Please use this identifier to cite or link to this item:
Title: Enhancing opportunistic networking using location based social networks
Authors: Lambrinos, Lambros 
Kosmides, Pavlos 
Keywords: Delay tolerant networks;Machine learning;Location based social networks;Mobile opportunistic networks
Category: Media and Communications
Field: Social Sciences
Issue Date: 5-Jul-2016
Publisher: Association for Computing Machinery, Inc.
Source: 8th MobiHoc International Workshop on Hot Topics in Planet-Scale mObile Computing and Online Social Networking, 2016, Paderborn, Germany
DOI: 10.1145/2944789.2949545
Abstract: The wireless communication capabilities of mobile devices have evolved rapidly during the last decade. Exploiting the various connectivity technologies available devices are capable of forming intermittently connected networks; in these networks, defined as Mobile Opportunistic Networks (MONs), a multitude of mobile devices are carried by people and data packets are transferred between these devices opportunistically i.e. when communication opportunities arise. One important issue that arises in MONs concerns routing which must cope with network partitioning, long delays, and dynamic topology changes. Several approaches have been proposed in the literature, including the use of location information and the exploitation of social characteristics. In this paper we aim to enhance MONs during the routing process by combining both location and social information. To achieve this we introduce the use of Location-Based Social Networks (LBSNs) in order to collect necessary information about users' possible future locations. We present the deployment architecture of the proposed system and analyse the business processes and application services, including foreseen components and their interactions.
ISBN: 978-145034344-2
Rights: Copyright 2016 ACM.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s) 50

Last Week
Last month
checked on Jun 12, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.