2021/11/11 by Abdallah Lakhdari, Lakhdari, Abdallah, Athman Bouguettaya +1
Computer Science · Engineering · #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Mobile Crowdsensing and Crowdsourcing #Parallel #Smart Grid Energy Management #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2111.06064
openalex publication_date 2021/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.