eprintid: 3013 rev_number: 7 eprint_status: archive userid: 2 dir: disk0/00/00/30/13 datestamp: 2022-07-27 23:30:08 lastmod: 2023-07-12 23:30:21 status_changed: 2022-07-27 23:30:08 type: article metadata_visibility: show creators_name: Lin, Xi creators_name: Wu, Jun creators_name: Bashir, Ali Kashif creators_name: Yang, Wu creators_name: Singh, Aman creators_name: AlZubi, Ahmad Ali creators_id: creators_id: creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: title: FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: none keywords: 5G; edge healthcare; proportional fairness; Internet of Medical Things abstract: Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5 G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice Quality of Service (QoS) for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5 G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot sub-problems, realizing a trade-off between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve non-convex fair sub-problems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme. date: 2022-12 publication: IEEE Transactions on Industrial Informatics pagerange: 1-10 id_number: doi:10.1109/TII.2022.3183000 refereed: TRUE issn: 1551-3203 official_url: http://doi.org/10.1109/TII.2022.3183000 access: close language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Cerrado Inglés Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5 G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice Quality of Service (QoS) for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5 G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot sub-problems, realizing a trade-off between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve non-convex fair sub-problems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme. metadata Lin, Xi; Wu, Jun; Bashir, Ali Kashif; Yang, Wu; Singh, Aman y AlZubi, Ahmad Ali mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR (2022) FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things. IEEE Transactions on Industrial Informatics. pp. 1-10. ISSN 1551-3203