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Indoor WiFi path loss model to estimate indoor network coverage considering residential design

Spencer Ii Ern Teo (Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore)
Yuhan Zhou (Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore)
Justin Ker-Wei Yeoh (Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore)

Smart and Sustainable Built Environment

ISSN: 2046-6099

Article publication date: 15 January 2024

65

Abstract

Purpose

Network coverage is crucial for the adoption of advanced Smart Home applications. The commonly used log-based path loss model is not able to accurately estimate WiFi signal strength in different houses, as it does not fully consider the impact of building morphology. To better describe the propagation of WiFi signals and achieve higher estimation accuracy, this paper studies the basic building morphology characteristics of houses.

Design/methodology/approach

A new path loss model based on a decision tree was proposed after measuring the WiFi signal strength passing through multiple housing units. Three types of regression models were tested and compared.

Findings

The findings demonstrate that the log-based path loss model fits small houses well, while the newly proposed nonlinear path loss model performs better in large houses (area larger than 125 m2 and area-to-perimeter ratio larger than 2.5). The impact of building design on path loss has been proven and specifically quantified in the model.

Originality/value

Proposed an improved model to estimate indoor network coverage. Quantify the impacts of building morphology on indoor WiFi signal strength. Improve WiFi signal strength estimation to support Smart Home applications.

Keywords

Citation

Teo, S.I.E., Zhou, Y. and Yeoh, J.K.-W. (2024), "Indoor WiFi path loss model to estimate indoor network coverage considering residential design", Smart and Sustainable Built Environment, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SASBE-05-2023-0131

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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