Abstract
IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.
Highlights
The Internet of Things (IoT) aims to provide connectivity among a huge number of “things”anytime and anywhere
Traffic-Adaptive restricted access window (RAW) Optimization Algorithm (TAROA) Overview s of traffic-adaptive RAW optimization algorithm (TAROA) aims to solve the aforementioned problem by estimating the transmission interval tint s each station s on the access point (AP) side
We propose a novel traffic-adaptive RAW optimization algorithm (TAROA) to adjust the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks with mainly upstream traffic in which each station is assumed to transmit packets with a certain frequency
Summary
The Internet of Things (IoT) aims to provide connectivity among a huge number of “things”. 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. It is designed for dynamic and heterogeneous traffic conditions, where each station has a different packet transmission interval that may change over time It only uses information readily available on the AP side, estimating station-side variables based on available data.
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