Improving Data Forwarding in Mobile Social Networks with Infrastructure Support: A Space-Crossing Community Approach

TL;DR

This paper introduces a space-crossing community detection method and a novel data forwarding algorithm, SAAS, which leverage infrastructure support to enhance data delivery efficiency in mobile social networks.

Contribution

It presents a new community detection approach and a data forwarding algorithm that utilize infrastructure to improve performance in MSNs.

Findings

SAAS outperforms existing algorithms like Bubble Rap and Nguyen's Routing.

Space-crossing communities positively influence data delivery metrics.

The approach is validated on real-life datasets from MIT and UIM.

Abstract

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and UIM. Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of deliver ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms.