Traffic Demand-based Grouping for Fairness among the RAW Groups of Heterogeneous Stations in IEEE802.11ah IoT Networks

Abstract

The emerging technology Internet of Things (IoT) has introduced a new dimension of communications by providing connectivity in dense networks with long coverage range. Legacy IEEE 802.11 standard developed for sparse networks with short coverage range which is not suitable for IoT. To address this issue, the IEEE TGah has developed IEEE 802.11ah standard to support dense networks by introducing restricted access window (RAW) mechanism. The grouping strategy and RAW parameters of RAW mechanism have not been yet fully addressed in IEEE 802.11ah standard and are still an open issue. However, most existing grouping algorithms focus on either even distribution of stations (STAs) or maximization of channel utilization to achieve fairness among RAW groups assuming that the network is saturated which is more impractical. Fair resource allocation depends on traffic demand when the network is non-saturated. Therefore, we propose a grouping algorithm that considers the non-saturation condition of STAs to enhance fairness. Moreover, we design a mathematical model to analyze the performance of RAW mechanism for STAs with heterogeneous traffic demands. We evaluate the proposed grouping algorithm via analytical model which has been solved by Maple. Our traffic demand-based grouping algorithm outperforms other existing grouping algorithms, particularly for non-saturated networks.