The number of IoT devices has been growing in the last few years. Many devices that usually had no connectivity whatsoever, are now internet connected. With this high number of devices, it is possible to lose track of which devices are connected to a network. In this work, approaches used to locate and classify devices in the local environment using an unmodified Android device are evaluated. The developed procedures remove entry barriers like expensive equipment or complicated setups for scanning, as only an installed Android application is needed. To increase the number of recognized devices, methods to create device fingerprints from offline Linux firmware images have been researched and implemented. This allows devices to be recognized, which have never been in our hands while developing this application. But not only devices connected to an IP-based network are of concern for this work, but also Bluetooth devices can be recognized by the application. Using this feature allows the user to get an overview of surrounding portable devices, including modern speakers and smartwatches. One major finding of this work is that most active scanning methods found in the literature can be used on Android as well. Passive methods are often unsupported or restricted on this mobile platform. This does not have to negatively affect the results, as detailed information often has to be probed actively anyway.