Directional-IPeR: Enhanced Direction and Interest Aware Peoplerank for Opportunistic Mobile Social Networks

Yosra Shahin, Soumaia Al Ayyat, Sherif Aly

Abstract

Network infrastructures are being continuously challenged by virtue of increased demand, resource-hungry applications, and at times of crisis when people need to work from homes such as the current Covid-19 epidemic situation, where most of the countries applied partial or complete lockdown and most of the people worked from home. Opportunistic Mobile Social Networks (OMSN) prove to be a great candidate to support existing network infrastructures. However, OMSNs have copious challenges comprising frequent disconnections and long delays. In this research, we aim to enhance the performance of OMSNs including delivery ratio and delay. We build upon an interest-aware social forwarding algorithm, namely Interest Aware PeopleRank (IPeR) in two ways 1) By embracing directional forwarding (Directional-IPeR), and (2) By utilizing a combination of Directional forwarding and multi-hop forwarding (DMIPeR). Different interest distributions and users’ densities are simulated using the Social-Aware Opportunistic Forwarding Simulator (SAROS). The results show that Directional-IPeR with a tolerance factor of 75% performed the best in terms of delay and delivery ratio compared to IPeR, and two other algorithms, namely MIPeR and DMIPeR.

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