Automated technology-assisted positioning and context-aware services have become available to the mass market in recent years due to the decreasing cost of able mobile devices. Although there are established solutions for large-scale positioning, like Global Positioning System (GPS), no generic solution for these services for indoor applications has emerged yet. Providers usually struggle with the diversity and complexity of radio wave propagation in enclosed settings. This thesis proposes a novel approach to indoor positioning based on the development of a new system named Seasnips. It is based on received signal strength indicator (RSSI) with IEEE 802.11, and combines different techniques such as trilateration with received signal strength (RSS) and scene analysis based on radio maps from interpolated actual measurements. It also adapts its underlying radio propagation model from the statistical analysis of RSS readings from the used site. The aim of this system is to provide a powerful tool which automates most setup tasks, is easy to maintain and scales while maintaining accuracy on par with current scene analysis-based services. To familiarize the reader with these algorithms and methods, the first part provides detailed information about positioning techniques, investigates the usefulness of radio propagation models as well as RSS for positioning, and summarizes important related approaches. The design and implementation details of the system architecture and a fully functional prototype follow in the next chapters. As part of a real-world evaluation, Seasnips was compared to a commercially available indoor positioning provider based on fingerprinting with Wireless Local Area Network (WLAN) in different settings. The results indicate that the positioning accuracy offers a performance comparable to a state-of-the-art solution while outperforming it in terms of maintainability and scalability.