  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.
