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Master Thesis: Indoor Location Prediction for unmodified Smart Mobile Devices using RF Signals


Indoor Location Prediction for unmodified Smart Mobile Devices using RF Signals



Smart mobile devices offer a great amount of functionality to the user, which is tremendously based on being connected to the Internet. The connection is realized via various wireless network interfaces, such as Wi-Fi, Bluetooth and the GSM module. To enable an ubiquitous user experience, with the user not even worrying about the devices connectivity, passive RF signals are broadcasted on all the mentioned wireless network interfaces. By logging this broadcast signals on the network side, an ambiance of people can be sensed and analyzed for different purposes. This thesis proposes an agent called the Passive Location Prediction Agent (PLPA), which is responsible for predicting future semantic locations based on passive RF signals. The PLPA uses its knowledge of the devices future location to optimize the indoor environment of interest. For Example, an elevator can be sent to the next needed floor; schedule more staff at airport gates or in a market section, when and where they are needed there; or turn on the heating in the dorm automatically before a person goes to bed. In this thesis the PLPA is fully conceptualized and consists of two main parts: the Passive Location Prediction Reasoner (PLPR) and the Domain Agent (DA), with the PLPR encapsulating the location prediction functionality and the DA sensing and actuating in an environment under the usage of the PLPRs predicted locations. As a proof of concept, the PLPR has been implemented using different variations of Markov models. The implementation is based on the Indoor Analytics testbed, which has been carried out as a Telekom Innovation Laboratories project. Furthermore, the implementation has been evaluated with reasonable results.


Supervisor: Prof. Dr. Axel Küpper, Bersant Deva

Type:  Master Thesis

Duration: 6 months



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