# Opportunistic Data Ferrying in Areas with Limited Information and Communications Infrastructure

## TL;DR

This paper introduces an ensemble-based opportunistic data ferrying algorithm that leverages existing vehicles to reduce data delivery delays in smart city environments, demonstrated through real-world taxi trace experiments.

## Contribution

It proposes a novel ensemble online hiring algorithm for vehicle-based data ferrying, improving delay performance over existing methods in urban smart city scenarios.

## Key findings

- Reduces data delivery delay by up to 258% compared to baseline algorithms.
- Effectively utilizes real-world taxi traces for empirical evaluation.
- Outperforms four state-of-the-art online hiring algorithms.

## Abstract

Interest in smart cities is rapidly rising due to the global rise in urbanization and the wide-scale instrumentation of modern cities. Due to the considerable infrastructural cost of setting up smart cities and smart communities, researchers are exploring the use of existing vehicles on the roads as "message ferries" for the transport data for smart community applications to avoid the cost of installing new communication infrastructure. In this paper, we propose an opportunistic data ferry selection algorithm that strives to select vehicles that can minimize the overall delay for data delivery from a source to a given destination. Our proposed opportunistic algorithm utilizes an ensemble of online hiring algorithms, which are run together in passive mode, to select the online hiring algorithm that has performed the best in recent history. The proposed ensemble based algorithm is evaluated empirically using real-world traces from taxies plying routes in Shanghai, China, and its performance is compared against a baseline of four state-of-the-art online hiring algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm can reduce the overall delay compared to the baseline by an impressive 13% to 258%.