  Context-aware applications have been gaining huge interest in the last few
years. With cell phones becoming ubiquitous computing devices, cell phone
localization has become an important research problem. In this paper, we
present CellSense, a prob- abilistic RSSI-based fingerprinting location
determina- tion system for GSM phones.We discuss the challenges of implementing
a probabilistic fingerprinting local- ization technique in GSM networks and
present the details of the CellSense system and how it addresses the
challenges. To evaluate our proposed system, we implemented CellSense on
Android-based phones. Re- sults for two different testbeds, representing urban
and rural environments, show that CellSense provides at least 23.8% enhancement
in accuracy in rural areas and at least 86.4% in urban areas compared to other
RSSI-based GSMlocalization systems. This comes with a minimal increase in
computational requirements. We also evaluate the effect of changing the
different system parameters on the accuracy-complexity tradeoff.
