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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks.

Eduard Garcia-Villegas¹, Alejandro Lopez-Garcia², Elena Lopez-Aguilera¹

¹Department of Network Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain.

Sensors (Basel, Switzerland)

|January 21, 2023

Summary

This study introduces a genetic algorithm (GA) for optimizing the Restricted Access Window (RAW) in IEEE 802.11ah networks. The GA-based grouping strategy enhances throughput and fairness for heterogeneous Internet of Things (IoT) devices.

Keywords:

IEEE 802.11ah RAW Wi-Fi HaLow genetic algorithm

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Published on: December 12, 2012

Area of Science:

Computer Science
Electrical Engineering
Wireless Communication

Background:

The IEEE 802.11ah standard targets Internet of Things (IoT) applications.
The Restricted Access Window (RAW) mechanism in IEEE 802.11ah aims to reduce contention and improve energy efficiency by scheduling station transmissions.
Optimal configuration of RAW parameters is not specified, posing a challenge for network performance.

Purpose of the Study:

To propose a genetic algorithm (GA)-based grouping strategy for optimizing RAW parameters in IEEE 802.11ah networks.
To address the challenge of heterogeneous stations with varying Modulation and Coding Schemes (MCS).
To improve system throughput and fairness in IEEE 802.11ah networks.

Main Methods:

A genetic algorithm (GA) is developed for grouping stations under the RAW mechanism.
A fitness function is defined by combining predicted system throughput and fairness.
GA parameters are tuned for optimal results and efficient convergence.
The proposed strategy is evaluated through simulations and tested on a Raspberry Pi 3B+.

Main Results:

The GA-based grouping strategy effectively optimizes RAW parameters for heterogeneous IEEE 802.11ah networks.
The method demonstrates convergence in approximately 5 seconds on a Raspberry Pi 3B+.
Comparisons show the proposed mechanism achieves a favorable balance between throughput and fairness.

Conclusions:

The developed GA-based grouping strategy offers an effective solution for optimizing IEEE 802.11ah RAW mechanisms.
This approach enhances performance in IoT scenarios with diverse station capabilities.
The study validates the practical applicability and efficiency of the proposed method.