Abstract

This work focuses on delay-tolerant networks in a social network environment. The nonexistence of end-to-end path between the source and the destination poses great challenges to the successful message transmission in delay-tolerant networks. In this article, we attempt to find a socially connected path above the intermittently connected physical topology. To this end, we study a weighted community graph model, which turns the original network into a network composed of communities and then describes the interaction delays between these communities. By performing a Dijkstra algorithm on this community graph, the expected minimum transmission delay to a destination community can be computed. To improve the performance on delivery delay, we propose a social routing called weighted community graph–based social routing that makes use of interaction delays between communities and social ties among nodes, which consists of two routing phases. In inter-community routing phase, messages are forwarded to its destination communities based on the computed minimum delays, and then in intra-community routing phase, each copy is forwarded within a destination community based on the social ties between nodes until meeting the final destination. Extensive simulations are conducted and the results show that weighted community graph–based social routing can improve routing performance, especially the performance on delivery delay and overhead ratio.

Highlights

  • Traditional communication model is based on some inherent network assumptions, such as the existence of end-to-end path, low delivery latency, and symmetrical bidirectional data transfer rate

  • We propose an optimal opportunistic forwarding routing scheme which uses these two optimal relay sets. Based on this graph model, we propose a weighted community graph–based social routing (WCGSR) to improve routing performance in delay-tolerant networks (DTNs)

  • We introduce a weighted community graph model

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Summary

Introduction

Traditional communication model is based on some inherent network assumptions, such as the existence of end-to-end path, low delivery latency, and symmetrical bidirectional data transfer rate. Social property reflecting the social distance between human nodes provides a new way to characterize the connectivity of DTNs. taking full use of communities and social ties performed by humans, we introduce a weighted community graph model to assist message transmissions. For each node pair a and b of the same community, we use the statistical analysis methods to compute the social tie strength Ia, b For this purpose, each node maintains a matrix MaCDT as illustrated in Formula (7), where Rka, b denotes the kth sample of contact duration between node a and node b within a period of time T. B characterized by the total number of stable connection times between node a and b is calculated using

ÁÁÁ k ÁÁÁ n
Á Á Á Cj
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