BLIPS: Bluetooth locator for an Indoor Positioning System in Realtime
Authors: 
Shyama Sastha Krishnamoorthy Srinivasan, Siddharth Singh, 
Pushpendra Singh, Mohan KumarAuthors Info & Claims
Shyama Sastha Krishnamoorthy Srinivasan, Siddharth Singh, Pushpendra Singh, Mohan KumarAuthors Info & ClaimsPages 18 - 29
Abstract
Traditional localization systems often rely on a network of external sensors, making the setups cumbersome, expensive, and requiring significant calibration effort. The advent of Bluetooth 5.1 and later versions brought enhancements that enable precise localization using constant tone extension (CTE) in the signal through Angle of Arrival (AoA) and Angle of Departure (AoD) techniques. This work examines the capacity of a single Bluetooth Low-Energy (BLE) locator with an antenna array based on AoA in terms of performance, efficiency, and latency in real-time indoor positioning. While traditional neural networks train measured entities to match calculated distances, we utilize the azimuth and elevation angle components in the AoA measured and train neural networks to match their theoretical counterparts. We conducted extensive experiments in a real-world lab environment, providing ablation studies in the design. The results demonstrate the system’s capability in real-time with many potential interference variables. Under lab conditions, our results show the capacity of a single locator wanes past 4m with the best average accuracy of 0.09m error in positioning within a 5m radius to as much as ∼ 1m of error beyond 6m up to the maximum possible measuring distance in the lab.
References
[1]
Web Admin. 2023. Bluetooth Location Services Solutions - Silicon Labs. https://www.silabs.com/wireless/bluetooth/location-services
[2]
Abien Fred Agarap. 2018. Deep Learning using Rectified Linear Units (ReLU). CoRR abs/1803.08375 (2018). arXiv:1803.08375http://arxiv.org/abs/1803.08375
[3]
Leonid Antsfeld, Boris Chidlovskii, and Dmitrii Borisov. 2020. Magnetic Sensor Based Indoor Positioning by Multi-Channel Deep Regression: Poster Abstract. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems (Virtual Event, Japan) (SenSys ’20). Association for Computing Machinery, New York, NY, USA, 707–708. https://doi.org/10.1145/3384419.3430419
[4]
Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Rajkumar Sethi, Deepak Vasisht, and Dinesh Bharadia. 2020. Deep Learning Based Wireless Localization for Indoor Navigation. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (London, United Kingdom) (MobiCom ’20). Association for Computing Machinery, New York, NY, USA, Article 17, 14 pages. https://doi.org/10.1145/3372224.3380894
[5]
Noor Baha Aldin, Ergun Erçelebi, and Mahmut Aykaç. 2017. An Accurate Indoor RSSI Localization Algorithm Based on Active RFID System with Reference Tags. Wireless Personal Communications 97, 3 (Dec. 2017), 3811–3829. https://doi.org/10.1007/s11277-017-4700-7
[6]
Taylor S Barber. 2019. Performance Analysis of Angle of Arrival Algorithms Applied to Radiofrequency Interference Direction Finding. Master’s thesis. AIR FORCE INSTITUTE OF TECHNOLOGY.
[7]
Luca Barbieri, Mattia Brambilla, Andrea Trabattoni, Stefano Mervic, and Monica Nicoli. 2021. UWB Localization in a Smart Factory: Augmentation Methods and Experimental Assessment. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1–18. https://doi.org/10.1109/TIM.2021.3074403
[8]
Md Fazlay Rabbi Masum Billah, Nurani Saoda, Jiechao Gao, and Bradford Campbell. 2021. BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021). ACM, Nashville TN USA, 132–147. https://doi.org/10.1145/3412382.3458262
[9]
Miran Borić, Rebeca P. Díaz Redondo, and Ana Fernández Vilas. 2018. Space Occupancy through BLE Dynamic Broadcasting. Wireless Communications and Mobile Computing 2018 (Oct. 2018), e2182614. https://doi.org/10.1155/2018/2182614 Publisher: Hindawi.
[10]
Miran Borić, Rebeca P. Díaz Redondo, and Ana Fernández Vilas. 2018. Dynamic Content Distribution over BLE iBeacon Technology: Implementation Challenges. In 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, Thessaloniki, Greece, 910–915. https://doi.org/10.1109/CoDIT.2018.8394958 ISSN: 2576-3555.
[11]
Regin Cabacas and In-Ho Ra. 2021. First Responder Positioning and Localization Based on Optimal Anchor Access Point Selection Using Minimum Uncertainty Propagation for Disaster Scenarios. In The 9th International Conference on Smart Media and Applications (Jeju, Republic of Korea) (SMA 2020). Association for Computing Machinery, New York, NY, USA, 425–428. https://doi.org/10.1145/3426020.3426157
[12]
Zhenghua Chen, Qingchang Zhu, and Yeng Chai Soh. 2016. Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections. IEEE Transactions on Industrial Informatics 12, 4 (2016), 1540–1549. https://doi.org/10.1109/TII.2016.2579265
[13]
Maciej Ciężkowski, Sławomir Romaniuk, and Adam Wolniakowski. 2020. Apparent beacon position estimation for accuracy improvement in lateration positioning system. Measurement 153 (March 2020), 107400. https://doi.org/10.1016/j.measurement.2019.107400
[14]
Paolo Dabove, Vincenzo Di Pietra, Marco Piras, Ansar Abdul Jabbar, and Syed Ali Kazim. 2018. Indoor positioning using Ultra-wide band (UWB) technologies: Positioning accuracies and sensors’ performances. In 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, Monterey, CA, USA, 175–184. https://doi.org/10.1109/PLANS.2018.8373379
[15]
S. P. Dhanushka. 2021. Location-Based Indoor Mobile Advertising. Thesis. University of Colombo School of Computing. https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4236Accepted: 2021-07-27T05:41:12Z.
[16]
Abdelrahman El-Naggar, Amr Wassal, and Khaled Sharaf. 2019. Indoor Positioning Using WiFi RSSI Trilateration and INS Sensor Fusion System Simulation. In Proceedings of the 2019 2nd International Conference on Sensors, Signal and Image Processing (Prague, Czech Republic) (SSIP ’19). Association for Computing Machinery, New York, NY, USA, 21–26. https://doi.org/10.1145/3365245.3365261
[17]
Moustafa Elhamshary, Anas Basalmah, and Moustafa Youssef. 2017. A Fine-Grained Indoor Location-Based Social Network. IEEE Transactions on Mobile Computing 16, 5 (2017), 1203–1217. https://doi.org/10.1109/TMC.2016.2591532
[18]
Mahmoud Elsanhoury, Petteri Mäkelä, Janne Koljonen, Petri Välisuo, Ahm Shamsuzzoha, Timo Mantere, Mohammed Elmusrati, and Heidi Kuusniemi. 2022. Precision Positioning for Smart Logistics Using Ultra-Wideband Technology-Based Indoor Navigation: A Review. IEEE Access 10 (2022), 44413–44445. https://doi.org/10.1109/ACCESS.2022.3169267
[19]
Dominik Esslinger, Martin Oberdorfer, Michael Zeitz, and Cristina Tarín. 2020. Improving ultrasound-based indoor localization systems for quality assurance in manual assembly. In 2020 IEEE International Conference on Industrial Technology (ICIT). IEEE, Buenos Aires, Argentina, 563–570. https://doi.org/10.1109/ICIT45562.2020.9067248
[20]
Xirui Fan, Jing Wu, Chengnian Long, and Yanmin Zhu. 2017. Accurate and Low-Cost Mobile Indoor Localization with 2-D Magnetic Fingerprints. In Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications (Delft, Netherlands) (CrowdSenSys ’17). Association for Computing Machinery, New York, NY, USA, 13–18. https://doi.org/10.1145/3139243.3139244
[21]
Wei Fang, Wei Fan, Wei Ji, Lei Han, Shuhong Xu, Lianyu Zheng, and Lihui Wang. 2022. Distributed cognition based localization for AR-aided collaborative assembly in industrial environments. Robotics and Computer-Integrated Manufacturing 75 (2022), 102292. https://doi.org/10.1016/j.rcim.2021.102292
[22]
J. C. Fuentes Michel, Mark Christmann, Michael Fiegert, Peter Gulden, and Martin Vossiek. 2006. Multisensor Based Indoor Vehicle Localization System for Production and Logistic. In 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. IEEE, Heidelberg, Germany, 553–558. https://doi.org/10.1109/MFI.2006.265666
[23]
Giovanni Fusco and James M. Coughlan. 2020. Indoor Localization for Visually Impaired Travelers Using Computer Vision on a Smartphone. In Proceedings of the 17th International Web for All Conference (Taipei, Taiwan) (W4A ’20). Association for Computing Machinery, New York, NY, USA, Article 8, 11 pages. https://doi.org/10.1145/3371300.3383345
[24]
Jijun Geng, Xuexiang Yu, Congcong Wu, and Guoqing Zhang. 2023. Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors. Micromachines 14, 6 (2023). https://doi.org/10.3390/mi14061252
[25]
Alasdair Gilchrist. 2016. Industry 4.0: The Industrial Internet of Things (1st ed.). Apress, USA.
[26]
Davide Giovanelli and Elisabetta Farella. 2018. RSSI or Time-of-flight for Bluetooth Low Energy based localization? An experimental evaluation. In 2018 11th IFIP Wireless and Mobile Networking Conference (WMNC). IEEE, Prague, 1–8. https://doi.org/10.23919/WMNC.2018.8480847
[27]
Linqing Gui, Shuwen Xu, Fu Xiao, Feng Shu, and Shui Yu. 2022. Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems. IEEE Transactions on Mobile Computing 21, 10 (2022), 3731–3743. https://doi.org/10.1109/TMC.2021.3058952
[28]
Zohreh Hajiakhondi-Meybodi, Mohammad Salimibeni, Konstantinos N. Plataniotis, and Arash Mohammadi. 2020. Bluetooth Low Energy-based Angle of Arrival Estimation via Switch Antenna Array for Indoor Localization. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, Rustenburg, South Africa, 1–6. https://doi.org/10.23919/FUSION45008.2020.9190573
[29]
M. Hazas and A. Hopper. 2006. Broadband ultrasonic location systems for improved indoor positioning. IEEE Transactions on Mobile Computing 5, 5 (2006), 536–547. https://doi.org/10.1109/TMC.2006.57
[30]
Niklas Hesslein, Mike Wesselhöft, Johannes Hinckeldeyn, and Jochen Kreutzfeldt. 2021. Industrial Indoor Localization: Improvement of Logistics Processes Using Location Based Services. In Advances in Automotive Production Technology – Theory and Application, Philipp Weißgraeber, Frieder Heieck, and Clemens Ackermann (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 460–467.
[31]
Mohd Nizam Husen and Sukhan Lee. 2014. Indoor Human Localization with Orientation Using WiFi Fingerprinting. In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (Siem Reap, Cambodia) (ICUIMC ’14). Association for Computing Machinery, New York, NY, USA, Article 109, 6 pages. https://doi.org/10.1145/2557977.2557980
[32]
Silicon Laboratories Inc.2022. QSG175: Silicon Labs Direction-Finding Solution Quick-Start Guide., 19 pages. https://www.silabs.com/documents/public/quick-start-guides/qsg175-direction-finding-solution-quick-start-guide.pdf
[33]
Feiyu Jin, Kai Liu, Hao Zhang, Joseph Kee-Yin Ng, Songtao Guo, Victor C. S. Lee, and Sang H. Son. 2020. Toward Scalable and Robust Indoor Tracking: Design, Implementation, and Evaluation. IEEE Internet of Things Journal 7, 2 (Feb. 2020), 1192–1204. https://doi.org/10.1109/JIOT.2019.2953376
[34]
Diederik P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arxiv:1412.6980 [cs.LG] https://arxiv.org/abs/1412.6980
[35]
Oluwatayo Y. Kolawole and Mythri Hunukumbure. 2022. UAV Based 5G Indoor Localization for Emergency Services. In Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond (Sydney, NSW, Australia) (DroneCom ’22). Association for Computing Machinery, New York, NY, USA, 43–48. https://doi.org/10.1145/3555661.3560862
[36]
Jayakanth Kunhoth, AbdelGhani Karkar, Somaya Al-Maadeed, and Abdulla Al-Ali. 2020. Indoor positioning and wayfinding systems: a survey. Human-centric Computing and Information Sciences 10, 1 (May 2020), 18. https://doi.org/10.1186/s13673-020-00222-0
[37]
Masaki Kuribayashi, Tatsuya Ishihara, Daisuke Sato, Jayakorn Vongkulbhisal, Karnik Ram, Seita Kayukawa, Hironobu Takagi, Shigeo Morishima, and Chieko Asakawa. 2023. PathFinder: Designing a Map-Less Navigation System for Blind People in Unfamiliar Buildings. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 41, 16 pages. https://doi.org/10.1145/3544548.3580687
[38]
C. K. M. Lee, C. M. Ip, Taezoon Park, and S.Y. Chung. 2019. A Bluetooth Location-based Indoor Positioning System for Asset Tracking in Warehouse. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, Macao, China, 1408–1412. https://doi.org/10.1109/IEEM44572.2019.8978639
[39]
Qianfeng Lin, Jooyoung Son, and Hyeongseol Shin. 2023. A self-learning mean optimization filter to improve bluetooth 5.1 AoA indoor positioning accuracy for ship environments. Journal of King Saud University - Computer and Information Sciences 35, 3 (2023), 59–73. https://doi.org/10.1016/j.jksuci.2023.01.019
[40]
Fang-Tsung Liu, Chiung-Hsing Chen, Yi-Chun Kao, Chih-Ming Hong, and Chia-Ying Yang. 2017. Improved ZigBee module based on fuzzy model for indoor positioning system. In 2017 International Conference on Applied System Innovation (ICASI). IEEE, Sapporo, Japan, 1331–1334. https://doi.org/10.1109/ICASI.2017.7988150
[41]
Chris Xiaoxuan Lu, Yang Li, Peijun Zhao, Changhao Chen, Linhai Xie, Hongkai Wen, Rui Tan, and Niki Trigoni. 2018. Simultaneous Localization and Mapping with Power Network Electromagnetic Field. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (New Delhi, India) (MobiCom ’18). Association for Computing Machinery, New York, NY, USA, 607–622. https://doi.org/10.1145/3241539.3241540
[42]
Yongtao Ma, Chenglong Tian, and Yue Jiang. 2019. A Multitag Cooperative Localization Algorithm Based on Weighted Multidimensional Scaling for Passive UHF RFID. IEEE Internet of Things Journal 6, 4 (2019), 6548–6555. https://doi.org/10.1109/JIOT.2019.2907771
[43]
Mojtaba Masoudinejad, Aswin Karthik Ramachandran Venkatapathy, David Tondorf, Danny Heinrich, Robert Falkenberg, and Markus Buschhoff. 2018. Machine Learning Based Indoor Localisation Using Environmental Data in PhyNetLab Warehouse. In Smart SysTech 2018; European Conference on Smart Objects, Systems and Technologies. VDE, Munich, Germany, 1–8.
[44]
A. Moura, J. Antunes, A. Dias, A. Martins, and J. Almeida. 2021. Graph-SLAM Approach for Indoor UAV Localization in Warehouse Logistics Applications. In 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, Santa Maria da Feira, Portugal, 4–11. https://doi.org/10.1109/ICARSC52212.2021.9429791
[45]
Sharareh Naghdi and Kyle O’Keefe. 2022. Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration. Sensors (Basel, Switzerland) 22, 12 (June 2022), 4320. https://doi.org/10.3390/s22124320
[46]
Rajalakshmi Nandakumar, Vikram Iyer, and Shyamnath Gollakota. 2018. 3D Localization for Sub-Centimeter Sized Devices. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems (Shenzhen, China) (SenSys ’18). Association for Computing Machinery, New York, NY, USA, 108–119. https://doi.org/10.1145/3274783.3274851
[47]
Thu L. N. Nguyen, Tuan D. Vy, Kwan-Soo Kim, Chenxiang Lin, and Yoan Shin. 2021. Smartphone-Based Indoor Tracking in Multiple-Floor Scenarios. IEEE Access 9 (2021), 141048–141063. https://doi.org/10.1109/ACCESS.2021.3119577 Conference Name: IEEE Access.
[48]
Huthaifa Obeidat, Wafa Shuaieb, Omar Obeidat, and Raed Abd-Alhameed. 2021. A Review of Indoor Localization Techniques and Wireless Technologies. Wireless Personal Communications 119, 1 (July 2021), 289–327. https://doi.org/10.1007/s11277-021-08209-5
[49]
Ayan Kumar Panja, Dhritesh Bhagat, Sarmistha Neogy, and Chandreyee Chowdhury. 2022. Framework for Remote Device Localization and Application Level Visualization for Emergency Service Providers. In Adjunct Publication of the 24th International Conference on Human-Computer Interaction with Mobile Devices and Services (Vancouver, BC, Canada) (MobileHCI ’22). Association for Computing Machinery, New York, NY, USA, Article 19, 4 pages. https://doi.org/10.1145/3528575.3551445
[50]
Giovanni Pau, Fabio Arena, Yonas Engida Gebremariam, and Ilsun You. 2021. Bluetooth 5.1: An Analysis of Direction Finding Capability for High-Precision Location Services. Sensors 21, 11 (Jan. 2021), 3589. https://doi.org/10.3390/s21113589 Number: 11 Publisher: Multidisciplinary Digital Publishing Institute.
[51]
Chao Peng, Hong Jiang, and Liangdong Qu. 2021. Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features. IEEE Access 9 (2021), 15441–15451. https://doi.org/10.1109/ACCESS.2021.3052567
[52]
Milica Petrović, Maciej Ciężkowski, Sławomir Romaniuk, Adam Wolniakowski, and Zoran Miljković. 2021. A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System. Sensors 21, 24 (Jan. 2021), 8204. https://doi.org/10.3390/s21248204 Number: 24 Publisher: Multidisciplinary Digital Publishing Institute.
[53]
James Pieszala, Gabriel Diaz, Jeff Pelz, Jacqueline Speir, and Reynold Bailey. 2016. 3D Gaze Point Localization and Visualization Using LiDAR-Based 3D Reconstructions. In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (Charleston, South Carolina) (ETRA ’16). Association for Computing Machinery, New York, NY, USA, 201–204. https://doi.org/10.1145/2857491.2857545
[54]
Xinyou Qiu, Bowen Wang, Jian Wang, and Yuan Shen. 2020. AOA-Based BLE Localization with Carrier Frequency Offset Mitigation. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, Dublin, Ireland, 1–5. https://doi.org/10.1109/ICCWorkshops49005.2020.9145137
[55]
Ramiro Ramirez, Chien-Yi Huang, Che-An Liao, Po-Ting Lin, Hsin-Wei Lin, and Shu-Hao Liang. 2021. A Practice of BLE RSSI Measurement for Indoor Positioning. Sensors 21, 15 (Jan. 2021), 5181. https://doi.org/10.3390/s21155181 Number: 15 Publisher: Multidisciplinary Digital Publishing Institute.
[56]
D.I.B. Randeniya, M. Gunaratne, S. Sarkar, and A. Nazef. 2008. Calibration of inertial and vision systems as a prelude to multi-sensor fusion. Transportation Research Part C: Emerging Technologies 16, 2 (2008), 255–274. https://doi.org/10.1016/j.trc.2007.08.003
[57]
O. Rashid, P. Coulton, and R. Edwards. 2005. Implementing location based information/advertising for existing mobile phone users in indoor/urban environments. In International Conference on Mobile Business (ICMB’05). IEEE, Sydney, NSW, Australia, 377–383. https://doi.org/10.1109/ICMB.2005.45
[58]
Darshana Rathnayake, Meeralakshmi Radhakrishnan, Inseok Hwang, and Archan Misra. 2023. LILOC: Enabling Precise 3D Localization in Dynamic Indoor Environments Using LiDARs. In Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation (San Antonio, TX, USA) (IoTDI ’23). Association for Computing Machinery, New York, NY, USA, 158–171. https://doi.org/10.1145/3576842.3582364
[59]
András Rácz-Szabó, Tamás Ruppert, László Bántay, Andreas Löcklin, László Jakab, and János Abonyi. 2020. Real-Time Locating System in Production Management. Sensors 20, 23 (2020). https://doi.org/10.3390/s20236766
[60]
Juan Manuel Vera-Diaz, Daniel Pizarro, and Javier Macias-Guarasa. 2018. Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates. Sensors 18, 10 (2018). https://doi.org/10.3390/s18103418
[61]
Bowen Wang, Yunlong Wang, Xinyou Qiu, and Yuan Shen. 2021. BLE Localization With Polarization Sensitive Array. IEEE Wireless Communications Letters 10, 5 (2021), 1014–1017. https://doi.org/10.1109/LWC.2021.3055558
[62]
William Van Woensel, Patrice C. Roy, Syed Sibte Raza Abidi, and Samina Raza Abidi. 2020. Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods. Artificial Intelligence in Medicine 108 (2020), 101931. https://doi.org/10.1016/j.artmed.2020.101931
[63]
Martin Woolley. 2020. Bluetooth® Core Specification Version 5.1 Feature Overview. https://www.bluetooth.com/bluetooth-resources/bluetooth-core-specification-v5-1-feature-overview/
[64]
Chenshu Wu, Feng Zhang, Beibei Wang, and K. J. Ray Liu. 2020. EasiTrack: Decimeter-Level Indoor Tracking With Graph-Based Particle Filtering. IEEE Internet of Things Journal 7, 3 (March 2020), 2397–2411. https://doi.org/10.1109/JIOT.2019.2958040
[65]
Wei Wu, Leidi Shen, Zhiheng Zhao, Ming Li, and George Q. Huang. 2022. Industrial IoT and Long Short-Term Memory Network-Enabled Genetic Indoor-Tracking for Factory Logistics. IEEE Transactions on Industrial Informatics 18, 11 (2022), 7537–7548. https://doi.org/10.1109/TII.2022.3146598
[66]
Runming Yang, Xiaolong Yang, Jiacheng Wang, Mu Zhou, Zengshan Tian, and Lingxia Li. 2022. Decimeter Level Indoor Localization Using WiFi Channel State Information. IEEE Sensors Journal 22, 6 (2022), 4940–4950. https://doi.org/10.1109/JSEN.2021.3067144
[67]
Hongyun Ye, Biao Yang, Zhiqiang Long, and Chunhui Dai. 2022. A Method of Indoor Positioning by Signal Fitting and PDDA Algorithm Using BLE AOA Device. IEEE Sensors Journal 22, 8 (April 2022), 7877–7887. https://doi.org/10.1109/JSEN.2022.3141739 Conference Name: IEEE Sensors Journal.
[68]
Zhendong Yin, Xu Jiang, Zhutian Yang, Nan Zhao, and Yunfei Chen. 2019. WUB-IP: A High-Precision UWB Positioning Scheme for Indoor Multiuser Applications. IEEE Systems Journal 13, 1 (2019), 279–288. https://doi.org/10.1109/JSYST.2017.2766690
[69]
Feng Zhang, Chen Chen, Beibei Wang, Hung-Quoc Lai, Yi Han, and K. J. Ray Liu. 2018. WiBall: A Time-Reversal Focusing Ball Method for Decimeter-Accuracy Indoor Tracking. IEEE Internet of Things Journal 5, 5 (Oct. 2018), 4031–4041. https://doi.org/10.1109/JIOT.2018.2854825 Conference Name: IEEE Internet of Things Journal.
[70]
Mingyang Zhang, Jie Jia, Jian Chen, Yansha Deng, Xingwei Wang, and Abdol Hamid Aghvami. 2021. Indoor Localization Fusing WiFi With Smartphone Inertial Sensors Using LSTM Networks. IEEE Internet of Things Journal 8, 17 (2021), 13608–13623. https://doi.org/10.1109/JIOT.2021.3067515
[71]
Yuan Zhuang, Jun Yang, You Li, Longning Qi, and Naser El-Sheimy. 2016. Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons. Sensors 16, 5 (2016). https://doi.org/10.3390/s16050596
Index Terms
- BLIPS: Bluetooth locator for an Indoor Positioning System in Realtime
Recommendations
Improving indoor positioning system using weighted linear least square and neural network
Indoor positioning has grasped great attention in recent years. Many of those technologies are related to the problem of determining the position of an object in space, such as the robot, people, and so on. In this paper, we combine a range-free method, ...
A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network
In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also ...
Real-time indoor positioning and route guidance system by using beacons
Real-time tracking of objects an indoor environment has become important in numerous industries, including shopping, logistics, advertising, and healthcare. This paper represents a smartphone-based real time indoor positioning and route guidance system ...
Comments
Information & Contributors
Information
Published In
July 2024
354 pages
ISBN:9798400710483
DOI:10.1145/3674829
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 August 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
- iHub Anubhuti IIIT Foundation
Conference
COMPASS '24: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies
July 8 - 11, 2024
New Delhi, India
Acceptance Rates
Overall Acceptance Rate 25 of 50 submissions, 50%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 74Total Downloads
- Downloads (Last 12 months)74
- Downloads (Last 6 weeks)3
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format