In current healthcare environments, a trend toward mobile and personalized interactions between people and nurse call systems is strongly noticeable. Therefore, it should be possible to locate patients at all times and in all places throughout the care facility. This paper aims at describing a method by which a mobile node can locate itself indoors, based on signal strength measurements and a minimal amount of yes/no decisions. The algorithm has been developed specifically for use in a healthcare environment. With extensive testing and statistical support, we prove that our algorithm can be used in a healthcare setting with an envisioned level of localization accuracy up to room revel (or region level in a corridor), while avoiding heavy investments since the hardware of an existing nurse call network can be reused. The approach opted for leads to very high scalability, since thousands of mobile nodes can locate themselves. Network timing issues and localization update delays are avoided, which ensures that a patient can receive the needed care in a time and resources efficient way.

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With the latest generation of ultra-sensitive GPS-receivers, satellite signals can often be picked up even indoors, resulting in (inaccurate) indoor GPS-localization. A covered position will therefore no longer be characterized by the absence of satellite signals, creating the need for another way of categorizing this data as potentially inaccurate. This paper describes the use of GPS-based localization in an indoor environment. Only high level, generally available, GPS-data (NMEA-0183 GNSS-subset) are taken into account. Applications of ubiquitous location awareness, where the use of several RTLS (Real Time Location System) combinations is feasible, may benefit from this information to discriminate between GPS and other available localization data. A quality indicating parameter is readily available in GPS-data; the DOP (Dilution Of Precision) data field, which indicates the accuracy of the GPS localization based on the current satellite geometry. However since in indoor environments the roof and possible overlying floors often cause more signal attenuation compared to (outer) walls or windows, the probability of a better reception of 'low' orbiting satellite signals increases, giving rise to an unjustified good horizontal DOP value. Standard NMEA-0183 GPS strings are therefore analyzed in search of other indicators for malicious GPS-data