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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 …