This thesis explores the extent to which passive observation of wireless traffic in a smart home environment can be used to infer privacy-invasive information about its inhabitants. Using a setup that mimics the capabilities of a nosy neighbor in an adjacent flat, we analyze raw 802.11 packets and Bluetooth Low Energy advertisemets. From this data, we identify devices, infer their activity states and approximate their location using RSSI-based trilateration. Despite the encrypted nature of the data, we demonstrate that it is possible to detect active periods of multimedia devices, infer common activities such as sleeping, working and consuming media, and even approximate the layout of the neighbor's apartment. Our results show that privacy risks in smart homes extend beyond traditional data breaches: a nosy neighbor behind the wall can gain privacy-invasive insights into the lives of their neighbors purely from encrypted network traffic.