Sample Size Determination Algorithm for fingerprint-based indoor localization systems

Provision of accurate location information is an important task in the Internet of Things (IoT) applications and scenarios. This need has boosted the research and development of fingerprint based, indoor localization systems, since GPS information is not available in indoor environments. Performance evaluation of such systems and their related localization algorithms, is usually based on sampling collection in predetermined test environments. The sample size determination and sampling methodology can significantly affect the reliability of the outcome. This work proposes an algorithm that calculates the minimum sample size of positioning data required for objective performance evaluation of fingerprint based localization systems. The use of a correct, independent, unbiased and representative sample size can speed up the training, evaluation and calibration procedures of a fingerprint based localization system, while ensuring that the system's true accuracy is achieved. The proposed Sample Size Determination Algorithm (SSDA) takes into consideration the desired confidence level, the resulting standard deviation of a small size preliminary sample as well as the error approximation with respect to the actual error of the system and proposes the final sample size for the evaluation and/or calibration and/or training of the utilized radio-maps. Additionally, the SSDA, assumes random sample allocation in the area of interest in order to avoid biased results. Risks arising from the selection of a sample of convenience are also investigated. Finally, the performance of the proposed algorithm is tested in both measured and simulated radio-maps.