<p>Indoor localization has a significant contribution to various location-based services including user tracking, asset tracking, robot navigation, and so on. Existing research indicates that WiFi-based RSSI fingerprinting is a promising technique for indoor localization. Although the research is mostly focused on smartphone-based user localization, a few existing works attempted to locate assets and robots using IoT devices. In this work, our contribution is to propose the design of a unified localization system using smartphones and IoT devices to connect these two seemingly different research directions. Accordingly, real-life data has been collected from IoT modules—ESP8266 and ESP32-S3 along with a smartphone. The dataset is available online. Classification techniques have been utilized to extract meaningful distinguishing patterns from both IoT devices and smartphones and their combinations. Localization performance is evaluated using machine learning and deep learning classifiers. The Random Forest classifier is found to achieve consistent accuracy for the proposed unified localization system and yields around 96% accuracy. The localization error was statistically analyzed using the cumulative distribution function.</p>