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An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method

Year 2024, Volume: 40 Issue: 1, 92 - 107, 30.04.2024

Emre Yüksek Ahmet Gürkan Yüksek

Abstract

Localization systems have an important place in many areas. GPS (Global Positioning Systems) using data from satellites gives successful results in localization systems. However, localization systems such as GPS, which can be quite successful outdoors, do not achieve the same success indoors because the satellite viewing angle cannot be maintained continuously or due to low reception quality. In this respect, there is a need for localization systems that can provide the most precise localization with the least cost in the interior. This study aims to improve fingerprint-based localization systems, which is a localization method based on Received Signal Strength Indicator (RSSI) data using ANFIS. The proposed system has been shown to give more successful results than the methods frequently used in the literature.

Keywords

Indoor Localization ANFIS Fingerprinting

References

  • Vanneschi L., Silva S. 2023. Lectures on Intelligent Systems. 1st edition. Springer Nature. Switzerland, 349s.
  • Xiao Q. 2023. A review: Wireless sensor network location. Journal of Physics, 2580(1), 1-7.
  • Brena RF, García-Vázquez JP, Galván-Tejada CE, Muñoz-Rodriguez D, Vargas-Rosales C, Fangmeyer J. 2017. Evolution of Indoor Positioning Technologies: A Survey. Journal of Sensors, 2017(1), 1-21.
  • Frattasi S, Rosa FD. 2017. Mobile Positioning and Tracking: From Conventional to Cooperative Techniques. 2nd edition, John Wiley & Sons. New York, USA, 416s.
  • Zandian R. 2019. Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations. Universität Bielefeld, PhD Thesis, 255s, Biefeld.
  • Dziubany M, Machhamer R, Laux H, Schmeink A, Gollmer KU, Burger G, Dartmann G. 2018. Machine Learning Based Indoor Localization Using a Representative k-Nearest-Neighbor Classifier on a Low-Cost IoT-Hardware. 26th European Signal Processing Conference, Rome, Italy.
  • Bai YB. 2016. Development of a WiFi and RFID based indoor location and mobility tracking system. Royal Melbourne Institute of Technology, PhD Thesis, 233s, Melbourne Australia.
  • Gao X. 2018. UWB Indoor Localization System. The George Washington University, Master Thesis, 178s, Ann Arbor USA.
  • Liu H, Darabi H, Banarjee P, Liu J. 2007. Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080.
  • Gu Y, Lo A, Niemegeers I. 2009. A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials, 11(1), 13-32.
  • Deak G, Curran K, Condell J. 2012. Review: A survey of active and passive indoor localisation systems. Computer Communications, 35(16), 1939-1954.
  • Han Z. 2016. Robust and accurate localization algorithms for indoor positioning and navigation. Nanyang Technological University, PhD Thesis, 216s, Singapore.
  • Sumitra ID, Supatmi S, Hou R. 2018. Enhancement of Indoor Localization Algorithms in Wireless Sensor Networks: A Survey. IOP Conference Series: Materials Science and Engineering, 9 May, Bandung, Indonesia.
  • Doiphode SR, Bakal JW, Gedam M. 2016. Survey of Indoor Positioning Measurements, Methods and Techniques. Internal Journal of Computer Applications, 140(7), 1-4.
  • Liu J. 2014. Survey of wireless based indoor localization technologies. Department of Science & Engineering, Washington University, 2014.
  • Al-Ammar MA, Alhadhrami S, Al-Salman A, Alarifi A, Al-Khalifa HS, Alnafessah A, Alsaleh M. 2014. Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms. 2014 International Conference on Cyberworlds, Santander, Spain.
  • Al Nuaimi K, Kamel H. 2011. A survey of indoor positioning systems and algorithms. 2011 International Conference on Innovations in Information Technology, Abu Dhabi, United Arab Emirates.
  • Oguntala G, Abd-Alhameed R, Jones S, Noras J, Patwary M, Rodriguez J. 2018. Indoor location identification technologies for real-time IoT-based applications: An inclusive survey. Computer Science Review, 30(2018), 55-79.
  • Malavalli R, Earthperson A, Gupta. 2017. Indoor Localization Through Machine Learning on WiFi Fingerprints. International Conference on Indoor Positioning and Indoor Navigation, Sapporo, Japan.
  • Mascharka D, Manley E. 2016. Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors. 2016 13th IEEE Annual Consumer Communications & Networking Conference, Las Vegas, USA.
  • Salamah AH, Tamazin M, Sharkas MA, Khedr M. 2016. An enhanced WiFi indoor localization system based on machine learning. 2016 International Conference on Indoor Positioning and Indoor Navigation, Alcala de Henares, Spain.
  • Hsieh JY, Fan CH, Liao JZ, Hsu JY, Chen H. Study on the application of indoor positioning based on low power Bluetooth device combined with Kalman Filter and machine learning. EasyChair PrePrint, 2516-2524.
  • AlHajri MI, Ali NT, Shubair RM. 2019. Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach. IEEE Antennas and Wireless Propagation Letterst, 18(11), 2306-2310.
  • Terán M, Carrillo H, Parra C. 2018. WLAN-BLE Based Indoor Positioning System using Machine Learning Cloud Services. 2018 IEEE 2nd Colombian Conference on Robotics and Automation, Barranquilla, Colombia.
  • Chuenurajit T, Phimmasean S, Cherntanomwong P. 2013. Robustness of 3D indoor localization based on fingerprint technique in wireless sensor networks. 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand.
  • Jang JSR. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
  • Jang JSR. 1991. Fuzzy modeling using generalized neural networks and kalman filter algorithm. Association for the Advancement of Artificial Intelligence, 91(1991), 762-767.
  • Tsoukalas LH, Uhrig RE. 1996. Fuzzy and neural approaches in engineering. 1st edition. John Wiley & Sons. New York, USA, 600s.
  • Buragohain M. 2009. Adaptive network based fuzzy inference system (ANFIS) as a tool for system identification with special emphasis on training data minimization. Indian Institute of Technology Guwahati, PhD Thesis, 110s, Guwahati, India.
  • Franklin GF, Powell JD, Workman ML. 1997. Digital control of dynamic systems. 3rd edition. Addison-Wesley. New York, USA, 794s.
  • Özgan E, Kap T, Beycioğlu A, Emiroğlu M. 2009. Asfalt Betonunda Marshall Stabilitesinin Uyarlamalı Sinirsel Bulanık Mantık Yaklaşımı ile Tahmini. 5. Uluslararası İleri Teknolojiler Sempozyumu, Karabük, Turkey.
  • Güney K, Sarıkaya N. 2008. Dairesel Mikroşerit Antenlerin Yama Yarıçapının Çeşitli Algoritmalarla Optimize Edilen Bulanık Mantık Sistemine Dayalı Uyarlanır Ağlar İle Hesaplanması. Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyumu, Bursa, Turkey.
  • Laitinen E, Lohan, ES. 2016. On the Choice of Access Point Selection Criterion and Other Position Estimation Characteristics for WLAN-Based Indoor Positioning. Sensors, 16(5), 737.
  • Navindoor. “A simulation tool for the design, testing and evaluation of localization algorithms”. https://deustotech.github.io/navindoor-documentation/
  • MATLAB. Genfis Options. https://www.mathworks.com/help/fuzzy/genfisoptions.html
  • González-Sopeña JM, Pakrashi V, Ghosh B, 2021. An overview of performance evaluation metrics for short-term statistical wind power forecasting. Renewable and Sustainable Energy Reviews, 138, 110515.

RSSI Sinyalleri ve Fingerprinting Yöntemi Kullanılarak Noktasal Konum Algılama Sisteminin ANFIS ile İyileştirilmesine Yönelik Yenilikçi Bir Yaklaşım

Year 2024, Volume: 40 Issue: 1, 92 - 107, 30.04.2024

Emre Yüksek Ahmet Gürkan Yüksek

Abstract

Localization systems have an important place in many areas. GPS (Global Positioning Systems) using data from satellites gives successful results in localization systems. However, localization systems such as GPS, which can be quite successful outdoors, do not achieve the same success indoors because the satellite viewing angle cannot be maintained continuously or due to low reception quality. In this respect, there is a need for localization systems that can provide the most precise localization with the least cost in the interior. This study aims to improve fingerprint-based localization systems, which is a localization method based on Received Signal Strength Indicator (RSSI) data using ANFIS. The proposed system has been shown to give more successful results than the methods frequently used in the literature.

Keywords

İç Mekân Konumlandırma ANFIS Parmakizi

References

  • Vanneschi L., Silva S. 2023. Lectures on Intelligent Systems. 1st edition. Springer Nature. Switzerland, 349s.
  • Xiao Q. 2023. A review: Wireless sensor network location. Journal of Physics, 2580(1), 1-7.
  • Brena RF, García-Vázquez JP, Galván-Tejada CE, Muñoz-Rodriguez D, Vargas-Rosales C, Fangmeyer J. 2017. Evolution of Indoor Positioning Technologies: A Survey. Journal of Sensors, 2017(1), 1-21.
  • Frattasi S, Rosa FD. 2017. Mobile Positioning and Tracking: From Conventional to Cooperative Techniques. 2nd edition, John Wiley & Sons. New York, USA, 416s.
  • Zandian R. 2019. Ultra-wideband Based Indoor Localization of Mobile Nodes in ToA and TDoA Configurations. Universität Bielefeld, PhD Thesis, 255s, Biefeld.
  • Dziubany M, Machhamer R, Laux H, Schmeink A, Gollmer KU, Burger G, Dartmann G. 2018. Machine Learning Based Indoor Localization Using a Representative k-Nearest-Neighbor Classifier on a Low-Cost IoT-Hardware. 26th European Signal Processing Conference, Rome, Italy.
  • Bai YB. 2016. Development of a WiFi and RFID based indoor location and mobility tracking system. Royal Melbourne Institute of Technology, PhD Thesis, 233s, Melbourne Australia.
  • Gao X. 2018. UWB Indoor Localization System. The George Washington University, Master Thesis, 178s, Ann Arbor USA.
  • Liu H, Darabi H, Banarjee P, Liu J. 2007. Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080.
  • Gu Y, Lo A, Niemegeers I. 2009. A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials, 11(1), 13-32.
  • Deak G, Curran K, Condell J. 2012. Review: A survey of active and passive indoor localisation systems. Computer Communications, 35(16), 1939-1954.
  • Han Z. 2016. Robust and accurate localization algorithms for indoor positioning and navigation. Nanyang Technological University, PhD Thesis, 216s, Singapore.
  • Sumitra ID, Supatmi S, Hou R. 2018. Enhancement of Indoor Localization Algorithms in Wireless Sensor Networks: A Survey. IOP Conference Series: Materials Science and Engineering, 9 May, Bandung, Indonesia.
  • Doiphode SR, Bakal JW, Gedam M. 2016. Survey of Indoor Positioning Measurements, Methods and Techniques. Internal Journal of Computer Applications, 140(7), 1-4.
  • Liu J. 2014. Survey of wireless based indoor localization technologies. Department of Science & Engineering, Washington University, 2014.
  • Al-Ammar MA, Alhadhrami S, Al-Salman A, Alarifi A, Al-Khalifa HS, Alnafessah A, Alsaleh M. 2014. Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms. 2014 International Conference on Cyberworlds, Santander, Spain.
  • Al Nuaimi K, Kamel H. 2011. A survey of indoor positioning systems and algorithms. 2011 International Conference on Innovations in Information Technology, Abu Dhabi, United Arab Emirates.
  • Oguntala G, Abd-Alhameed R, Jones S, Noras J, Patwary M, Rodriguez J. 2018. Indoor location identification technologies for real-time IoT-based applications: An inclusive survey. Computer Science Review, 30(2018), 55-79.
  • Malavalli R, Earthperson A, Gupta. 2017. Indoor Localization Through Machine Learning on WiFi Fingerprints. International Conference on Indoor Positioning and Indoor Navigation, Sapporo, Japan.
  • Mascharka D, Manley E. 2016. Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors. 2016 13th IEEE Annual Consumer Communications & Networking Conference, Las Vegas, USA.
  • Salamah AH, Tamazin M, Sharkas MA, Khedr M. 2016. An enhanced WiFi indoor localization system based on machine learning. 2016 International Conference on Indoor Positioning and Indoor Navigation, Alcala de Henares, Spain.
  • Hsieh JY, Fan CH, Liao JZ, Hsu JY, Chen H. Study on the application of indoor positioning based on low power Bluetooth device combined with Kalman Filter and machine learning. EasyChair PrePrint, 2516-2524.
  • AlHajri MI, Ali NT, Shubair RM. 2019. Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach. IEEE Antennas and Wireless Propagation Letterst, 18(11), 2306-2310.
  • Terán M, Carrillo H, Parra C. 2018. WLAN-BLE Based Indoor Positioning System using Machine Learning Cloud Services. 2018 IEEE 2nd Colombian Conference on Robotics and Automation, Barranquilla, Colombia.
  • Chuenurajit T, Phimmasean S, Cherntanomwong P. 2013. Robustness of 3D indoor localization based on fingerprint technique in wireless sensor networks. 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand.
  • Jang JSR. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
  • Jang JSR. 1991. Fuzzy modeling using generalized neural networks and kalman filter algorithm. Association for the Advancement of Artificial Intelligence, 91(1991), 762-767.
  • Tsoukalas LH, Uhrig RE. 1996. Fuzzy and neural approaches in engineering. 1st edition. John Wiley & Sons. New York, USA, 600s.
  • Buragohain M. 2009. Adaptive network based fuzzy inference system (ANFIS) as a tool for system identification with special emphasis on training data minimization. Indian Institute of Technology Guwahati, PhD Thesis, 110s, Guwahati, India.
  • Franklin GF, Powell JD, Workman ML. 1997. Digital control of dynamic systems. 3rd edition. Addison-Wesley. New York, USA, 794s.
  • Özgan E, Kap T, Beycioğlu A, Emiroğlu M. 2009. Asfalt Betonunda Marshall Stabilitesinin Uyarlamalı Sinirsel Bulanık Mantık Yaklaşımı ile Tahmini. 5. Uluslararası İleri Teknolojiler Sempozyumu, Karabük, Turkey.
  • Güney K, Sarıkaya N. 2008. Dairesel Mikroşerit Antenlerin Yama Yarıçapının Çeşitli Algoritmalarla Optimize Edilen Bulanık Mantık Sistemine Dayalı Uyarlanır Ağlar İle Hesaplanması. Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyumu, Bursa, Turkey.
  • Laitinen E, Lohan, ES. 2016. On the Choice of Access Point Selection Criterion and Other Position Estimation Characteristics for WLAN-Based Indoor Positioning. Sensors, 16(5), 737.
  • Navindoor. “A simulation tool for the design, testing and evaluation of localization algorithms”. https://deustotech.github.io/navindoor-documentation/
  • MATLAB. Genfis Options. https://www.mathworks.com/help/fuzzy/genfisoptions.html
  • González-Sopeña JM, Pakrashi V, Ghosh B, 2021. An overview of performance evaluation metrics for short-term statistical wind power forecasting. Renewable and Sustainable Energy Reviews, 138, 110515.
There are 36 citations in total.

Details

Primary Language English
Subjects Context Learning, Neural Networks, Machine Learning (Other)
Journal Section Articles
Authors

Emre Yüksek 0000-0002-1885-5539

Ahmet Gürkan Yüksek 0000-0001-7709-6360

Early Pub Date April 30, 2024
Publication Date April 30, 2024
Submission Date April 16, 2024
Acceptance Date April 23, 2024
Published in Issue Year 2024 Volume: 40 Issue: 1

Cite

APA Yüksek, E., & Yüksek, A. G. (2024). An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 40(1), 92-107.
AMA Yüksek E, Yüksek AG. An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. April 2024;40(1):92-107.
Chicago Yüksek, Emre, and Ahmet Gürkan Yüksek. “An Innovative Approach to Improve Point Location Detection System With ANFIS Using RSSI Signals and Fingerprinting Method”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 40, no. 1 (April 2024): 92-107.
EndNote Yüksek E, Yüksek AG (April 1, 2024) An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 40 1 92–107.
IEEE E. Yüksek and A. G. Yüksek, “An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 40, no. 1, pp. 92–107, 2024.
ISNAD Yüksek, Emre - Yüksek, Ahmet Gürkan. “An Innovative Approach to Improve Point Location Detection System With ANFIS Using RSSI Signals and Fingerprinting Method”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 40/1 (April 2024), 92-107.
JAMA Yüksek E, Yüksek AG. An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2024;40:92–107.
MLA Yüksek, Emre and Ahmet Gürkan Yüksek. “An Innovative Approach to Improve Point Location Detection System With ANFIS Using RSSI Signals and Fingerprinting Method”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 40, no. 1, 2024, pp. 92-107.
Vancouver Yüksek E, Yüksek AG. An Innovative Approach to Improve Point Location Detection System with ANFIS using RSSI Signals and Fingerprinting Method. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2024;40(1):92-107.
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