, the travel companion forwards the message to the destination (if met)or to a fixed number of other travel companions.

In [4] authors introduce a different routing algorithm. They defined anew social metric that does not rely on the encounter frequency, total oraverage contact period, but it takes into account contemporaneously thefollowing features: frequency, longevity and regularity. Friendship, therefore,is ensured when two individuals get in contact with frequently, for a longperiod each time and regularly. Notice that frequency and regularity here aredifferent: two individuals that get in contact with regularly during months,but unfrequently (once a month for example) should still be consideredfriends (in a weaker fashion). The three properties form the so called SocialPressure Metric (SPM): it measure how much an individual is motivated toshare contents with others.

In [10] is proposed SocialCast, an interest-based routing algorithm. Thebasic paradigm is publish/subscribing: the publisher simply inject the messageinto the network and it will be delivered just to nodes that previously declaredto be interested in (subscription phase). The algorithm relies on the fact thatsocially-related people tend to be co-located regularly and this allows to makereasonable predictions to select good carries. This approach allows to infer aone-to-many communication paradigm without extracting community tiesamong the individuals. SocialCast allows, with a small number of replicas inthe system, to achieve a very high delivery ratio by keeping the same networktraffic of a non-predictive algorithm. Still related to the publish/subscribingparadigm are Still related to the publish/subscribing paradigm are [43], [44]and [26].

Another work of this kind and that exploits social relationship to transmitmessages is [1] with the ContentPlace framework. When a node gets in

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contact with another one, it advertises the data object it is interested inand exchanges summaries of the data object it holds. Since each objecthas an utility value, individuals can decide where to place their object (thepublisher can decide the direction and the propagation range) such that theiravailability for the whole network is maximize.

The above strategies are “stateful” strategie, that is the improvement ofperformances comes at the cost of knowing information like past encountersor portion of the social network graph. In [27] authors provide an approachin which they model individuals’ interests in m-dimensional interest spaceand they create individuals’ interest profiles, that are m-dimensional vectorscorresponding to a point in the interest space. The forwarding is based onthe similarity of the individuals’ interest profiles.

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