Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF
The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. Signal strength difference (DIFF), a calibration-free solution...
Saved in:
| Published in: | Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5891 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Basel
MDPI AG
07.08.2022
MDPI |
| Subjects: | |
| ISSN: | 1424-8220, 1424-8220 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. 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. |
|---|---|
| AbstractList | The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. 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. The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. 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.The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. 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. |
| Author | Zhang, Wenjie Xu, Li Yang, Jingmin Deng, Shanghui |
| AuthorAffiliation | 1 School of Computer Science, Minnan Normal University, Zhangzhou 363000, China 2 Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, China 3 Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China |
| AuthorAffiliation_xml | – name: 2 Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, China – name: 1 School of Computer Science, Minnan Normal University, Zhangzhou 363000, China – name: 3 Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China |
| Author_xml | – sequence: 1 givenname: Jingmin orcidid: 0000-0001-6467-7545 surname: Yang fullname: Yang, Jingmin – sequence: 2 givenname: Shanghui orcidid: 0000-0003-4419-986X surname: Deng fullname: Deng, Shanghui – sequence: 3 givenname: Li surname: Xu fullname: Xu, Li – sequence: 4 givenname: Wenjie surname: Zhang fullname: Zhang, Wenjie |
| BookMark | eNptkU1PGzEQhq0KVCD00H9gqZf2sMUer9e7l0o0aWgkBBzaszX-2OBoY1N7U6n_vhuCUEE9eTR-_MzI7xk5iil6Qt5z9lmIjl0UAC5l2_E35JTXUFctADv6pz4hZ6VsGAMhRPuWnAjZSVXXzSm5muMQTMYxpFgts_dULOgqupQyvUsl7PshrunlsE45jPfbQr9i8Y6mSBc3NxSjo4vVcnlOjnscin_3dM7Iz-W3H_Pv1fXt1Wp-eV3ZadxYofJ9z6G3HE0DzjCGAK5xUklEZSyTltcCO2y4Ewa44dI3WIME0XYgmJiR1cHrEm70Qw5bzH90wqAfGymvNeYx2MFr7BuBYMCgkfVUGm69M1hLUML3qp1cXw6uh53Zemd9HDMOL6Qvb2K41-v0W3dCcdHuBR-fBDn92vky6m0o1g8DRp92RYNiwFWrps1n5MMrdJN2OU5ftaeYahSTcqI-HSibUynZ98_LcKb3UevnqCf24hVrw_iY47RrGP7z4i9ZLKk5 |
| CitedBy_id | crossref_primary_10_1016_j_comcom_2023_10_018 crossref_primary_10_3390_electronics13020360 crossref_primary_10_1088_1402_4896_ade8b7 crossref_primary_10_1109_JSEN_2022_3226303 crossref_primary_10_1038_s41598_025_09899_8 crossref_primary_10_3389_fpubh_2025_1475456 |
| Cites_doi | 10.1109/ACCESS.2020.2992727 10.1109/ICC.2019.8761118 10.1109/IPIN.2015.7346967 10.1016/j.autcon.2008.10.011 10.1109/JSTSP.2018.2827701 10.1007/978-3-319-45246-3 10.1016/j.jfranklin.2019.10.028 10.3390/s19112508 10.1007/s00500-018-3171-4 10.1049/iet-com.2019.0681 10.1109/ISMICT.2016.7498906 10.1109/TVT.2018.2833029 10.3390/s150614809 10.1016/j.adhoc.2021.102443 10.1109/CONFLUENCE.2016.7508143 10.1109/TLA.2016.7587622 10.1109/ACCESS.2016.2530806 10.1016/j.eswa.2009.05.062 10.1109/TMC.2018.2839112 10.1109/ACCESS.2019.2913784 10.1109/TMC.2016.2550452 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139 10.1109/TIE.2010.2055774 10.1109/IPEC51340.2021.9421239 10.1186/s41044-018-0031-2 10.1109/ACCESS.2018.2843325 10.1109/PERCOM.2008.75 10.1109/IPIN.2014.7275492 10.1109/JIOT.2019.2901093 10.1007/s11277-017-4852-5 10.1016/j.asoc.2022.108624 10.1109/IPIN.2016.7743591 10.1109/ICUFN.2018.8436598 10.3390/s21041114 10.1109/LES.2021.3094965 10.1109/IWCMC.2017.7986446 10.1109/TPAMI.2013.50 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22155891 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_af63a2b2bab5463ab1cedba45273ef78 PMC9371388 10_3390_s22155891 |
| GrantInformation_xml | – fundername: Opening Foundation of Fujian Provincial Key Laboratory of Network Security and Cryptology Research Fund, Fujian Normal University grantid: NSCL-KF2021-07 – fundername: Research Project on Education and Teaching Reform of Undergraduate Colleges and Universities in Fujian Province grantid: FBJG20210070 – fundername: Fujian Province Nature Science Foundation grantid: 2020J01813; 2021J011002 – fundername: Zhangzhou Municipal Natural Science Foundation grantid: ZZ2021J23 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c446t-a7eff12fc1ab62db00a22d6d575aa7bc05c143a9a61d3b21b15e6a42523892303 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 8 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000839846500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:52:08 EDT 2025 Tue Nov 04 02:01:18 EST 2025 Fri Sep 05 07:03:21 EDT 2025 Tue Oct 07 07:08:32 EDT 2025 Tue Nov 18 20:58:05 EST 2025 Sat Nov 29 07:17:19 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 15 |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c446t-a7eff12fc1ab62db00a22d6d575aa7bc05c143a9a61d3b21b15e6a42523892303 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-4419-986X 0000-0001-6467-7545 |
| OpenAccessLink | https://doaj.org/article/af63a2b2bab5463ab1cedba45273ef78 |
| PMID | 35957446 |
| PQID | 2700767055 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_af63a2b2bab5463ab1cedba45273ef78 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9371388 proquest_miscellaneous_2702178792 proquest_journals_2700767055 crossref_primary_10_3390_s22155891 crossref_citationtrail_10_3390_s22155891 |
| PublicationCentury | 2000 |
| PublicationDate | 20220807 |
| PublicationDateYYYYMMDD | 2022-08-07 |
| PublicationDate_xml | – month: 8 year: 2022 text: 20220807 day: 7 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Sadowski (ref_17) 2018; 6 Chen (ref_3) 2016; 4 Kim (ref_34) 2018; 3 Uradzinski (ref_12) 2017; 97 ref_36 ref_35 ref_33 ref_32 Wang (ref_37) 2022; 14 Cha (ref_38) 2022; 120 ref_31 Saab (ref_9) 2011; 58 Lin (ref_21) 2020; 14 ref_16 Mohammad (ref_4) 2019; 7 Zhu (ref_6) 2020; 357 Tesoriero (ref_10) 2010; 37 (ref_19) 2018; 12 ref_25 Jiang (ref_13) 2018; 22 Chow (ref_23) 2019; 18 ref_24 Bengio (ref_39) 2013; 35 ref_22 ref_42 ref_41 ref_1 Alvarez (ref_11) 2016; 14 Ravi (ref_18) 2021; 115 Guo (ref_20) 2018; 67 ref_29 ref_28 ref_27 Quinteiro (ref_30) 2015; 15 ref_26 Luo (ref_15) 2017; 16 ref_8 Maswadi (ref_2) 2020; 8 Chen (ref_5) 2019; 6 Khoury (ref_14) 2009; 18 Vincent (ref_40) 2010; 11 ref_7 |
| References_xml | – volume: 8 start-page: 92244 year: 2020 ident: ref_2 article-title: Systematic literature review of smart home monitoring technologies based on IoT for the elderly publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2992727 – ident: ref_22 doi: 10.1109/ICC.2019.8761118 – ident: ref_41 doi: 10.1109/IPIN.2015.7346967 – volume: 18 start-page: 444 year: 2009 ident: ref_14 article-title: Evaluation of position tracking technologies for user localization in indoor construction environments publication-title: Autom. Constr. doi: 10.1016/j.autcon.2008.10.011 – volume: 12 start-page: 529 year: 2018 ident: ref_19 article-title: Hybrid Analog-Digital Processing System for Amplitude-Monopulse RSSI-Based MiMo WiFi Direction-of-Arrival Estimation publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2018.2827701 – ident: ref_29 doi: 10.1007/978-3-319-45246-3 – volume: 357 start-page: 1420 year: 2020 ident: ref_6 article-title: Accurate WiFi-based indoor localization by using fuzzy classifier and mlps ensemble in complex environment publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2019.10.028 – ident: ref_31 doi: 10.3390/s19112508 – ident: ref_32 – volume: 11 start-page: 3371 year: 2010 ident: ref_40 article-title: Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion publication-title: J. Mach. Learn. Res. – ident: ref_26 – volume: 22 start-page: 3621 year: 2018 ident: ref_13 article-title: FSELM: Fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints publication-title: Soft Comput. doi: 10.1007/s00500-018-3171-4 – volume: 14 start-page: 497 year: 2020 ident: ref_21 article-title: Characteristic analysis of wireless local area network’s received signal strength indication in indoor positioning publication-title: IET Commun. doi: 10.1049/iet-com.2019.0681 – ident: ref_7 doi: 10.1109/ISMICT.2016.7498906 – volume: 67 start-page: 7314 year: 2018 ident: ref_20 article-title: Accurate WiFi Localization by Fusing a Group of Fingerprints via a Global Fusion Profile publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2833029 – volume: 15 start-page: 14809 year: 2015 ident: ref_30 article-title: A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization publication-title: Sensors doi: 10.3390/s150614809 – ident: ref_16 – volume: 115 start-page: 102443 year: 2021 ident: ref_18 article-title: Practical server-side WiFi-based indoor localization: Addressing cardinality and outlier challenges for improved occupancy estimation publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2021.102443 – ident: ref_8 doi: 10.1109/CONFLUENCE.2016.7508143 – volume: 14 start-page: 3208 year: 2016 ident: ref_11 article-title: ZigBee-based Sensor Network for Indoor Location and Tracking Applications publication-title: IEEE Lat. Am. Trans. doi: 10.1109/TLA.2016.7587622 – volume: 4 start-page: 803 year: 2016 ident: ref_3 article-title: Vehicle localization and velocity estimation based on mobile phone sensing publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2530806 – ident: ref_42 – volume: 37 start-page: 894 year: 2010 ident: ref_10 article-title: Improving location awareness in indoor spaces using RFID technology publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2009.05.062 – volume: 18 start-page: 290 year: 2019 ident: ref_23 article-title: Efficient Locality Classification for Indoor Fingerprint-Based Systems publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2018.2839112 – volume: 7 start-page: 60376 year: 2019 ident: ref_4 article-title: Formal analysis of human-assisted smart city emergency services publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2913784 – volume: 16 start-page: 466 year: 2017 ident: ref_15 article-title: Pallas: Self-Bootstrapping Fine-Grained Passive Indoor Localization Using WiFi Monitors publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2016.2550452 – ident: ref_35 doi: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139 – volume: 58 start-page: 1961 year: 2011 ident: ref_9 article-title: A Standalone RFID Indoor Positioning System Using Passive Tags publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2010.2055774 – ident: ref_1 doi: 10.1109/IPEC51340.2021.9421239 – volume: 3 start-page: 4 year: 2018 ident: ref_34 article-title: A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting publication-title: Big Data Anal. doi: 10.1186/s41044-018-0031-2 – volume: 6 start-page: 30149 year: 2018 ident: ref_17 article-title: RSSI-Based Indoor Localization With the Internet of Things publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2843325 – ident: ref_25 doi: 10.1109/PERCOM.2008.75 – ident: ref_28 doi: 10.1109/IPIN.2014.7275492 – volume: 6 start-page: 7570 year: 2019 ident: ref_5 article-title: Crowdtracking: Real-time vehicle tracking through mobile crowdsensing publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2019.2901093 – volume: 97 start-page: 6509 year: 2017 ident: ref_12 article-title: Advanced Indoor Positioning Using Zigbee Wireless Technology publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-017-4852-5 – volume: 120 start-page: 108624 year: 2022 ident: ref_38 article-title: A hierarchical auxiliary deep neural network architecture for large-scale indoor localization based on Wi-Fi fingerprinting publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.108624 – ident: ref_24 doi: 10.1109/IPIN.2016.7743591 – ident: ref_33 doi: 10.1109/ICUFN.2018.8436598 – ident: ref_36 doi: 10.3390/s21041114 – volume: 14 start-page: 23 year: 2022 ident: ref_37 article-title: CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization With Deep Learning publication-title: IEEE Embed. Syst. Lett. doi: 10.1109/LES.2021.3094965 – ident: ref_27 doi: 10.1109/IWCMC.2017.7986446 – volume: 35 start-page: 1798 year: 2013 ident: ref_39 article-title: Representation Learning: A Review and New Perspectives publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2013.50 |
| SSID | ssj0023338 |
| Score | 2.4110258 |
| Snippet | The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 5891 |
| SubjectTerms | 3D indoor positioning Accuracy Algorithms Calibration calibration-free deep denoising autoencoder (DDAE) Deep learning fingerprint database Global positioning systems GPS Location based services Machine learning Methods Radio frequency identification signal strength difference (DIFF) |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Nb9UwDLfgjQMcxudEx0ABceBS7SVt0_aE9vZW2KV6QiDtVuWr26TRjvZtf__svryySojLro2VRLUd27HzM8Dnem5Nwg3lDOsojFWShrm2KiT0fBlznpu5HZpNpGWZnZ3lK3_h1vuyyu2ZOBzUtjV0R35ICdJUEvbL1-s_IXWNouyqb6HxGHYIqSyewc7ipFz9GEOuCCOwDZ5QhMH9YS_QwlEfvYkVGsD6Jx7mtD7ynsEpnj90qy9g17ua7GgjGy_hkWtewbN7AISv4Rs9zdIbIQiLzjkWLdlpY9u2YytfzoWE7OjqHBdYX_zu2QLNnmVtw5ZlyVRj2fK0KN7Ar-Lk5_H30LdWCA3Gf-tQpa6uuagNV1oKi7qnhLDSovOmVKrNPDHoSKlcSW4jLbjmiZMK9RstPLqE82gPZk3buLfAcp5nCXkZGBnFztFbWJmIWEqcHP0zHsCX7a-ujMcdp_YXVxXGH8SVauRKAJ9G0usN2Ma_iBbEr5GA8LGHD213Xnl1q1QtIyW00EoT3r_S3DirVUxwc65OswAOtqyrvNL21V--BfBxHEZ1oxyKalx7M9BgEJeluQggnUjJZEPTkebyYgDuJuzBKMv2_7_4O3gq6I0F1aWkBzBbdzfuPTwxt-vLvvvgJfwOg1oFIg priority: 102 providerName: ProQuest |
| Title | Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF |
| URI | https://www.proquest.com/docview/2700767055 https://www.proquest.com/docview/2702178792 https://pubmed.ncbi.nlm.nih.gov/PMC9371388 https://doaj.org/article/af63a2b2bab5463ab1cedba45273ef78 |
| Volume | 22 |
| WOSCitedRecordID | wos000839846500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB5B4QAHVF7C0EYL4sDFanZt79rHpokhh1oWAimcrH25rVRslKQc-e3M2E4US0i9cPFhd2StZzya-bQz3wB8rKfOJtzSnWEdhbFOVJgZp0Niz5cx55mdum7YhCqKdLXKyoNRX1QT1tMD94o707WMtDDCaEPM7dpw653RMRGH-Vp1bb6Y9ezA1AC1IkRePY9QhKD-bCMwstH8vFH06Uj6R5nluC7yINDkx_BsyBDZeX-y5_DANy_g6QFv4Ev4TB1VprddmK-9Z9GcLRvXtmtWDlVYKMjOb69aBP_XPzdshtHKsbZh86JgunFsvszzV_A9X3y7-BIOExFCi7BtG2rl65qL2nJtpHDoMloIJx3mXForY6eJxfxHZ1pyFxnBDU-81OiWGJgxk5tGr-GoaRv_BljGszSh5AABTew9tbDKRMRS4ssxreIBfNppqrIDXThNrbitEDaQUqu9UgP4sBf91XNk_EtoRureCxCtdbeAxq4GY1f3GTuAk52xqsHXNhVdnStJrEABvN9vo5fQ1YdufHvXySD2SlUmAlAjI48ONN5pbq47vm2iDIzS9O3_-IJ38ERQAwUVnagTONqu7_wpPLa_tzeb9QQeqpXqnukEHs0WRfl10v3Y-Lz8s8C1cnlZ_vgLRsL9dA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQceCMCBQwCiUvU2Hk4OSDUsoSuWlY9FGlvqV9pK5WkJFsQf4rfyEyS3TYS4tYD13jkxPGXeWTG3wC8KQNrYm4oZ1iGfqRi6WfaKp_Y85OI88wEtms2IWezdD7P9tfg9_IsDJVVLnVip6htbegf-SYlSGVC3C8fzr771DWKsqvLFho9LHbdr58YsrXvpxPc37dC5J8OPu74Q1cB32Dos_CVdGXJRWm40omwCDslhE0s-i1KSW2C2KAPoTKVcBtqwTWPXaIQ2mjc0BsKQpz3GlxHPS6phEzOLwK8EOO9nr0oDLNgsxVoT6lr38jmda0BRv7suBrzknnL7_5vL-Ye3BkcabbVI_8-rLnqAdy-RK_4ED7TwTPdQ9zPG-dYOGHTytZ1w_aHYjUUZFunR7igxfG3lm2jUbesrthkNmOqsmwyzfNH8PVKVvIY1qu6ck-AZTxLY_KhMO6LnKOTvkksoiTBydH75B68W25tYQZWdWrucVpgdEUoKFYo8OD1SvSspxL5m9A24WMlQOzf3YW6OSoGZVKoMgmV0EIrTd0MlObGWa0iItNzpUw92FhCpRhUUltc4MSDV6thVCaUIVKVq887GQxRU5kJD-QIlaMHGo9UJ8cdLTkxK4Zp-vTfN38JN3cOvuwVe9PZ7jO4Jeg0CVXgyA1YXzTn7jncMD8WJ23zovu2GBxeNWb_APSyYFw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3JbtRAEC2FBCE4sCMMARoEEhdr3O39gFCCMYwClg8ghZPTm5NIwQ72BMSv8XVUeTxDLCFuOXB1l9pLv67FVf0K4HntGR1yTTnD2ncDGcZuqox0iT0_CjhPtWeGZhNxUST7-2m5Ab9WZ2GorHKlEwdFbVpN_8hnlCCNI-J-mdVjWUSZ5a9Pv7nUQYoyrat2GkuI7NmfPzB861_NM1zrF0Lkbz-9ee-OHQZcjWHQwpWxrWsuas2lioRBCEohTGTQh5EyVtoLNfoTMpURN74SXPHQRhJhjoYOPSPPx3kvwRa65AHusa1y_rH8sg73fIz-llxGvp96s16gdaUefhMLODQKmHi309rMc8Yuv_E_f6abcH10sdnOck_cgg3b3IZr54gX78A7OpKmluB3885a5mds3pi27Vg5lrGhINs5OcQXWhx97dkumnvD2oZlRcFkY1g2z_O78PlC3uQebDZtY-8DS3mahORdYUQYWEtngKNQBFGEk6Nfyh14uVrmSo9869T246TCuIsQUa0R4cCztejpkmTkb0K7hJW1APGCDxfa7rAa1Uwl68iXQgklFfU5kIpra5QMiGbP1nHiwPYKNtWorPrqD2YceLoeRjVDuSPZ2PZskMHgNYlT4UA8QejkgaYjzfHRQFhOnIt-kjz4982fwBWEavVhXuw9hKuCjplQaU68DZuL7sw-gsv6--K47x6PG43BwUWD9jfnGWqr |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Calibration-Free+3D+Indoor+Positioning+Algorithms+Based+on+DNN+and+DIFF&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Jingmin+Yang&rft.au=Shanghui+Deng&rft.au=Li+Xu&rft.au=Wenjie+Zhang&rft.date=2022-08-07&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=22&rft.issue=15&rft.spage=5891&rft_id=info:doi/10.3390%2Fs22155891&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_af63a2b2bab5463ab1cedba45273ef78 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |