Indoor Localization for Wireless Sensor Networks

Localization has an import role in wireless sensor networks, as it adds context to gathered data. The goal of this thesis is to create an application that determines the location of a mobile sensor node in an indoor environment. A secondary goal is to get practical experience with indoor localization. Previously, in a literature review, we selected two localization algorithms for this purpose. RIPS was selected since it was tested in practice, uses no additional hardware and has a very good accuracy. CAB was selected because it is a range-free localization algorithm and has low computational complexity. RIPS turned out to be too hardware specific and was not portable to the target hardware. CAB failed because of multipath effects. However, by transmitting beacons on multiple frequencies and averaging over the received signal strength, these multipath effects can be mitigated. In this thesis, we tested the effect of multiple frequencies on a simple proximity algorithm. Although the accuracy did not improve as we expected, it did improve the robustness against temporal disturbances. We also tested the effect of multiple frequencies on two different fingerprinting systems, with the fingerprint database constructed in two ways: a small database, where samples are averaged over all channels and a large database, where samples are averaged per channel. We show that the small database provides the best accuracy with a mean localization error of 2.19 meters.

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