Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms

Abstract

The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

“…On the other hand, the machine learning-based solutions provide more scalable and cost effective solutions. The scope of this article is limited to the machine learning-based position estimation techniques due to their promising results and applications in the fields of object tracking and localization [4,10]. The following subsection reviews the existing well-known machine learning-based position estimation techniques.…”

“…Basically, in order to estimate the position of any object, decision tree can be an effective and usable approach. Similarly, for indoor positioning or localization systems, decision tree can be used in fingerprinting online phase which is also known as position estimation localization phase, where RSSI patterns of target nodes are compared with stored RSSI patterns of anchor nodes in the database [4,12].…”

“…[3]. Among the available wireless technologies, Bluetooth Low Energy (BLE) is considered as an ideal technology in terms of cost, energy consumption, range and deployment [4]. The word localization generally refers to the actual location identification of an object with reference to some coordinate system or known landmark.…”

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

“…Although the former mentioned INS solutions can improve the tracking distance at some extend, these solutions have not been tested and verified in long-term localization experiment, especially in high motion state. Except for INS-based localization system, radio frequency-based localization systems have been exploited sufficiently in recent decades, such as ZigBee [12], [13] WIFI [14], [15] Ultra-Wideband (UWB) [16], [17] Bluetooth [18], [19] and so on. These radio frequency-based localization system mainly adopt Received Signal Strength Indication (RSSI) or ranging information to estimation target position [20].…”

Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication

“…Typical fingerprint-based localization algorithms usually use machine-learning algorithms [ 13 ], for example, random forest (RF) [ 14 ], K -nearest neighbor (KNN) [ 15 ], extreme learning machine (ELM) [ 16 ], artificial neural network (ANN) [ 17 ], etc. In [ 11 ], for indoor positioning based on VLC, three classical machine-learning algorithms, RF, ELM and KNN are adopted to train multiple classifiers based on received signal strength indication (RSSI) fingerprints, and a grid-independent least square (GI-LS) algorithm was proposed to combine the outputs of these classifiers.…”