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Abstract

In this thesis an evaluation method on indoor positioning system called LORIOT is presented. This positioning system combines two technologies (RFID and IR) for positioning depending on Geo-referenced dynamic bayesian networks. LORIOT allows the users to calculate their position on their own device without sending any data to a server responsible for calculating the position [3]. This property provides less complexity and fast calculation. This positioning method is developed by placing the tags in the environment and letting the user carry the sensors that are used to read data from these tags. The user is then able to choose either to pass the positioning data to any third party application or not. The main focus here is to check the actual accuracy and performance of indoor positioning systems using the proposed evaluation method which is tested on LORIOT. Most of the evaluation methods that have been used to test the level of accuracy of indoor positioning systems are biased and not good enough. For instance, the system is tested under optimal conditions of the environment. To achieve this goal, the evaluation method will be used to test LORIOT in a natural environment and by using data of natural traces of people walking in the environment without giving them any task to do. This type of evaluation criteria improves the results because the system would be installed in an environment which has the same properties that the environment has in this study, (where the evaluation tests are done). In addition, the system will position people while walking naturally (unlike most evaluation methods which test indoor positioning systems not while walking).