RO-RAW: Run-Time Restricted Access Window Optimization in IEEE 802.11ah Network with Extended Kalman Filter

Zhao Liu,Peng Lv,Chaoyun Song

doi:10.1155/2020/8876669

Zhao Liu, Peng Lv + Show 1 more

Open Access

https://doi.org/10.1155/2020/8876669

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Abstract

In 2016, the IEEE task group ah (TGah) published a new standard IEEE 802.11ah, aimed at providing network connectivity among a large number of Internet of Things (IoT) devices. Restricted access window (RAW) is one of the fundamental MAC mechanisms of IEEE 802.11ah. It reduces the contention overhead in the dense wireless environment by dividing stations into different RAW groups. However, how to optimize the RAW parameters is still an open issue, especially in the run-time environment. In this paper, we propose a run-time RAW optimization scheme, namely RO-RAW, to improve the performance of RAW in the IEEE 802.11ah networks. RO-RAW adopts the Extended Kalman Filter method to estimate the channel status and adjusts the RAW parameters according to the number of competing stations in real-time. The evaluation via NS-3 simulations shows that, by tuning the RAW parameters appropriately, RO-RAW substantially improves throughput, latency, and packet loss performance compared with another RAW optimization scheme in different simulation scenarios. The results further show that, when the channel is relatively congested, RO-RAW improves the RAW performance more significantly.

Highlights

Smart manufacturing [1, 2] and Industry 4.0 production environments modernize the traditional plants and factories
The main contributions of this paper are summarized as follows: (i) We propose a run-time Restricted Access Window (RAW) parameter optimization scheme RO-RAW, which adaptively adjusts the RAW parameters to the theoretical optimal values based on the channel status (ii) In order to estimate the channel status, we adopt the Extended Kalman Filter method to obtain the current competing stations
We propose a run-time RAW optimization scheme with the extended Kalman filter, namely, RO-RAW, to improve the RAW performance in IEEE 802.11ah networks

Summary

Introduction

Smart manufacturing [1, 2] and Industry 4.0 production environments modernize the traditional plants and factories. The authors propose a run-time RAW optimization scheme, namely, RO-RAW, which adaptively optimizes the network performance by adjusting the RAW parameters according to the channel status. It first estimates the current channel status and returns the estimated station numbers by the Extended Kalman Filter (EKF) method. (i) We propose a run-time RAW parameter optimization scheme RO-RAW, which adaptively adjusts the RAW parameters to the theoretical optimal values based on the channel status (ii) In order to estimate the channel status, we adopt the Extended Kalman Filter method to obtain the current competing stations.

Background

RAW Mechanism Optimization with Extended Kalman Filter

1: Step 1

Performance Evaluation

Conclusion