Among the accurate indoor localisation systems that are using WiFi, Bluetooth, or infrared technologies, the ones that are based on the GSM rely on a stable external infrastructure that can be used even in an emergency. This paper presents an accurate GSM indoor localisation system that achieves a median error of 4.39 metres in horizontal coordinates and up to 64 percent accuracy in floor prediction (for 84 percent of cases the floor prediction is mistaken by not more than a single floor). The test and reference measurements were made inside a six-floor academic building, with an irregular shape, whose dimensions are around 50 metres by 70 metres. The localisation algorithm uses GSM signal readings from the 7 strongest cells available in the GSM standard (or fewer, if fewer than 7 are available). We estimate the location by a three-step method. Firstly, we propose a point localisation solution (i.e., localisation based on only one measurement). Then, by applying the central tendency filters and the Multilayer Perceptron, we build a localisation system that uses a sequence of estimations of current and past locations. We also discuss major accuracy factors such as the number of observed signals or the types of spaces in the building.

The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

We develop a technique of constructing uniformly continuous maps between function spaces $C_p(X)$ endowed with the pointwise topology. We prove that if $X$ is compact metrizable and strongly countable-dimensional, then there exists a uniformly continuous

This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.