Abstract

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon.

Highlights

  • Nowadays, there is great interest in developing indoor localization algorithms making use of the latest developments on low-power wireless technologies

  • Since our goal is to develop the localization scheme based on a classification algorithm, we explore the benefits of setting the transmission power setting of each individual BLE4.0 beacon to improve the quality of the radio map

  • This study has revealed some useful insight on the required tool characteristics to calibrate an accurate BLE4.0-assisted indoor localization mechanism

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Summary

Introduction

There is great interest in developing indoor localization algorithms making use of the latest developments on low-power wireless technologies. Once the best device (in terms of the accuracy performance) has been selected, we study the relevance of every BLE4.0 beacon in our experimental environment From this analysis, we conclude that an ad hoc setting of the transmission power level of the BLE4.0 beacons plays a major role on the quality of the signal fingerprint. We make use of two supervised learning algorithms to characterize the BLE4.0 beacon signal propagation These algorithms will be used for developing indoor localization mechanisms. Two main parameters are studied: (i) the contribution of each BLE4.0 beacon deployed in the environment; and (ii) the transmission power level of each BLE4.0 beacon

Related Work
Experimental Indoor Set-Up
Bluetooth Receiver’s Characteristics
Bluetooth Signal Attenuation
Supervised Learning Algorithms
On the Adequacy of the Bluetooth-Based Localization Platform
Baseline Evaluation
Asymmetric Transmission Power
Fingerprint as a Function of the Transmission Power
On Deriving the Best Asymmetric Transmission Power Setting
Asymmetric Transmission Power Setting
On the Relevance of the Individual RSSI Values
On Mitigating the Multipath Fading Impairment
Findings
Conclusions
Full Text
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