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Títol: Proposal and evaluation of a grouping mechanism for IEEE 802.11ah


Director/a: LÓPEZ AGUILERA, ELENA

Departament: ENTEL

Títol: Proposal and evaluation of a grouping mechanism for IEEE 802.11ah

Data inici oferta: 03-02-2020     Data finalització oferta: 03-10-2020



Estudis d'assignació del projecte:
    MU MASTEAM 2015
Tipus: Individual
 
Lloc de realització: EETAC
 
Segon director/a (UPC): GARCÍA VILLEGAS, EDUARD
 
Paraules clau:
IEEE 802.11ah, Wi-Fi HaLow, RAW, Genetic Algorithm, IoT, LPWAN
 
Descripció del contingut i pla d'activitats:
EEE 802.11ah is the basis for the Wi-Fi specification on IoT. It includes PHY and MAC features that differentiate this amendment from the mainstream specifications. One important characteristic of IoT environments is the increased amount of devices connected to the network. In order to reduce collisions and improve the overall performance, one key feature of the IEEE 802.11ah specification is the RAW mechanism, which allows splitting the connected devices into different contention groups. However, how to group STAs is not defined in the standard, being that topic open for research. In this Master Thesis, new grouping mechanisms will be proposed and studied. Analytical and simulation tools will be employed for its development and validation.
 
Overview (resum en anglès):
The aim of this project is to implement in Python a grouping algorithm for stations using new RAW (Restricted Access Window) medium access feature in the promising Wi-Fi standard for IoT, IEEE 802.11ah, or commonly known as Wi-Fi HaLow.

The race for IoT has put in the market several technologies classified as LPWAN (Low-Power Wide-Area Networks), all of them trying to achieve appropriate results in coverage range, throughput, battery life, energy consumption and scalability. Firstly, a competitive review of all of them was done to support the performance of Wi-Fi HaLow, acclaiming its key features. Secondly, a more detailed analysis of this technology was carried out, where RAW feature was introduced. This channel access mechanism seeks to reduce the collisions making the channel more efficient. However, the IEEE 802.11ah standard does not specify how to properly group the stations in order to make the most of the RAW feature.

For this last reason, we built a Python program based on IEEE 802.11ah analytical models, capable to derive the throughput of input group stations. These results were used later as a metric for an algorithm based on the theory of natural evolution, known as genetic algorithm.

Last phase of the research was focused on the tuning of the algorithm in order to select the most appropriate input parameters and the validation of the whole system by using different clusters of unordered stations. It was successfully proven the effectiveness of the algorithm as it was grouping the stations on the appropriate groups to achieve the best performance indicators, such as throughput and consequently helping to avoid collisions and retransmissions in the channel, directly reducing the energy consumption.


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