Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signals
Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory i...
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| Published in: | Pervasive and mobile computing Vol. 89; p. 101742 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.02.2023
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| ISSN: | 1574-1192, 1873-1589 |
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| Abstract | Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes. |
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| AbstractList | Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes. |
| ArticleNumber | 101742 |
| Author | Korpela, Joseph Zhang, Yizhe Murakami, Tomoki Kumrai, Teerawat Maekawa, Takuya Ohara, Kazuya Abeysekera, Hirantha |
| Author_xml | – sequence: 1 givenname: Teerawat orcidid: 0000-0002-0094-9545 surname: Kumrai fullname: Kumrai, Teerawat organization: Osaka University, Suita, Osaka, Japan – sequence: 2 givenname: Joseph surname: Korpela fullname: Korpela, Joseph organization: Osaka University, Suita, Osaka, Japan – sequence: 3 givenname: Yizhe surname: Zhang fullname: Zhang, Yizhe organization: Osaka University, Suita, Osaka, Japan – sequence: 4 givenname: Kazuya surname: Ohara fullname: Ohara, Kazuya organization: NTT Communication Science Laboratories, Kyoto, Japan – sequence: 5 givenname: Tomoki surname: Murakami fullname: Murakami, Tomoki organization: NTT Access Network Service Systems Laboratories, Yokosuka, Japan – sequence: 6 givenname: Hirantha surname: Abeysekera fullname: Abeysekera, Hirantha organization: NTT Access Network Service Systems Laboratories, Yokosuka, Japan – sequence: 7 givenname: Takuya orcidid: 0000-0002-7227-580X surname: Maekawa fullname: Maekawa, Takuya email: takuya.maekawa@acm.org organization: Osaka University, Suita, Osaka, Japan |
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| Cites_doi | 10.1109/TWC.2019.2957363 10.1145/2971648.2971710 10.1186/s41044-018-0031-2 10.1613/jair.953 10.1145/3264930 10.1145/3139243.3139254 10.1109/ACCESS.2019.2933921 10.3390/s20185182 10.1145/2971648.2971684 10.1145/3131903 10.1109/TVT.2014.2363842 10.1145/3284555 10.1109/LSENS.2017.2787651 10.1109/JSEN.2020.2980966 10.1109/TNSE.2018.2871165 10.1371/journal.pone.0090375 10.1145/1614320.1614350 10.1109/JSEN.2017.2660522 10.1145/2348543.2348580 10.1145/2750858.2804254 10.1145/3380979 10.1145/2370216.2370288 10.1016/j.buildenv.2018.05.026 10.3390/s17010147 10.1145/2634317.2634320 10.1145/2493432.2493504 10.1145/2667226 10.1109/JSEN.2021.3128517 10.1109/TWC.2021.3053582 10.3390/info12050180 10.1007/s12652-017-0549-6 10.1109/JSEN.2018.2885958 |
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| Keywords | Convolutional variational autoencoder (CVAE) Indoor logical location classifier Wi-Fi RSS information |
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| References | Wu, Chen, Lv, Han, Chen (b23) 2017; 1 Association (b4) 2010 Ha, Kim, Park, Kim (b10) 2018; 140 A. Rai, K.K. Chintalapudi, V.N. Padmanabhan, R. Sen, Zee: Zero-effort Crowdsourcing for Indoor Localization, in: 18th Annual International Conference on Mobile Computing and Networking, 2012, pp. 293–304. Chawla, Bowyer, Hall, Kegelmeyer (b45) 2002; 16 Niu, Li, He, Gao, Gary Chan, Luo (b12) 2019; 15 Gunawan, Li, Gallagher, Dempster, Retscher (b40) 2012 Song, Fan, Xiang, Ye, Liu, Wang, He, Yang, Fang (b48) 2019; 7 Zhang, Pei, Deng (b33) 2016 Abbas, Elhamshary, Rizk, Torki, Youssef (b49) 2019 J.G. Rodrigues, J.P. Pereira, A. Aguiar, Impact of crowdsourced data quality on travel pattern estimation, in: First ACM Workshop on Mobile Crowdsensing Systems and Applications, 2017, pp. 38–43. Mirowski, Ho, Yi, MacDonald (b30) 2013 Zhao, Wong, Feng, Garg (b38) 2019; 19 M. Azizyan, I. Constandache, R. Roy Choudhury, SurroundSense: mobile phone localization via ambience fingerprinting, in: MobiCom 2009, 2009, pp. 261–272. Celik, Incel (b24) 2018; 9 Jedari, Wu, Rashidzadeh, Saif (b1) 2015 Gu, Blankenbach, Khoshelham, Grottke, Valaee (b35) 2019 Taniuchi, Maekawa (b39) 2015; 14 Zhou, Lapedriza, Khosla, Oliva, Torralba (b42) 2017 M. Fan, A.T. Adams, K.N. Truong, Public restroom detection on mobile phone via active probing, in: International Symposium on Wearable Computers (ISWC 2014), 2014, pp. 27–34. Li, Zhao, Zhao, Braun (b37) 2021; 20 Nov, Arazy, Anderson (b5) 2014; 9 Yang, Qiu, Han, Yang (b22) 2020 Wang, Wang, Mao (b19) 2018; 7 Chidlovskii, Antsfeld (b46) 2019 Liang, Corso, Turner, Zakhor (b11) 2013 Rajab, Wang (b54) 2021; 22 Kim, Lee, Huang (b16) 2018; 3 M. Tachikawa, T. Maekawa, Y. Matsushita, Predicting location semantics combining active and passive sensing with environment-independent classifier, in: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016, pp. 220–231. Zhou, Lapedriza, Xiao, Torralba, Oliva (b43) 2014; 27 Gong, Shroff (b50) 2018 Górak, Luckner (b2) 2016 Y. Chon, N.D. Lane, F. Li, H. Cha, F. Zhao, Automatically characterizing places with opportunistic crowdsensing using smartphones, in: 2012 ACM Conference on Ubiquitous Computing, 2012, pp. 481–490. Lee, Moon, Lee, Han (b31) 2020; 20 Pérez-Penichet, Moreira (b52) 2014 Rizk, Torki, Youssef (b15) 2018; 19 Chan, Wu, Fu (b32) 2018 Li, Liu, Niu, Liu, Chan, Gao (b13) 2018; 2 Yang, Guo, Guo, Zhao, Zhao (b21) 2020; 20 Elbakly, Youssef (b28) 2020; 4 Hernández, Ocaña, Alonso, Kim (b36) 2017; 17 Jiang, He, Xi, Zeng (b7) 2021; 12 R. Palaniappan, P. Mirowski, T.K. Ho, H. Steck, P. Whiting, M. MacDonald, Autonomous RF Surveying Robot for Indoor Localization and Tracking, in: International Conference on Indoor Positioning and Indoor Navigation, 2011. M. Shimosaka, O. Saisho, Efficient Calibration for RSSI-based Indoor Localization by Bayesian Experimental Design on Multi-task Classification, in: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016, pp. 244–249. S. He, S.-H.G. Chan, L. Yu, N. Liu, Calibration-free Fusion of Step Counter and Wireless Fingerprints for Indoor Localization, in: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, pp. 897–908. Wang, Wang, Mao (b18) 2017 Khatab, Hajihoseini, Ghorashi (b14) 2017; 2 Xue, Qiu, Hua, Yu (b27) 2017; 17 Wen, Shi, Zhang, Tian, Huang, Yu, Cheng, Shen (b51) 2014; 64 Salamah, Tamazin, Sharkas, Khedr (b3) 2016 J. Krumm, D. Rouhana, Placer: semantic place labels from diary data, in: 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2013, pp. 163–172. Dissanayake, Maekawa, Hara, Miyanishi, Kawanabe (b9) 2021 Kingma, Ba (b44) 2017 Turgut, Üstebay, Aydın, Sertbaş (b47) 2019 Liang (10.1016/j.pmcj.2022.101742_b11) 2013 Chan (10.1016/j.pmcj.2022.101742_b32) 2018 Lee (10.1016/j.pmcj.2022.101742_b31) 2020; 20 Zhou (10.1016/j.pmcj.2022.101742_b42) 2017 Turgut (10.1016/j.pmcj.2022.101742_b47) 2019 Celik (10.1016/j.pmcj.2022.101742_b24) 2018; 9 Yang (10.1016/j.pmcj.2022.101742_b22) 2020 10.1016/j.pmcj.2022.101742_b41 Ha (10.1016/j.pmcj.2022.101742_b10) 2018; 140 Rajab (10.1016/j.pmcj.2022.101742_b54) 2021; 22 Jedari (10.1016/j.pmcj.2022.101742_b1) 2015 Wang (10.1016/j.pmcj.2022.101742_b18) 2017 Yang (10.1016/j.pmcj.2022.101742_b21) 2020; 20 Chawla (10.1016/j.pmcj.2022.101742_b45) 2002; 16 Niu (10.1016/j.pmcj.2022.101742_b12) 2019; 15 Górak (10.1016/j.pmcj.2022.101742_b2) 2016 Hernández (10.1016/j.pmcj.2022.101742_b36) 2017; 17 Zhao (10.1016/j.pmcj.2022.101742_b38) 2019; 19 Pérez-Penichet (10.1016/j.pmcj.2022.101742_b52) 2014 Gong (10.1016/j.pmcj.2022.101742_b50) 2018 Zhou (10.1016/j.pmcj.2022.101742_b43) 2014; 27 Mirowski (10.1016/j.pmcj.2022.101742_b30) 2013 10.1016/j.pmcj.2022.101742_b34 Nov (10.1016/j.pmcj.2022.101742_b5) 2014; 9 10.1016/j.pmcj.2022.101742_b8 Wu (10.1016/j.pmcj.2022.101742_b23) 2017; 1 Association (10.1016/j.pmcj.2022.101742_b4) 2010 10.1016/j.pmcj.2022.101742_b6 Xue (10.1016/j.pmcj.2022.101742_b27) 2017; 17 Kingma (10.1016/j.pmcj.2022.101742_b44) 2017 Wen (10.1016/j.pmcj.2022.101742_b51) 2014; 64 Li (10.1016/j.pmcj.2022.101742_b37) 2021; 20 Gunawan (10.1016/j.pmcj.2022.101742_b40) 2012 Zhang (10.1016/j.pmcj.2022.101742_b33) 2016 Gu (10.1016/j.pmcj.2022.101742_b35) 2019 Chidlovskii (10.1016/j.pmcj.2022.101742_b46) 2019 Li (10.1016/j.pmcj.2022.101742_b13) 2018; 2 Khatab (10.1016/j.pmcj.2022.101742_b14) 2017; 2 Song (10.1016/j.pmcj.2022.101742_b48) 2019; 7 10.1016/j.pmcj.2022.101742_b20 Salamah (10.1016/j.pmcj.2022.101742_b3) 2016 10.1016/j.pmcj.2022.101742_b25 Elbakly (10.1016/j.pmcj.2022.101742_b28) 2020; 4 10.1016/j.pmcj.2022.101742_b29 Kim (10.1016/j.pmcj.2022.101742_b16) 2018; 3 10.1016/j.pmcj.2022.101742_b26 Abbas (10.1016/j.pmcj.2022.101742_b49) 2019 Wang (10.1016/j.pmcj.2022.101742_b19) 2018; 7 10.1016/j.pmcj.2022.101742_b53 Dissanayake (10.1016/j.pmcj.2022.101742_b9) 2021 10.1016/j.pmcj.2022.101742_b17 Taniuchi (10.1016/j.pmcj.2022.101742_b39) 2015; 14 Rizk (10.1016/j.pmcj.2022.101742_b15) 2018; 19 Jiang (10.1016/j.pmcj.2022.101742_b7) 2021; 12 |
| References_xml | – volume: 2 start-page: 1 year: 2017 end-page: 4 ident: b14 article-title: A fingerprint method for indoor localization using autoencoder based deep extreme learning machine publication-title: IEEE Sens. Lett. – reference: M. Shimosaka, O. Saisho, Efficient Calibration for RSSI-based Indoor Localization by Bayesian Experimental Design on Multi-task Classification, in: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016, pp. 244–249. – start-page: 161 year: 2018 end-page: 170 ident: b50 article-title: Incentivizing truthful data quality for quality-aware mobile data crowdsourcing publication-title: Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing – volume: 17 start-page: 147 year: 2017 ident: b36 article-title: Continuous space estimation: Increasing WiFi-based indoor localization resolution without increasing the site-survey effort publication-title: Sensors – start-page: 1 year: 2012 end-page: 6 ident: b40 article-title: A new method to generate and maintain a WiFi fingerprinting database automatically by using RFID publication-title: 2012 International Conference on Indoor Positioning and Indoor Navigation – reference: Y. Chon, N.D. Lane, F. Li, H. Cha, F. Zhao, Automatically characterizing places with opportunistic crowdsensing using smartphones, in: 2012 ACM Conference on Ubiquitous Computing, 2012, pp. 481–490. – volume: 27 year: 2014 ident: b43 article-title: Learning deep features for scene recognition using places database publication-title: Adv. Neural Inf. Process. Syst. – volume: 2 year: 2018 ident: b13 article-title: SweepLoc: Automatic video-based indoor localization by camera sweeping publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. – year: 2017 ident: b42 article-title: Places: A 10 million image database for scene recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: R. Palaniappan, P. Mirowski, T.K. Ho, H. Steck, P. Whiting, M. MacDonald, Autonomous RF Surveying Robot for Indoor Localization and Tracking, in: International Conference on Indoor Positioning and Indoor Navigation, 2011. – reference: M. Azizyan, I. Constandache, R. Roy Choudhury, SurroundSense: mobile phone localization via ambience fingerprinting, in: MobiCom 2009, 2009, pp. 261–272. – volume: 4 year: 2020 ident: b28 article-title: The StoryTeller: Scalable building- and AP-independent deep learning-based floor prediction publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. – year: 2010 ident: b4 article-title: Exchangeable image file format for digital still cameras: Exif version 2.3 publication-title: CIPA DC-008 Translation-2010 – reference: S. He, S.-H.G. Chan, L. Yu, N. Liu, Calibration-free Fusion of Step Counter and Wireless Fingerprints for Indoor Localization, in: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, pp. 897–908. – volume: 7 start-page: 110698 year: 2019 end-page: 110709 ident: b48 article-title: A novel convolutional neural network based indoor localization framework with WiFi fingerprinting publication-title: IEEE Access – start-page: 1 year: 2013 end-page: 10 ident: b30 article-title: SignalSLAM: Simultaneous localization and mapping with mixed WiFi, bluetooth, LTE and magnetic signals publication-title: International Conference on Indoor Positioning and Indoor Navigation – volume: 1 year: 2017 ident: b23 article-title: Cost-sensitive semi-supervised personalized semantic place label recognition using multi-context data publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. – volume: 15 start-page: 1 year: 2019 end-page: 31 ident: b12 article-title: Resource-efficient and automated image-based indoor localization publication-title: ACM Trans. Sensor Netw. – volume: 20 start-page: 5182 year: 2020 ident: b31 article-title: Fusion of the SLAM with Wi-Fi-based positioning methods for mobile robot-based learning data collection, localization, and tracking in indoor spaces publication-title: Sensors – start-page: 101 year: 2019 end-page: 110 ident: b47 article-title: Deep learning in indoor localization using WiFi publication-title: International Telecommunications Conference – volume: 7 start-page: 316 year: 2018 end-page: 327 ident: b19 article-title: Deep convolutional neural networks for indoor localization with CSI images publication-title: IEEE Trans. Netw. Sci. Eng. – start-page: 272 year: 2014 end-page: 277 ident: b52 article-title: Analyzing the quality of crowd sensed WiFi data publication-title: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS) – start-page: 61 year: 2016 end-page: 67 ident: b33 article-title: GraphSLAM-based crowdsourcing framework for indoor Wi-Fi fingerprinting publication-title: 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services – volume: 20 start-page: 3785 year: 2021 end-page: 3799 ident: b37 article-title: WiFi-RITA positioning: Enhanced crowdsourcing positioning based on massive noisy user traces publication-title: IEEE Trans. Wireless Commun. – reference: M. Tachikawa, T. Maekawa, Y. Matsushita, Predicting location semantics combining active and passive sensing with environment-independent classifier, in: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016, pp. 220–231. – year: 2017 ident: b44 article-title: Adam: A method for stochastic optimization – volume: 9 start-page: 2109 year: 2018 end-page: 2124 ident: b24 article-title: Semantic place prediction from crowd-sensed mobile phone data publication-title: J. Ambient Intell. Humaniz. Comput. – reference: J.G. Rodrigues, J.P. Pereira, A. Aguiar, Impact of crowdsourced data quality on travel pattern estimation, in: First ACM Workshop on Mobile Crowdsensing Systems and Applications, 2017, pp. 38–43. – reference: M. Fan, A.T. Adams, K.N. Truong, Public restroom detection on mobile phone via active probing, in: International Symposium on Wearable Computers (ISWC 2014), 2014, pp. 27–34. – start-page: 1263 year: 2018 end-page: 1268 ident: b32 article-title: Robust 2D indoor localization through laser SLAM and visual slam fusion publication-title: 2018 IEEE International Conference on Systems, Man, and Cybernetics – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: b45 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J. Artificial Intelligence Res. – volume: 19 start-page: 1770 year: 2019 end-page: 1785 ident: b38 article-title: Calibration-free indoor positioning using crowdsourced data and multidimensional scaling publication-title: IEEE Trans. Wireless Commun. – volume: 140 start-page: 23 year: 2018 end-page: 31 ident: b10 article-title: Image retrieval using BIM and features from pretrained VGG network for indoor localization publication-title: Build. Environ. – volume: 14 start-page: 1 year: 2015 end-page: 23 ident: b39 article-title: Automatic update of indoor location fingerprints with pedestrian dead reckoning publication-title: ACM Trans. Embedded Comput. Syst. – start-page: 1 year: 2019 end-page: 10 ident: b49 article-title: Wideep: WiFi-based accurate and robust indoor localization system using deep learning publication-title: 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom) – year: 2021 ident: b9 article-title: IndoLabel: Predicting indoor location class by discovering location-specific sensor data motifs publication-title: IEEE Sens. J. – volume: 19 start-page: 2305 year: 2018 end-page: 2312 ident: b15 article-title: CellinDeep: Robust and accurate cellular-based indoor localization via deep learning publication-title: IEEE Sens. J. – year: 2020 ident: b22 article-title: Received signal strength indicator-based indoor localization using distributed set-membership filtering publication-title: IEEE Trans. Cybern. – volume: 12 start-page: 180 year: 2021 ident: b7 article-title: Machine-learning-based user position prediction and behavior analysis for location services publication-title: Information – volume: 9 year: 2014 ident: b5 article-title: Scientists@ Home: what drives the quantity and quality of online citizen science participation? publication-title: PLoS One – volume: 20 start-page: 8164 year: 2020 end-page: 8172 ident: b21 article-title: A novel trilateration algorithm for RSSI-based indoor localization publication-title: IEEE Sens. J. – start-page: 147 year: 2016 end-page: 157 ident: b2 article-title: Modified random forest algorithm for wi–fi indoor localization system publication-title: International Conference on Computational Collective Intelligence – volume: 64 start-page: 4203 year: 2014 end-page: 4214 ident: b51 article-title: Quality-driven auction-based incentive mechanism for mobile crowd sensing publication-title: IEEE Trans. Veh. Technol. – start-page: 1 year: 2019 end-page: 6 ident: b35 article-title: ZeeFi: Zero-effort floor identification with deep learning for indoor localization publication-title: 2019 IEEE Global Communications Conference – reference: J. Krumm, D. Rouhana, Placer: semantic place labels from diary data, in: 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2013, pp. 163–172. – start-page: 1 year: 2015 end-page: 5 ident: b1 article-title: Wi-Fi based indoor location positioning employing random forest classifier publication-title: 2015 International Conference on Indoor Positioning and Indoor Navigation – start-page: 70 year: 2013 end-page: 75 ident: b11 article-title: Image based localization in indoor environments publication-title: 2013 Fourth International Conference on Computing for Geospatial Research and Application – volume: 22 start-page: 575 year: 2021 end-page: 588 ident: b54 article-title: Automatic radio map database maintenance and updating based on crowdsourced samples for indoor localization publication-title: IEEE Sens. J. – start-page: 1 year: 2017 end-page: 6 ident: b18 article-title: CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi publication-title: 2017 IEEE International Conference on Communications – reference: A. Rai, K.K. Chintalapudi, V.N. Padmanabhan, R. Sen, Zee: Zero-effort Crowdsourcing for Indoor Localization, in: 18th Annual International Conference on Mobile Computing and Networking, 2012, pp. 293–304. – start-page: 1 year: 2016 end-page: 8 ident: b3 article-title: An enhanced WiFi indoor localization system based on machine learning publication-title: 2016 International Conference on Indoor Positioning and Indoor Navigation – volume: 3 start-page: 1 year: 2018 end-page: 17 ident: b16 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. – start-page: 1 year: 2019 end-page: 8 ident: b46 article-title: Semi-supervised variational autoencoder for Wifi indoor localization publication-title: 2019 International Conference on Indoor Positioning and Indoor Navigation – volume: 17 start-page: 2224 year: 2017 end-page: 2230 ident: b27 article-title: Improved Wi-Fi RSSI measurement for indoor localization publication-title: IEEE Sens. J. – volume: 27 year: 2014 ident: 10.1016/j.pmcj.2022.101742_b43 article-title: Learning deep features for scene recognition using places database publication-title: Adv. Neural Inf. Process. Syst. – start-page: 1 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b49 article-title: Wideep: WiFi-based accurate and robust indoor localization system using deep learning – start-page: 1 year: 2015 ident: 10.1016/j.pmcj.2022.101742_b1 article-title: Wi-Fi based indoor location positioning employing random forest classifier – volume: 19 start-page: 1770 issue: 3 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b38 article-title: Calibration-free indoor positioning using crowdsourced data and multidimensional scaling publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2019.2957363 – ident: 10.1016/j.pmcj.2022.101742_b17 doi: 10.1145/2971648.2971710 – volume: 3 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b16 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 – year: 2020 ident: 10.1016/j.pmcj.2022.101742_b22 article-title: Received signal strength indicator-based indoor localization using distributed set-membership filtering publication-title: IEEE Trans. Cybern. – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.pmcj.2022.101742_b45 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.953 – volume: 2 issue: 3 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b13 article-title: SweepLoc: Automatic video-based indoor localization by camera sweeping publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. doi: 10.1145/3264930 – ident: 10.1016/j.pmcj.2022.101742_b53 doi: 10.1145/3139243.3139254 – volume: 7 start-page: 110698 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b48 article-title: A novel convolutional neural network based indoor localization framework with WiFi fingerprinting publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2933921 – volume: 20 start-page: 5182 issue: 18 year: 2020 ident: 10.1016/j.pmcj.2022.101742_b31 article-title: Fusion of the SLAM with Wi-Fi-based positioning methods for mobile robot-based learning data collection, localization, and tracking in indoor spaces publication-title: Sensors doi: 10.3390/s20185182 – ident: 10.1016/j.pmcj.2022.101742_b8 doi: 10.1145/2971648.2971684 – start-page: 1 year: 2012 ident: 10.1016/j.pmcj.2022.101742_b40 article-title: A new method to generate and maintain a WiFi fingerprinting database automatically by using RFID – volume: 1 issue: 3 year: 2017 ident: 10.1016/j.pmcj.2022.101742_b23 article-title: Cost-sensitive semi-supervised personalized semantic place label recognition using multi-context data publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. doi: 10.1145/3131903 – volume: 64 start-page: 4203 issue: 9 year: 2014 ident: 10.1016/j.pmcj.2022.101742_b51 article-title: Quality-driven auction-based incentive mechanism for mobile crowd sensing publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2014.2363842 – year: 2021 ident: 10.1016/j.pmcj.2022.101742_b9 article-title: IndoLabel: Predicting indoor location class by discovering location-specific sensor data motifs publication-title: IEEE Sens. J. – start-page: 61 year: 2016 ident: 10.1016/j.pmcj.2022.101742_b33 article-title: GraphSLAM-based crowdsourcing framework for indoor Wi-Fi fingerprinting – volume: 15 start-page: 1 issue: 2 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b12 article-title: Resource-efficient and automated image-based indoor localization publication-title: ACM Trans. Sensor Netw. doi: 10.1145/3284555 – volume: 2 start-page: 1 issue: 1 year: 2017 ident: 10.1016/j.pmcj.2022.101742_b14 article-title: A fingerprint method for indoor localization using autoencoder based deep extreme learning machine publication-title: IEEE Sens. Lett. doi: 10.1109/LSENS.2017.2787651 – volume: 20 start-page: 8164 issue: 14 year: 2020 ident: 10.1016/j.pmcj.2022.101742_b21 article-title: A novel trilateration algorithm for RSSI-based indoor localization publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.2980966 – volume: 7 start-page: 316 issue: 1 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b19 article-title: Deep convolutional neural networks for indoor localization with CSI images publication-title: IEEE Trans. Netw. Sci. Eng. doi: 10.1109/TNSE.2018.2871165 – year: 2017 ident: 10.1016/j.pmcj.2022.101742_b44 – volume: 9 issue: 4 year: 2014 ident: 10.1016/j.pmcj.2022.101742_b5 article-title: Scientists@ Home: what drives the quantity and quality of online citizen science participation? publication-title: PLoS One doi: 10.1371/journal.pone.0090375 – ident: 10.1016/j.pmcj.2022.101742_b6 doi: 10.1145/1614320.1614350 – volume: 17 start-page: 2224 issue: 7 year: 2017 ident: 10.1016/j.pmcj.2022.101742_b27 article-title: Improved Wi-Fi RSSI measurement for indoor localization publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2017.2660522 – start-page: 147 year: 2016 ident: 10.1016/j.pmcj.2022.101742_b2 article-title: Modified random forest algorithm for wi–fi indoor localization system – ident: 10.1016/j.pmcj.2022.101742_b34 doi: 10.1145/2348543.2348580 – ident: 10.1016/j.pmcj.2022.101742_b20 doi: 10.1145/2750858.2804254 – year: 2010 ident: 10.1016/j.pmcj.2022.101742_b4 article-title: Exchangeable image file format for digital still cameras: Exif version 2.3 – volume: 4 issue: 1 year: 2020 ident: 10.1016/j.pmcj.2022.101742_b28 article-title: The StoryTeller: Scalable building- and AP-independent deep learning-based floor prediction publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. doi: 10.1145/3380979 – start-page: 70 year: 2013 ident: 10.1016/j.pmcj.2022.101742_b11 article-title: Image based localization in indoor environments – start-page: 1 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b35 article-title: ZeeFi: Zero-effort floor identification with deep learning for indoor localization – ident: 10.1016/j.pmcj.2022.101742_b41 doi: 10.1145/2370216.2370288 – volume: 140 start-page: 23 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b10 article-title: Image retrieval using BIM and features from pretrained VGG network for indoor localization publication-title: Build. Environ. doi: 10.1016/j.buildenv.2018.05.026 – start-page: 1 year: 2017 ident: 10.1016/j.pmcj.2022.101742_b18 article-title: CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi – volume: 17 start-page: 147 issue: 1 year: 2017 ident: 10.1016/j.pmcj.2022.101742_b36 article-title: Continuous space estimation: Increasing WiFi-based indoor localization resolution without increasing the site-survey effort publication-title: Sensors doi: 10.3390/s17010147 – ident: 10.1016/j.pmcj.2022.101742_b26 doi: 10.1145/2634317.2634320 – ident: 10.1016/j.pmcj.2022.101742_b25 doi: 10.1145/2493432.2493504 – ident: 10.1016/j.pmcj.2022.101742_b29 – start-page: 1 year: 2013 ident: 10.1016/j.pmcj.2022.101742_b30 article-title: SignalSLAM: Simultaneous localization and mapping with mixed WiFi, bluetooth, LTE and magnetic signals – volume: 14 start-page: 1 issue: 2 year: 2015 ident: 10.1016/j.pmcj.2022.101742_b39 article-title: Automatic update of indoor location fingerprints with pedestrian dead reckoning publication-title: ACM Trans. Embedded Comput. Syst. doi: 10.1145/2667226 – start-page: 101 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b47 article-title: Deep learning in indoor localization using WiFi – volume: 22 start-page: 575 issue: 1 year: 2021 ident: 10.1016/j.pmcj.2022.101742_b54 article-title: Automatic radio map database maintenance and updating based on crowdsourced samples for indoor localization publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2021.3128517 – start-page: 1263 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b32 article-title: Robust 2D indoor localization through laser SLAM and visual slam fusion – start-page: 161 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b50 article-title: Incentivizing truthful data quality for quality-aware mobile data crowdsourcing – volume: 20 start-page: 3785 issue: 6 year: 2021 ident: 10.1016/j.pmcj.2022.101742_b37 article-title: WiFi-RITA positioning: Enhanced crowdsourcing positioning based on massive noisy user traces publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2021.3053582 – volume: 12 start-page: 180 issue: 5 year: 2021 ident: 10.1016/j.pmcj.2022.101742_b7 article-title: Machine-learning-based user position prediction and behavior analysis for location services publication-title: Information doi: 10.3390/info12050180 – year: 2017 ident: 10.1016/j.pmcj.2022.101742_b42 article-title: Places: A 10 million image database for scene recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 272 year: 2014 ident: 10.1016/j.pmcj.2022.101742_b52 article-title: Analyzing the quality of crowd sensed WiFi data – start-page: 1 year: 2019 ident: 10.1016/j.pmcj.2022.101742_b46 article-title: Semi-supervised variational autoencoder for Wifi indoor localization – volume: 9 start-page: 2109 issue: 6 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b24 article-title: Semantic place prediction from crowd-sensed mobile phone data publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-017-0549-6 – start-page: 1 year: 2016 ident: 10.1016/j.pmcj.2022.101742_b3 article-title: An enhanced WiFi indoor localization system based on machine learning – volume: 19 start-page: 2305 issue: 6 year: 2018 ident: 10.1016/j.pmcj.2022.101742_b15 article-title: CellinDeep: Robust and accurate cellular-based indoor localization via deep learning publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2018.2885958 |
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