Sezgisel Optimizasyon Kullanan Ağırlıklı Görünürlük Grafı Tabanlı Kapalı Alan WiFi Konumlandırma Yöntemi

Year 2024, Volume: 19 Issue: 1, 133 - 145, 28.03.2024

Turan Göktuğ Altundoğan Mehmet Karaköse

https://doi.org/10.55525/tjst.1254099

Abstract

Kablosuz iletişim teknolojilerinin ve IoT uygulamalarının yaygınlaşmasıyla birlikte araştırmacılar, WiFi sinyallerini iç mekân konum tespiti için kullanan yaklaşımlar geliştirmektedir. Bu çalışmada WiFi sinyal gücü (RSSI) değerlerine dayalı erişim noktalarının ağırlıklı görünürlük matrisleri oluşturularak sezgisel optimizasyon tabanlı yöntemlere dayalı iç mekan konumlandırma işlemi gerçekleştirilmiştir. Önerilen yöntemde, PSO ve GA yaklaşımları, görünürlük ağırlık matrislerine dayalı ortak bir uygunluk fonksiyonu kullanarak mobil kullanıcının konumunu belirler. Önerilen yöntem, RSSI aralıklarına dayalı konum aralıklarının belirlendiği sanal bir senaryo üzerinde test edilmiştir. Her iki sezgisel optimizasyon yöntemi farklı kriterlere göre karşılaştırılmış ve GA tabanlı yöntem için maksimum 3m, PSO tabanlı yöntem için maksimum 1,5m hata ile konumlandırma işlemi gerçekleştirilmiştir.

Keywords

WiFi, Kapalı Alan Konumlandırma, Görünürlük Grafları, PSO, GA

Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization

Abstract

With the widespread use of wireless communication technologies and IoT applications, researchers are developing approaches that utilize WiFi signals for indoor location determination. In this study, indoor positioning process based on heuristic optimization-based methods was performed by creating weighted visibility matrices of access points based on WiFi signal strength (RSSI) values. In the proposed method, the PSO and GA approaches determine the position of the mobile user using a common fitness function based on the visibility weight matrices. The proposed method has been tested on a virtual scenario where position ranges based on RSSI ranges are determined. Both heuristic optimization methods are compared according to different criteria and the positioning process is performed with a maximum error of 3m for the GA based method and a maximum of 1.5m for the PSO based method.

Keywords

WiFi Indoor Positioning, Visibility Graphs, PSO, GA

References

• Yan S, Luo H, Zhao F, Shao W, Li Z and Crivello A, "Wi-Fi RTT based indoor positioning with dynamic weighted multidimensional scaling," 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2019, pp. 1-8, doi: 10.1109/IPIN.2019.8911783.
• Yuan D et al., "Model Checking Indoor Positioning System With Triangulation Positioning Technology," 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 2018, pp. 862-866, doi: 10.1109/ITME.2018.00193.
• Maung NAM, and Zaw W, "Comparative Study of RSS-based Indoor Positioning Techniques on Two Different Wi-Fi Frequency Bands," 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020, pp. 185-188, doi: 10.1109/ECTI-CON49241.2020.9158211.
• Jaworski W, Wilk P, Juszczak M, Wysoczańska M and Lee AY, "Towards automatic configuration of floorplans for Indoor Positioning System," 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2019, pp. 1-7, doi: 10.1109/IPIN.2019.8911747.
• Joseph R and Sasi SB, "Indoor Positioning Using WiFi Fingerprint," 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), 2018, pp. 1-3, doi: 10.1109/ICCSDET.2018.8821184.
• Li Z and Huang J, "Study on the use of Q-R codes as landmarks for indoor positioning: Preliminary results," 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), 2018, pp. 1270-1276, doi: 10.1109/PLANS.2018.8373516.
• Blazek J, Jiranek J and Bajer J, "Indoor Passive Positioning Technique using Ultra Wide Band Modules," 2019 International Conference on Military Technologies (ICMT), 2019, pp. 1-5, doi: 10.1109/MILTECHS.2019.8870099.
• Perakis H and Gikas V, "Evaluation of Range Error Calibration Models for Indoor UWB Positioning Applications," 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018, pp. 206-212, doi: 10.1109/IPIN.2018.8533755.
• Molina B, Olivares E, Palau CE and Esteve M, "A Multimodal Fingerprint-Based Indoor Positioning System for Airports," in IEEE Access, vol. 6, pp. 10092-10106, 2018, doi: 10.1109/ACCESS.2018.2798918.
• Andrushchak V, Maksymyuk T, Klymash M and Ageyev D, "Development of the iBeacon’s Positioning Algorithm for Indoor Scenarios," 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), 2018, pp. 741-744, doi: 10.1109/INFOCOMMST.2018.8632075.
• Teoman E and Ovatman T, "Trilateration in Indoor Positioning with an Uncertain Reference Point," 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), 2019, pp. 397-402, doi: 10.1109/ICNSC.2019.8743240.
• Shao W, Luo H, Zhao F, Tian H, Yan S and Crivello A, "Accurate Indoor Positioning Using Temporal–Spatial Constraints Based on Wi-Fi Fine Time Measurements," in IEEE Internet of Things Journal, vol. 7, no. 11, pp. 11006-11019, Nov. 2020, doi: 10.1109/JIOT.2020.2992069.
• Pakanon N, Chamchoy M and Supanakoon P, "Study on Accuracy of Trilateration Method for Indoor Positioning with BLE Beacons," 2020 6th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2020, pp. 1-4, doi: 10.1109/ICEAST50382.2020.9165464.
• Ang JLF, Lee WK and Ooi BY, "GreyZone: A Novel Method for Measuring and Comparing Various Indoor Positioning Systems," 2019 International Conference on Green and Human Information Technology (ICGHIT), 2019, pp. 30-35, doi: 10.1109/ICGHIT.2019.00014.
• Cai L, Deng B, Wei X, Wang R and Wang J, "Analysis of Spontaneous EEG Activity in Alzheimer’s Disease Using Weighted Visibility Graph," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp. 3100-3103, doi: 10.1109/EMBC.2018.8513010.
• Samanta K, Chatterjee S and Bose R, "Cross-Subject Motor Imagery Tasks EEG Signal Classification Employing Multiplex Weighted Visibility Graph and Deep Feature Extraction," in IEEE Sensors Letters, vol. 4, no. 1, pp. 1-4, Jan. 2020, Art no. 7000104, doi: 10.1109/LSENS.2019.2960279
• Roy SS, Chatterjee S, Barman R, Roy S and Dey S, "Bearing Fault Detection in Induction Motors Employing Difference Visibility Graph," 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020, pp. 1-4, doi: 10.1109/PEDES49360.2020.9379635.
• León C, Carrault G, Pladys P and Beuchée A, "Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 4, pp. 1006-1017, April 2021, doi: 10.1109/JBHI.2020.3021662.
• Kulbiej E, "Autonomous Vessels' Pathfinding Using Visibility Graph," 2018 Baltic Geodetic Congress (BGC Geomatics), 2018, pp. 107-111, doi: 10.1109/BGC-Geomatics.2018.00026.
• Songtao L, Zhenshuo L, Yang G and Zhenming W, "Automatic radar antenna scan type recognition based onlimited penetrable visibility graph," in Journal of Systems Engineering and Electronics, vol. 32, no. 2, pp. 437-446, April 2021, doi: 10.23919/JSEE.2021.000037.
• Wan T, Fu X, Jiang K, Zhao Y and Tang B, "Radar Antenna Scan Pattern Intelligent Recognition Using Visibility Graph," in IEEE Access, vol. 7, pp. 175628-175641, 2019, doi: 10.1109/ACCESS.2019.2957769.
• Tao W, J. Kaili, Jingyi L, Yanli T and Bin T, "Detection and recognition of LPI radar signals using visibility graphs," in Journal of Systems Engineering and Electronics, vol. 31, no. 6, pp. 1186-1192, Dec. 2020, doi: 10.23919/JSEE.2020.000091.
• Shang F, Su W, Wang Q, Gao, H and Fu Q. “A location estimation algorithm based on RSSI vector similarity degree” International Journal of Distributed Sensor Networks, 10(8), 2014, 371350.