Restricted Access Window Slots Research Articles

Energy and spectrum efficient retransmission scheme with RAW optimization for IEEE 802.11ah networks

Restricted Access Window (RAW) and group-based media access are new features that are exploited by IEEE 802.11ah standard to resolve massive access problem in Internet of Things (IoT) applications. Nevertheless, inefficient device grouping and inappropriate contention resolution in a RAW may still result in collisions and consequently lead to degradation of QoS, energy efficiency, and channel utilization. In existing works, contention resolution schemes schedule all failed devices to retransmit in the next slot of the RAW, which increases the probability of collisions. In this paper, we propose a new retransmission scheme that allows the collided devices of each RAW slot to have another transmission chance in one of the next µ upcoming slots, randomly. We represent the proposed retransmission method via a probabilistic model and formulate a problem based on it, to adjust the number of slots of each RAW with the aim of increasing overall energy efficiency and overall channel utilization regarding delay constraint of devices. Solving the formulated problem, a meta-heuristic algorithm is used to adjust the number of RAW slots and so the size of each RAW regarding the results of the device grouping algorithm. To better exploit the capabilities of the proposed idea, we suggest a load-aware and distance-based device grouping algorithm that not only considers the hidden node problem, but also attends to the load balance of the groups. Simulation results show that the proposed retransmission and RAW adjustment scheme alongside the proposed device grouping algorithm, improve energy efficiency and channel utilization by 25 % and 17 % respectively, and reduce the access delay by 31 % in average, compared to the previous retransmission method.

Load-Aware Channel Allocation for IEEE 802.11ah-Based Networks

Most wireless communication technologies addressed to the Internet of Things (IoT) applications face the bottleneck of dense and large-scale use cases. One solution to this problem is a periodic channel reservation strategy, in which only a small group of stations can compete for channel access during a given period. The IEEE 802.11ah standard, a.k.a. Wi-Fi HaLow, deploys this idea in its channel access protocol, named Restricted Access Window (RAW). A single RAW consists of one or more RAW slots during which only designated stations can contend for channel access. This paper considers an IEEE 802.11ah-based network with randomly distributed stations around the Access Point (AP), operating under a Rayleigh-fading channel with capture enabled. We develop an analytical model to evaluate the contention of a group of stations and propose a Load-Aware Channel Allocation (LACA) algorithm for the RAW slot period. The LACA algorithm ensures the delivery of all packets that designated stations carry, allowing the allocation of load-aware RAW slots, which is effective in enhancing the Age of Information (AoI). We evaluate the Packet Delivery Ratio (PDR) and channel usage within a pre-allocated RAW slot to prove the effectiveness of our proposal. We further study the impact of the spatial distribution of the stations around the AP and the capture effect under a Rayleigh channel on the performance of the proposed LACA algorithm. Extensive simulations are used to validate our analytical results. Our proposal provides a load-aware and adaptive channel allocation scheme based on the dynamic conditions of the network. Our model can be implemented in a global configuration scheme for the RAW mechanism in heterogeneous networks or for alternative communication technologies that address dense scenarios with the integration of periodic channel reservations.

An Analytical Model for the Aggregate Throughput of IEEE 802.11ah Networks under the Restricted Access Window Mechanism.

The IEEE 802.11ah is an amendment to the IEEE 802.11 standard to support the growth of the Internet of Things (IoT). One of its main novelties is the restricted access window (RAW), which is a channel access feature designed to reduce channel contention by dividing stations into RAW groups. Each RAW group is further divided into RAW slots, and stations only attempt channel access during the RAW slot they were assigned to. In this paper, we propose a discrete-time Markov chain model to evaluate the average aggregate throughput of IEEE 802.11ah networks using the RAW mechanism under saturated traffic and ideal channel conditions. The proposed analytical model describes the behavior of an active station within its assigned RAW slot. A key aspect of the model is the consideration of the event of RAW slot time completion during a station’s backoff operation. We study the average aggregate network throughput for various numbers of RAW slots and stations in the network. The numerical results derived from our analytical model are compared to computer simulations based on an IEEE 802.11ah model developed for the ns-3 simulator by other researchers, and its performance is also compared to two other analytical models proposed in the literature. The presented results indicate that the proposed analytical model reaches the closest agreement with independently-derived computer simulations.

Service Differentiation in IEEE 802.11ah WLAN under Restricted Access Window based MAC protocol

The restricted access window (RAW) based channel access scheme proposed for IEEE 802.11ah wireless LAN (WLAN) divides the stations (STAs) in the network into groups and allocates RAW slots for each group of STAs. In this way, the STAs belonging to each group can attempt to access the medium during their designated RAW slot by invoking the enhanced distributed channel access (EDCA) protocol. This paper describes an analytical model to evaluate the saturation throughput of IEEE 802.11ah WLAN under the RAW based scheme, when STAs carry traffic of distinct access categories (ACs) and use the EDCA protocol for medium sharing. However, when groups are formed by randomly selecting the STAs, i.e., if random grouping (RG) scheme is employed, each group will contain STAs carrying traffic of distinct ACs. This will lead to starvation and degraded throughput performance for the low priority class STAs, since they have to contend with high priority class STAs within the same group during the RAW slot assigned. To resolve this issue, we propose an algorithm to realize priority class based grouping (PCG), in which the STAs belonging to the same ACs are grouped together. We also describe an analytical model to evaluate the saturation throughput of the network under the proposed PCG scheme. We establish that saturation throughput of the network and the relative service differentiation ratio of the lower priority class STAs can be significantly improved under the proposed PCG scheme compared to the conventional RG scheme.

Restricted Access Window-Based Resource Allocation Scheme for Performance Enhancement of IEEE 802.11ah Multi-Rate IoT Networks

IEEE 802.11ah (Wi-Fi HaLow) is introduced to support Internet of things (IoT) applications. In this paper, we consider the IEEE 802.11ah multi-rate IoT network where stations (STAs) contend for channel access using group-based restricted access window (RAW) mechanism with the default uniform grouping scheme. RAW divides the channel time into various slots, STAs into different groups and assigns a dedicated RAW slot to each group of STAs. As uniform grouping scheme performs grouping on random basis, each group will comprise of distinct data rate STAs, which leads to severe degradation of throughput because of performance anomaly. We propose a novel RAW based resource allocation (RAW-RA) scheme which groups the STAs in a multi-rate IoT network based on data rates and allocates the RAW slots to the groups proportional to their data rates. We also develop an accurate analytical model with unsaturated traffic conditions for calculating the throughput and energy efficiency of the IEEE 802.11ah multi-rate IoT network with the RAW mechanism. From the analytical results, it is evident that the proposed RAW-RA scheme can resolve the performance anomaly as well as significantly improves the aggregate throughput and energy efficiency of the IoT network. Finally, all the analytical results are validated with extensive simulation studies.

ANN-based optimization framework for performance enhancement of Restricted Access Window mechanism in dense IoT networks

IEEE 802.11ah, marketed as Wi-Fi HaLow, operates at Sub 1 GHz spectrum to provide broad coverage, high throughput, energy efficiency and scalability. This makes IEEE 802.11ah a promising candidate for the Internet of Things (IoT). One of the major enhancements in the MAC layer is the Restricted Access Window (RAW) mechanism, which focuses on mitigating the channel contention in dense networks. The RAW mechanism reduces the channel contention among the group of devices by restricting their channel access to the allocated RAW slots. Since the standard does not specify the optimal RAW configuration parameters, choosing the number of RAW slots has significant impact on the performance of the RAW mechanism. In this paper, we develop an optimization framework by exploiting the Multilayer Perceptron Artificial Neural Network (MLP-ANN) to find the optimal number of RAW slots that can maximize the performance of the RAW mechanism in terms of throughput, delay and energy consumption. We train the ANN using the network size, Modulation and Coding Schemes, duration of the RAW period and the optimal number of RAW slots found using the analytical model presented in this paper. Further, we evaluate the performance of the RAW mechanism by choosing the optimal number of RAW slots provided by the ANN-based optimization framework. Results show that the proposed scheme significantly enhances the performance of the RAW mechanism. Finally, the analytical results are corroborated using extensive simulations done in ns-3.

Restricted Access Window-Based Novel Service Differentiation Scheme for Group-Synchronized DCF

Group-synchronized distributed coordination function (GS-DCF) is a group-based medium access mechanism introduced in the IEEE 802.11ah draft standard. GS-DCF implements a novel channel access mechanism called restricted access window (RAW) which splits the channel time into various slots, devices into several groups, and assigns each slot to a group of devices. The Internet of Things is an ideal platform that captures the process and handles a large amount of data from the various heterogeneous applications with different channel access requirements. In this letter, we propose a novel RAW-based service differentiation scheme for GS-DCF. We propose a technique to allocate RAW slots to different applications based on their service requirements with minimal additional overhead. Analytical and simulation results show that the proposed scheme can effectively provide differentiated services in terms of throughput to the group of devices in GS-DCF.

Enabling the Internet of Things With Wi-Fi Halow—Performance Evaluation of the Restricted Access Window

IEEE 802.11ah, a new amendment to the Wi-Fi standard, adapts Wi-Fi networks to the emerging Internet of Things (IoT). A key component of .11ah is the Restricted Access Window (RAW), a new channel access mechanism, which reduces contention when even thousands of IoT devices operate in the same area by assigning them different channel times. This paper shows that existing studies incorrectly understand the RAW behavior, oversimplify its modeling and thereby overestimate the real system throughput in several times, especially for short durations of the reserved RAW slots. The core contribution of this paper is a new mathematical model based on a completely different approach, which yields more accurate results and thereby enables better IoT system dimensioning. The developed model is suitable for many scenarios typical for IoT. It allows finding RAW parameters that optimize system performance in terms of throughput, power consumption, and packet loss ratio. The proposed solution is can be used for various traffic patterns: when each device transmits a single packet, a batch of packets of random size, or it has full-buffer traffic.

Performance Analysis on IEEE 802.11ah Standard with Enhanced Distributed Channel Access Mechanism

IEEE 802.11ah is a new task group on the IEEE 802.11 standard designed to work on the 900 MHz. It is with a range of communication coverage up to 1kilometer, lower energy consumption, and up to 8191 stations. There are two types of STAs in 802.11ah: sensor type to support sensor service and non-sensor type for offload service. In this research, it only focuses on non-sensor STA. For non-sensor STA, maximizing throughput is more important than power consumption. This research aims to see the performance of IEEE 802.1 1ah with Enhanced Distributed Channel Access (EDCA). To achieve that purpose, a mechanism is needed to provide guarantees various services required by theSTA. EDCA is an access mechanism used to set the Quality of Service (QoS) for the IEEE 802.11 standard through modifications in MAC layer. In this research,it focuses on one of the EDCA parameters, Arbitration Inter-Frame Space (AIFS). In addition, this research also focuses on the 802.11ah feature is Restricted Access Window (RAW) by changing the number of the RAW groups. From the results of the research, it is found that the improvement scheme with Arbitration Inter-Frame Space Number (AIFSN) value AC BK = 2, AC BE = 1, AC VI = 1, AC VO = 1 has better performance compared to the default scheme with AIFSN value AC BK = 7, AC BE = 3, AC VI = 2, AC VO = 2) with an average throughput of 1.504598 Mbps, average overall delay of 0.066242 second and average PDR of 62%. In addition, changes in the number of RAW groups and RAW slots affect network performance. This feature can improve the value of throughput, average delay, and Packet Delivery Ratio. The goals of this research is to know the effect of AIFSN value changes on AIFSN parameters, variation of RAW group and RAW slot number to throughput,average delay and packet delivery ratio.

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 Analysis of Group-Synchronized DCF for Dense IEEE 802.11 Networks

In dense IEEE 802.11 networks, improving the efficiency of contention-based media access control is an important and challenging issue. Recently, the IEEE802.11ah Task Group has discussed a group-synchronized distributed coordination function (GS-DCF) for densely deployed wireless networks with a large number of stations. By using the restricted access window (RAW) and RAW slots, the GS-DCF is anticipated to improve the throughput substantially, primarily due to relieving the channel contention. However, optimizing the MAC configurations for the RAW, i.e., the number and duration of RAW slots, is still an open issue. In this paper, we first build an analytical model to track the performance of the GS-DCF in saturated 802.11 networks. Then, we study and compare the GS-DCF throughput using both centralized and decentralized grouping schemes. The accuracy of our model has been validated with simulation results. It is observed that the GS-DCF obtains a throughput gain of seven times or more over DCF in a network of 512 or more stations. Moreover, it is demonstrated that the decentralized grouping scheme can be implemented with a small throughput loss when compared with the centralized grouping scheme.