Opportunistic Routing Based on Daily Routines

TL;DR

This paper introduces dLife, a routing algorithm that leverages daily human routines and dynamic social ties to improve message delivery in opportunistic networks, outperforming existing social-based methods.

Contribution

The paper presents dLife, a novel routing algorithm that models time-evolving social ties based on daily routines to enhance opportunistic routing performance.

Findings

dLife achieves higher delivery probability than existing methods.

dLife reduces latency and cost in message delivery.

Experimental results validate dLife's effectiveness on real and synthetic data.

Abstract

Opportunistic routing is being investigated to enable the proliferation of low-cost wireless applications. A recent trend is looking at social structures, inferred from the social nature of human mobility, to bring messages close to a destination. To have a better picture of social structures, social-based opportunistic routing solutions should consider the dynamism of users' behavior resulting from their daily routines. We address this challenge by presenting dLife, a routing algorithm able to capture the dynamics of the network represented by time-evolving social ties between pair of nodes. Experimental results based on synthetic mobility models and real human traces show that dLife has better delivery probability, latency, and cost than proposals based on social structures.