Restricted Access Window-Based Resource Allocation Scheme for Performance Enhancement of IEEE 802.11ah Multi-Rate IoT Networks

IEEE 802.11ah (Wi-Fi HaLow) is introduced to support Internet of things (IoT) applications. In this paper, we consider the IEEE 802.11ah multi-rate IoT network where stations (STAs) contend for channel access using group-based restricted access window (RAW) mechanism with the default uniform grouping scheme. RAW divides the channel time into various slots, STAs into different groups and assigns a dedicated RAW slot to each group of STAs. As uniform grouping scheme performs grouping on random basis, each group will comprise of distinct data rate STAs, which leads to severe degradation of throughput because of performance anomaly. We propose a novel RAW based resource allocation (RAW-RA) scheme which groups the STAs in a multi-rate IoT network based on data rates and allocates the RAW slots to the groups proportional to their data rates. We also develop an accurate analytical model with unsaturated traffic conditions for calculating the throughput and energy efficiency of the IEEE 802.11ah multi-rate IoT network with the RAW mechanism. From the analytical results, it is evident that the proposed RAW-RA scheme can resolve the performance anomaly as well as significantly improves the aggregate throughput and energy efficiency of the IoT network. Finally, all the analytical results are validated with extensive simulation studies