# Fairness-aware Crowdsourcing of IoT Energy Services

arXiv: https://arxiv.org/abs/2111.06064 · HTML: https://mlsanity.com/paper/2111.06064/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2021.

## Built on by (most cited first, 5 of 5)

- [Fair Concurrent Training of Multiple Models in Federated Learning](https://mlsanity.com/paper/2404.13841/) (2024)
- [Monitoring Efficiency of IoT Wireless Charging](https://mlsanity.com/paper/2303.05629/) (2023)
- [IMAP: Individual huMAn mobility Patterns visualizing platform](https://mlsanity.com/paper/2209.03615/) (2022)
- [DIY-IPS: Towards an Off-the-Shelf Accurate Indoor Positioning System](https://mlsanity.com/paper/2209.03613/) (2022)
- [Energy Loss Prediction in IoT Energy Services](https://mlsanity.com/paper/2305.10238/) (2023)

## Abstract (arXiv)

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.