Personal Bluetooth Low Energy (BLE) trackers such as AirTag help locate lost items but can be misused for stalking. Research indicates that BLE trackers can be identified through their transmitted packets, thus offering potential for machine learning (ML) solutions. However, current packet datasets lack the scale and diversity needed for real-world applicability. This paper presents an open-source 200-hour BLE advertisement packet dataset focused on personal tags, enabling future ML-based device detection approaches. Additionally, introduces the first large-scale BLE data preprocessing framework for efficient and modular BLE packet preprocessing. The Framework is showcased on the dataset, demonstrating feature extraction, labelling, and dynamic plotting. This paper lays the groundwork for IoT device detection in high-density, heterogeneous environments, enabling future advances in BLE device classification.

One major goal of eGovernment is the provision of an efficient and cost-effective infrastructure enabling access to online resources for all citizens. While at the beginning this goal was basically related to Internet, today there is growing demand for providing access anywhere and anytime also where wired networks are not available. Major drivers of this development are emerging technologies as WLAN and the growing mobility of inhabitants as well as increasing usage and penetration of mobile phones. Another driver are emerging opportunities for new services for citizens based on mobile technology summarized under the term mGovernment. In order to enable mobile applications on a larger scale an efficient infrastructure is necessary. In this paper the approach of Turku, Southwest Finland, in creating a wireless infrastructure will be described based on a case study.