Interest-based Epidemic Routing in Opportunistic Mobile Networks

Message delivery in opportunistic mobile networks is a challenging task since the network topology constantly changes and end-to-end paths can hardly be sustained. Epidemic routing forwards a copy message to each contacted node to achieve a high network delivery performance; this however easily burdens the network nodes with high traffic load, quickly depleting the node’s resources, e.g. power and storage, and finally degrading the network delivery performance. This paper proposes an interest-based Epidemic that improves Epidemic to be a content-aware forwarding by taking message content, node interest, and node community into consideration. Using simulation, driven by real human contact datasets, we investigate the performance of the proposed algorithm compared with Epidemic (content-oblivious) and Direct Transmission (content-aware), in terms of total delivered messages, average convergence time, and total relayed messages. Simulation results show that Epidemic-Interest outperforms Direct Transmission in terms of total delivered message and average convergence time. Moreover, compared with Epidemic, it can reduces the transmission cost while keeping the total delivered messages as high as Epidemic’s; however, it increases the convergence time beyond that of Epidemic.

[1]  Yongjian Yang,et al.  Community Based Routing in Social Delay Tolerant Networks , 2015, 2015 Ninth International Conference on Frontier of Computer Science and Technology.

[2]  Paulo Mendes,et al.  Social-aware forwarding in opportunistic wireless networks: Content awareness or obliviousness? , 2014, Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014.

[3]  Cauligi S. Raghavendra,et al.  Spray and wait: an efficient routing scheme for intermittently connected mobile networks , 2005, WDTN '05.

[4]  Jörg Ott,et al.  The ONE simulator for DTN protocol evaluation , 2009, SimuTools.

[5]  Susana Sargento,et al.  Social-Aware Opportunistic Routing Protocol Based on User's Interactions and Interests , 2013, ADHOCNETS.

[6]  Laurence T. Yang,et al.  $K$-Clique Community Detection in Social Networks Based on Formal Concept Analysis , 2017, IEEE Systems Journal.

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

[8]  Amin Vahdat,et al.  Epidemic Routing for Partially-Connected Ad Hoc Networks , 2009 .

[9]  Susana Sargento,et al.  Opportunistic routing based on daily routines , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[10]  Mohammad S. Obaidat,et al.  A priority based message forwarding scheme for Opportunistic Networks , 2016, 2016 International Conference on Computer, Information and Telecommunication Systems (CITS).

[11]  Mohan Kumar,et al.  Opportunities in Opportunistic Computing , 2010, Computer.

[12]  Qing Liao,et al.  COFFEE-CUP: A cost-efficient routing strategy for delay tolerant networks using time-varying community partitioning , 2016, 2016 International Symposium on Wireless Communication Systems (ISWCS).

[13]  Leanna Vidya Yovita,et al.  Performance analysis of social-aware content-based opportunistic routing protocol on MANET based on DTN , 2016, 2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC).