ML-SOR: Message routing using multi-layer social networks in opportunistic communications A. Socievole a,⇑ , E. Yoneki b , F. De Rango a , J. Crowcroft b a Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Arcavacata di Rende (CS), Italy b Computer Laboratory, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK article info Article history: Received 17 January 2014 Received in revised form 5 January 2015 Accepted 18 February 2015 Available online 24 February 2015 Keywords: Opportunistic network Opportunistic routing Online social network Detected social network Multi-layer network abstract Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, mes- sage routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact infor- mation changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact informa- tion. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, commu- nity structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost. Ó 2015 Elsevier B.V. All rights reserved. 1. Introduction The pervasive use of mobile phones and the social networking applications available on these devices have attracted particular interest in recent years, especially in the research area of infrastructure-less network architectures exploiting peer-to-peer opportunistic connectivity and social relations for content dissemination. In a world where individuals are becoming increasingly reliant on mobile communication in several aspects of their life, being unable to communicate can negatively affect both business and personal relationships. Consequently, when there is no suitable network architec- ture, an alternative system is necessary. Delay Tolerant Networks (DTNs) [1–3] were designed to allow communi- cation in challenged scenarios where a fixed network infrastructure is not available, nodes often create sparse network topologies and the contacts between them are http://dx.doi.org/10.1016/j.comnet.2015.02.016 1389-1286/Ó 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: DIMES Cubo 41 C, Ponte P. Bucci, University of Calabria, 87036 Arcavacata di Rende (CS), Italy. Tel.: +39 0984494802. E-mail addresses: socievolea@dimes.unical.it (A. Socievole), eiko. yoneki@cl.cam.ac.uk (E. Yoneki), derango@dimes.unical.it (F. De Rango), jon.crowcroft@cl.cam.ac.uk (J. Crowcroft). Computer Networks 81 (2015) 201–219 Contents lists available at ScienceDirect Computer Networks journal homepage: www.elsevier.com/locate/comnet