MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

Jure Tuta,Matjaz Juric

doi:10.3390/s18040963

Abstract

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

Highlights

Internet of Things (IoT) platform predicts ubiquitous systems with every device being connected to the network to communicate with the surrounding devices
We utilize International Telecommunication Union (ITU) propagation model, which we have extended with β parameter and can be seen in (3); PLi,j stands for path loss between APi and selected point j
We have presented MFAM, which is a novel indoor localization method, which utilizes different wireless frequencies to improve the localization accuracy

Summary

Introduction

Internet of Things (IoT) platform predicts ubiquitous systems with every device being connected to the network to communicate with the surrounding devices. One of the most important properties of the IoT device, which is often beneficial to the other devices, is the device’s precise position in the indoor space. Most of the indoor localization methods based on wireless RF signals utilize Wi-Fi network, because it is commonly available indoors to provide wireless Internet access. Usage of the 2.4 GHz Wi-Fi for indoor localization usually requires additional devices to be installed; for the Internet access usually one access point (AP) in the medium-sized apartment suffices, for the implementation of the indoor localization at least three APs are required due to the triangulation principle of the indoor localization methods. Other signals in the indoor space should be considered for the localization process; nowadays, higher frequency 5 GHz Wi-Fi is commonly

Methods

Results

Discussion

Conclusion