Title: Measurement and analysis of RSS in IoT using Bluetooth mesh networks

Authors: Xiaobin Wu; Dan Luo

Addresses: Department of Business, Southwest Jiaotong University Hope College, Chengdu, Sichuan, 610000, China ' Sichuan Polytechnic Technician College, Chengdu, Sichuan, 611130, China

Abstract: People who work with Industry 4.0 need to be able to think quickly and find things by looking inside things. You can keep track of the steps each thing goes through as it's being made by putting parts and other things in the right places. In other words, someone can see something being made. Since GPS will not let you in, you'll need to come up with a new plan. We can think of a new way to quickly find things inside with beacons that have a received signal strength (RSS) indicator (RSSI). The study led to this plan. You can use more than one kind of machine learning at the same time to do this. These include convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and regressors. They need to be trained on test beds in the real world. These are places where you can see how well each method works. We now know which ones work best, are easy to use, and help us find things inside. A live feed will be shown as the last test to see how well the translation works. From what we saw, the K-Nearest Neighbours method works the best. You now know how things stand and how much money your company really makes.

Keywords: technology: Bluetooth; localisation improvement; optimal K for KNN and WKNN; high-level architecture Bluetooth mesh profile specification.

DOI: 10.1504/IJMNDI.2025.151008

International Journal of Mobile Network Design and Innovation, 2025 Vol.11 No.4, pp.177 - 189

Received: 22 Jan 2025
Accepted: 26 Mar 2025
Published online: 08 Jan 2026 *

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