# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 1 Introduction  
The Internet of Things (IoT) aims to provide connectivity among a huge number of \"things\" anytime and anywhere. This will highly impact every aspect of the world we live in, including economics, politics, and social life. Emerging IoT applications and services, such as smart meters, environmental/agricultural monitoring and automation of industrial processes, require myriads of battery-powered smart things (e.g., sensors, actuators, controllers) connected together in an energy efficient way. Currently, there are two categories of low-power IoT communication technologies: wireless personal area network (WPAN) and low-power wide area network (LPWAN) technologies. Within short transmission range (i.e., tens of meters), WPAN technologies (e.g., Bluetooth Low Energy, ZigBee) provide relatively high throughput (i.e., up to a few hundred kilobits per second), while long-range communications (i.e., up to several kilometers) can be supported by LPWAN technologies (e.g., LoRA, SigFox) at much lower throughput (i.e., up to at most a few kilobits per second). As the transmission range of the WPAN technologies is too short and throughput of the LPWAN technologies is too low, both of them can only be applied in a limited set of IoT scenarios. Therefore, a gap still exists for a low-power IoT communication technology that offers sufficient throughput over longer ranges. The recently released IEEE 802.11 ah Wi-Fi standard, marketed as Wi-Fi HaLow, fills this gap. It operates in the unlicensed sub-1 GHz frequency bands (e.g., 863-868 MHz in Europe, 755-787 MHz in China and 902-928 MHz in North-America). Similar to previous Wi-Fi standards, it supports several modulation and coding schemes (MCS) in order to offer a trade-off between throughput, range and energy efficiency. This allows it to support transmission ranges from 100 m up to 1 km with data rates between 0.15 Mbps and 346.67 Mbps. Thus, IEEE 802.11 ah has the potential to achieve much higher transmission ranges than existing WPAN and much higher throughput than both WPAN and LPWAN technologies. On the MAC (Media Access Control) layer, in order to increase efficiency in the face of a large number of densely deployed, energy constrained stations, several innovative mechanisms are introduced, including hierarchical organization, short MAC header, restricted access window (RAW), traffic indication map (TIM) segmentation and target wake time (TWT). The RAW feature aims to increase scalability in dense IoT networks, where a large number of stations connect to a single access point (AP). With RAW, stations are divided into groups, limiting simultaneous channel access to one group. Therefore, the collision probability for upstream traffic is highly reduced. However, the grouping strategy, which decides how to split stations among groups, is not mentioned in the standard. Concretely, a station grouping algorithm should be implemented at the AP side to determine the number of RAW groups, the duration of each group, and how to divide stations among them. Furthermore, these parameters should be dynamically adapted by the AP between consecutive beacon intervals. In previous work, we conducted an in-depth analysis of the influence of network and traffic conditions on the optimal station grouping parameters [1]. We concluded that the optimal parameters depend on a wide range of network variables, such as number of stations, network load and traffic patterns. This shows the need for dynamic station grouping algorithms that determine the optimal station grouping parameters based on the current network and traffic conditions. As these conditions change, the algorithm should similarly adapt. In this article, we present a novel real-time station grouping algorithm that adapts the RAW parameters based on the current (estimated) traffic conditions, optimized for sensor networks with mainly upstream traffic. It improves upon the state of the art in three ways.