Le Tian

Evaluation of the IEEE 802.11ah Restricted Access Window mechanism for dense IoT networks

IEEE 802.11ah is a new Wi-Fi draft for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT): connectivity among a large number of power-constrained stations deployed over a wide area. The new Restricted Access Window (RAW) mechanism promises to increase throughput and energy efficiency by dividing stations into different RAW groups. Only the stations in the same group can access the channel simultaneously, which reduces collision probability in dense scenarios. However, the draft does not specify any RAW grouping algorithms, while the grouping strategy is expected to severely impact RAW performance. To study the impact of parameters such as traffic load, number of stations and RAW group duration on optimal number of RAW groups, we implemented a sub-1Ghz PHY model and the 802.11ah MAC protocol in ns-3 to evaluate its transmission range, throughput, latency and energy efficiency in dense IoT network scenarios. The simulation shows that, with appropriate grouping, the RAW mechanism substantially improves throughput, latency and energy efficiency. Furthermore, the results suggest that the optimal grouping strategy depends on many parameters, and intelligent RAW group adaptation is necessary to maximize performance under dynamic conditions. This paper provides a major leap towards such a strategy.

Implementation and Validation of an IEEE 802.11ah Module for ns-3

IEEE 802.11ah or HaLow is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things: connectivity among a large number of power-constrained stations deployed over a wide area. Existing research on the performance evaluation of 802.11ah is generally based on analytical models, which does not accurately represent real network dynamics and is hard to adjust to different network conditions. To address this hiatus, we implemented the 802.11ah physical and MAC layer in the ns-3 network simulator, which, compared to analytical models, more closely reflects actual protocol behavior and can more easily be adapted to evaluate a broad range of network and traffic conditions. In this paper, we present the details of our implementation, including a sub-1Ghz physical layer model and several novel MAC layer features. Moreover, simulations based on the implemented model are conducted to evaluate performance of the novel features of IEEE 802.11ah.

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

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.

Performance Evaluation of IEEE 802.11ah Networks With High-Throughput Bidirectional Traffic

So far, existing sub-GHz wireless communication technologies focused on low-bandwidth, long-range communication with large numbers of constrained devices. Although these characteristics are fine for many Internet of Things (IoT) applications, more demanding application requirements could not be met and legacy Internet technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP) could not be used. This has changed with the advent of the new IEEE 802.11ah Wi-Fi standard, which is much more suitable for reliable bidirectional communication and high-throughput applications over a wide area (up to 1 km). The standard offers great possibilities for network performance optimization through a number of physical- and link-layer configurable features. However, given that the optimal configuration parameters depend on traffic patterns, the standard does not dictate how to determine them. Such a large number of configuration options can lead to sub-optimal or even incorrect configurations. Therefore, we investigated how two key mechanisms, Restricted Access Window (RAW) grouping and Traffic Indication Map (TIM) segmentation, influence scalability, throughput, latency and energy efficiency in the presence of bidirectional TCP/IP traffic. We considered both high-throughput video streaming traffic and large-scale reliable sensing traffic and investigated TCP behavior in both scenarios when the link layer introduces long delays. This article presents the relations between attainable throughput per station and attainable number of stations, as well as the influence of RAW, TIM and TCP parameters on both. We found that up to 20 continuously streaming IP-cameras can be reliably connected via IEEE 802.11ah with a maximum average data rate of 160 kbps, whereas 10 IP-cameras can achieve average data rates of up to 255 kbps over 200 m. Up to 6960 stations transmitting every 60 s can be connected over 1 km with no lost packets. The presented results enable the fine tuning of RAW and TIM parameters for throughput-demanding reliable applications (i.e., video streaming, firmware updates) on one hand, and very dense low-throughput reliable networks with bidirectional traffic on the other hand.

What Is the Fastest Way to Connect Stations to a Wi-Fi HaLow Network?

Wi-Fi HaLow is an adaptation of the widespread Wi-Fi technology for the Internet of Things scenarios. Such scenarios often involve numerous wireless stations connected to a shared channel, and contention for the channel significantly affects the performance in such networks. Wi-Fi HaLow contains numerous solutions aimed at handling the contention between stations, two of which, namely, the Centralized Authentication Control (CAC) and the Distributed Authentication Control (DAC), address the contention reduction during the link set-up process. The link set-up process is special because the access point knows nothing of the connecting stations and its means of control of these stations are very limited. While DAC is self-adaptive, CAC does require an algorithm to dynamically control its parameters. Being just a framework, the Wi-Fi HaLow standard neither specifies such an algorithm nor recommends which protocol, CAC or DAC, is more suitable in a given situation. In this paper, we solve both issues by developing a novel robust close-to-optimal algorithm for CAC and compare CAC and DAC in a vast set of experiments.

Extension of the IEEE 802.11ah ns-3 simulation module

IEEE 802.11ah, marketed as Wi-Fi HaLow, is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT), namely connectivity among a large number of densely deployed power-constrained stations. The standard was only published in May 2017 and hardware supporting Wi-Fi HaLow is not available on the market yet. As such, research on 802.11ah has been mostly based on mathematical and simulation models. Mathematical models generally introduce several simplifications and assumptions, which do not faithfully reflect real network conditions. As a solution, we previously developed an IEEE 802.11ah module for ns-3, publicly released in 2016. This initial release consisted of physical layer models for sub-1GHz communications and an implementation of the fast association and Restricted Access Window (RAW) channel access method. In this paper, we present an extension to our IEEE 802.11ah simulator. It contains several new features: an online RAW configuration interface, an energy state model, adaptive Modulation and Coding Scheme (MCS), and Traffic Indication Map (TIM) segmentation. This paper presents the details of our implementation, along with experimental results to validate each new feature. The extended Wi-Fi HaLow module can now support different scenarios with both uplink and downlink heterogeneous traffic, together with real-time RAW optimization, sleep management for energy conservation and adaptive MCS.

Supporting Heterogeneous IoT Traffic using the IEEE 802.11ah Restricted Access Window

IEEE 802.11ah is a new Wi-Fi standard operating on unlicensed sub-GHz frequencies. It aims to provide long-range connectivity to Internet of Things (IoT) devices. The IEEE 802.11ah restricted access window (RAW) mechanism promises to increase throughput and energy efficiency in dense deployments by dividing stations into different RAW groups and allowing only one group to access the channel at a time. In this demo, we demonstrate the ability of the RAW mechanism to support a large number of densely deployed IoT stations with heterogeneous traffic requirements. Differentiated Quality of Service (QoS) is offered to a small set of high-throughput wireless cameras that coexist with thousands of best-effort sensor monitoring stations. The results are visualized in near real-time using our own developed IEEE 802.11ah visualizer running on top of the ns-3 event-based network simulator.