Memoryless Techniques and Wireless Technologies for Indoor Localization With the Internet of Things

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of indoor positioning systems (IPSs) and location-based services (LBSs). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS is necessary for efficient workspaces to be developed. In this article, we examine two memoryless positioning techniques, $K$ -nearest neighbor (KNN) and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: 1) ZigBee; 2) Bluetooth low energy (BLE); and 3) WiFi (2.4-GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The received signal strength indicator data set of all the experiments is available online.

I. Introduction

Recent advancements in the Internet of Things (IoT) have lead to the emergence of new applications, one of which being positioning, commonly known as localization. Localization in its simplest terms is the process of making something local to an area, which can be achieved through the use of indoor positioning systems (IPSs) and location-based services (LBSs). IPS is used in calculating the target’s location, while LBS uses the target’s location information to control features of the environment.