Improving Aggregate Utility and Service Differentiation of IEEE 802.11ah Restricted Access Window Mechanism Using ANFIS

IEEE 802.11ah introduces the restricted access window (RAW) mechanism at the MAC layer to mitigate the effect of collisions and to improve the network performance. In an Internet of Things network with heterogeneous traffic requirements, the challenging aspect of the RAW mechanism is to choose the optimal number of RAW slots (ONRs) and to dynamically assign the RAW slots according to the transmission requirements of the devices. In this article, we propose an optimization model by using adaptive neuro-fuzzy inference system (ANFIS) to find the ONRs. The ANFIS is trained with network size, modulation and coding schemes, and the ONRs found analytically. Further, we propose a dynamic allocation of RAW slots (DARS) algorithm to classify the devices based on their traffic criteria. The proposed algorithm classifies the network using self-organizing maps and dynamically allocates the RAW slots to each group. We present a mathematical model to assess the throughput and energy consumption. The results show that the throughput performance is significantly improved and the energy consumption is considerably decreased by using the ONRs. Further, the DARS scheme effectively provides differentiated services to the group of devices in contrast to the default uniform grouping scheme. It is observed that the proposed optimization scheme significantly improves the throughput by 20% and reduces the energy consumption by 26% for a network of 4000 devices, compared to the legacy RAW mechanism. Further, the DARS scheme increases the average data transferred by the devices with the highest traffic requirements by 68%. Finally, all the analytical findings are validated by simulation studies using ns-3.