Indoor location using fingerprinting and fuzzy logic

Indoor location systems cannot rely on technologies such as GPS (Global Positioning System) to determine the position of a mobile terminal, because its signals are blocked by obstacles such as walls, ceilings, roofs, etc. In such environments. The use of alternative techniques, such as the use of wireless networks, should be considered. The location estimation is made by measuring and analysing one of the parameters of the wireless signal, usually the received power. One of the techniques used to estimate the locations using wireless networks is fingerprinting. This technique comprises two phases: in the first phase data is collected from the scenario and stored in a database; the second phase consists in determining the location of the mobile node by comparing the data collected from the wireless transceiver with the data previously stored in the database. In this paper an approach for localisation using fingerprinting based on Fuzzy Logic and pattern searching is presented. The performance of the proposed approach is compared with the performance of classic methods, and it presents an improvement between 10.24% and 49.43%, depending on the mobile node and the Fuzzy Logic parameters.ł

RSS and LEA adaptation for indoor location using fingerprinting

Fingerprinting is an indoor location technique, based on wireless networks, where data stored during the offline phase is compared with data collected by the mobile device during the online phase. In most of the real-life scenarios, the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node and the ones stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.

A lightweight indoor localization model based on motley-keenan and COST

This paper presents a novel approach to WLAN propagation models for use in indoor localization. The major goal of this work is to eliminate the need for in situ data collection to generate the Fingerprinting map, instead, it is generated by using analytical propagation models such as: COST Multi-Wall, COST 231 average wall and Motley- Keenan. As Location Estimation Algorithms kNN (K-Nearest Neighbour) and WkNN (Weighted K-Nearest Neighbour) were used to determine the accuracy of the proposed technique. This work is based on analytical and measurement tools to determine which path loss propagation models are better for location estimation applications, based on Receive Signal Strength Indicator (RSSI).This study presents different proposals for choosing the most appropriate values for the models parameters, like obstacles attenuation and coefficients. Some adjustments to these models, particularly to Motley-Keenan, considering the thickness of walls, are proposed. The best found solution is based on the adjusted Motley-Keenan and COST models that allows to obtain the propagation loss estimation for several environments.Results obtained from two testing scenarios showed the reliability of the adjustments, providing smaller errors in the measured values values in comparison with the predicted values.

Sistema de localização indoor com base no protocolo ZIGBEE

Actualmente, os sistemas de localização são uma área em forte expansão sendo que para espaços exteriores existe uma grande variedade de sistemas de localização enquanto que para espaços interiores as soluções são mais escassas. Este trabalho apresenta o estudo e implementação de um sistema de localização indoor baseado no protocolo ZigBee, utilizando a informação da intensidade de sinal recebido (RSSI - Received Signal Strength Indication). Para a realização deste projecto foi necessário iniciar uma pesquisa mais pormenorizada do protocolo ZigBee. O dispositivo móvel a ser localizado é o módulo XBee Serie 2 que se baseia no mesmo protocolo. Posto isto, foi necessário efectuar um estudo sobre sistemas de localização existentes e analisar as técnicas de localização utilizadas para ambientes interiores. Desta forma utiliza-se neste projecto uma técnica que consiste na análise de fingerprinting, onde é criado um mapa com os valores RSSI para diferentes coordenadas do espaço físico. As intensidades de sinal recebido são relativas a dispositivos XBee instalados em pontos fixos de referência. Para calcular a localização do dispositivo móvel é utilizado o algoritmo K-NN (K- Nearest Neighbors) que permite estimar a posição aproximada do dispositivo móvel. Por último é descrito todo o desenvolvimento do projecto assim como a apresentação e discussão de resultados.