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Signal strength difference (DIFF), a calibration-free solution for handling the received signal strength variance between diverse devices, can effectively reduce the negative impact of signal fluctuation. However, DIFF also leads to the explosion of the RSSI data dimension, expanding the number of dimensions from m to Cm2, which reduces the positioning efficiency. To this end, we design a data hierarchical processing strategy based on a building-floor-specific location, which effectively improves the efficiency of high-dimensional data processing. Moreover, based on a deep neural network (DNN), we design three different positioning algorithms for multi-building, multi-floor, and specific-location respectively, extending the indoor positioning from the single plane to three dimensions. Specifically, in the stage of data preprocessing, we first create the original RSSI database. Next, we create the optimized RSSI database by identifying and deleting the unavailable data in the RSSI database. Finally, we perform DIFF processing on the optimized RSSI database to create the DIFF database. In the stage of positioning, firstly, we design an improved multi-building positioning algorithm based on a denoising autoencoder (DAE). Secondly, we design an enhanced DNN for multi-floor positioning. Finally, the newly deep denoising autoencoder (DDAE) used for specific location positioning is proposed. The experimental results show that the proposed algorithms have better positioning efficiency and accuracy compared with the traditional machine learning algorithms and the current advanced deep learning algorithms.<\/jats:p>","DOI":"10.3390\/s22155891","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"5891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6467-7545","authenticated-orcid":false,"given":"Jingmin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4419-986X","authenticated-orcid":false,"given":"Shanghui","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Xu","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, F., and Geng, Z. 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