Next Article in Journal
A Modified Differential Coherent Bit Synchronization Algorithm for BeiDou Weak Signals with Large Frequency Deviation
Previous Article in Journal
Gas Sensors Based on Molecular Imprinting Technology
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(7), 1559;

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

IDLab, Department of Mathematics and Computer Science, University of Antwerp—imec, 2020 Antwerp, Belgium
Network Protocols Research Lab, Institute for Information Transmission Problems, Russian Academy of Sciences, 127051 Moscow, Russia
Author to whom correspondence should be addressed.
Received: 12 May 2017 / Revised: 22 June 2017 / Accepted: 29 June 2017 / Published: 4 July 2017
(This article belongs to the Section Sensor Networks)
Full-Text   |   PDF [1420 KB, uploaded 4 July 2017]   |  


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. View Full-Text
Keywords: IEEE 802.11ah; dense IoT networks; restricted access window (RAW); real-time RAW optimization; dynamic traffic IEEE 802.11ah; dense IoT networks; restricted access window (RAW); real-time RAW optimization; dynamic traffic

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Tian, L.; Khorov, E.; Latré, S.; Famaey, J. Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah. Sensors 2017, 17, 1559.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top