Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models

TL;DR

This paper introduces Epcast, a novel epidemic spreading model-based method for controlled information dissemination in human-based wireless networks, optimizing performance by leveraging network structure.

Contribution

It applies epidemic models to human networks for automatic tuning of dissemination, reducing overhead and improving efficiency compared to heuristic approaches.

Findings

Model-based tuning improves dissemination efficiency.

Evaluation with real human contact data validates approach.

Reduces transmission overhead in human-based wireless networks.

Abstract

Epidemics-inspired techniques have received huge attention in recent years from the distributed systems and networking communities. These algorithms and protocols rely on probabilistic message replication and redundancy to ensure reliable communication. Moreover, they have been successfully exploited to support group communication in distributed systems, broadcasting, multicasting and information dissemination in fixed and mobile networks. However, in most of the existing work, the probability of infection is determined heuristically, without relying on any analytical model. This often leads to unnecessarily high transmission overheads. In this paper we show that models of epidemic spreading in complex networks can be applied to the problem of tuning and controlling the dissemination of information in wireless ad hoc networks composed of devices carried by individuals, i.e., human-based networks. The novelty of our idea resides in the evaluation and exploitation of the structure of the underlying human network for the automatic tuning of the dissemination process in order to improve the protocol performance. We evaluate the results using synthetic mobility models and real human contacts traces.