Fairness-aware Crowdsourcing of IoT Energy Services

TL;DR

This paper introduces FACES, a fairness-aware framework for crowdsourcing IoT energy services that improves energy utilization and fairness among requests compared to traditional methods.

Contribution

The paper presents a novel framework, FACES, which enhances fairness and efficiency in crowdsourced IoT energy service provisioning.

Findings

FACES outperforms FCFS in energy utilization.

FACES achieves similar utilization to Max-min fair scheduling.

FACES provides better fairness among requests.

Abstract

We propose a Novel Fairness-Aware framework for Crowdsourcing Energy Services (FACES) to efficiently provision crowdsourced IoT energy services. Typically, efficient resource provisioning might incur an unfair resource sharing for some requests. FACES, however, maximizes the utilization of the available energy services by maximizing fairness across all requests. We conduct a set of preliminary experiments to assess the effectiveness of the proposed framework against traditional fairness-aware resource allocation algorithms. Results demonstrate that the IoT energy utilization of FACES is better than FCFS and similar to Max-min fair scheduling. Experiments also show that better fairness is achieved among the provisioned requests using FACES compared toFCFS and Max-min fair scheduling.