Combining RSSI and Accelerometer Features for Room-Level Localization.

Athina Tsanousa,Stefanos Vrochidis,Vasileios-Rafail Xefteris,Ioannis Kompatsiaris,Georgios Meditskos

doi:10.3390/s21082723

Abstract

The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.

Highlights

IntroductionInternet-of-Things systems consist of multiple connected devices
Using the EurValve Smart Home in a Box (SHiB) system [36], for each epoch, the provided data are the Received Signal Strength Indication (RSSI) data from each gateway along with the receiving gateway, 5 acceleration samples, the timestamp, the sequence number of the packet sent from the SHiB wearable and the true room annotation that the user was in, as well as the associated activity
We assessed the performance of RSSI and accelerometer features in room-level localization

Summary

Introduction

Internet-of-Things systems consist of multiple connected devices. The interconnected devices interact and produce large amounts of exploitable data that are used to improve lives in various aspects [1]. One of the common services found in IoT systems is localization or Location-Based Services (LBS). Depending on the space where it takes place, localization can be categorized as indoor and outdoor. Indoor spaces are considered more difficult for such tasks due to the existence of obstacles, like walls and objects that affect the accurate position estimation of a target. We usually find systems that exploit the global positioning system (GPS) while in indoor localization the most popular technology is the use of radio transmissions like WiFi and Bluetooth beacons [5]

Methods

Results

Discussion

Conclusion