Impact of social structure on forwarding algorithms in opportunistic networks

Opportunistic networks are formed among mobile wireless devices based on spontaneous connectivity such as mobile phone networks using short range radio. Different setting of social structure in such networks gives significant impact on the feasibility and performance. In this paper we aim at understanding how social structure affects forwarding algorithm in various opportunistic network configurations. Having human mobility traces from the real world, we focus on the social structure in terms of centrality and community. We exploit different community detection and centrality calculation from the trace to present the features of such networks. We study a collection of Social-based Forwarding algorithms, such as LABEL, RANK, and BUBBLE [10]. Furthermore, we implement those forwarding algorithms over a Xen-based Haggle testbed [9]. We investigate the impact of community structure and centrality on performance and demonstrate that social structure influences the performance of the social-based Forwarding algorithms. Our result demonstrates that it is important to find appropriate centrality and communities for social networks with complex structure in the design of the social-based data dissemination algorithms.

[1]  M. Fiedler A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory , 1975 .

[2]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[3]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[4]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[5]  Anders Lindgren,et al.  Probabilistic Routing in Intermittently Connected Networks , 2004, SAPIR.

[6]  Pan Hui,et al.  BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks , 2008, IEEE Transactions on Mobile Computing.

[7]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[8]  Eiko Yoneki,et al.  Visualizing communities and centralities from encounter traces , 2008, CHANTS '08.

[9]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[10]  Pan Hui,et al.  Distributed community detection in delay tolerant networks , 2007, MobiArch '07.

[11]  Pan Hui,et al.  A socio-aware overlay for publish/subscribe communication in delay tolerant networks , 2007, MSWiM '07.

[12]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  A. Clauset Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[16]  Zhensheng Zhang,et al.  Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges , 2006, IEEE Communications Surveys & Tutorials.

[17]  Erwan Le Merrer,et al.  Centralities: capturing the fuzzy notion of importance in social graphs , 2009, SNS '09.