Wireless network

Trilateration

Computer network

Naive Bayes classifier

Bluetooth

Wireless

Workspace

Structural engineering

Support vector machine

The Internet

World Wide Web

Machine learning

Engineering

Embedded system

Artificial intelligence

Computer science

Real-time computing

Node (physics)

Telecommunications

Internet of Things

Robot

IEEE Internet of Things Journal

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). 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 are necessary for efficient workspaces to be developed. In this paper, 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: Zigbee, Bluetooth Low Energy (BLE), and 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 RSSI dataset of all the experiments is available online.

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