TY  - JOUR
SP  - 1
SN  - 1551-3203
ID  - uninimx3013
AV  - none
EP  - 10
N2  - 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.
UR  - http://doi.org/10.1109/TII.2022.3183000
TI  - FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things
KW  - 5G; edge healthcare; proportional fairness; Internet of Medical Things
JF  - IEEE Transactions on Industrial Informatics
A1  - Lin, Xi
A1  - Wu, Jun
A1  - Bashir, Ali Kashif
A1  - Yang, Wu
A1  - Singh, Aman
A1  - AlZubi, Ahmad Ali
Y1  - 2022/12//
ER  -