Research Article
RSSI Sinyalleri Kullanarak İç Ortamda Parmak İzi Tabanlı YSA ile Konum Tespitinin Gerçekleştirilmesi
Abstract
Açık alanlarda konum tespiti doğru bir şekilde GPS(Global Positioning System) sistemleri vasıtasıyla elde edilebilmektedir. Ancak GPS sistemleri kapalı ortamlarda konum bilgisini hassas bir şekilde ölçememektedir. İç ortamlarda konum tespiti için özel ağ sistemleri tasarlanmaktadır. Bu makalede, kapalı bir ortamda konumlandırılmış sensörler yardımıyla ortamın parmak izi RSSI sinyalleri yardımıyla çıkartılmış ve sensör düğüm konumları YSA kullanılarak tespit edilmiştir. Gerçekleştirilen uygulamada 2 senaryo kullanılmıştır. İlk senaryo boş bir ofis ortamında, ikinci senaryo ise insanların ve çeşitli nesnelerin olduğu bir ofis ortamında gerçekleştirilmiştir. Sensör düğümlerin gerçek ve tahmini konumları ölçüldüğünde hesaplanan hatanın literatüre göre kabul edilebilir olduğu görülüştür. İlk senaryo için konum tespiti ortalama 18,2 cm hata ile ikinci senaryoda ortalama 24,2 cm hata ile tespit edilmiştir. Önerilen algoritma ve uygulama doğruluk ve güvenilirlik açısından mevcut tekniklerle paralel sonuçlar üretmektedir.
Supporting Institution
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu ( TUBITAK)
Project Number
208E070
Thanks
Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu ( TUBITAK) tarafından 208E070 numaralı proje ile desteklenmiştir.
References
- 1. G. Félix, M. Siller E. Álvarez “A fingerprinting indoor localization algorithm based deep learning”, 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp.1006-1011, 2016
- 2. Z. Liu, B. Dai, X. Wan, X. Li, “Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network”, Sensors, 19, 4597; doi:10.3390/s19204597, 2019.
- 3. R. Wang, Z. Li, H. Luo, F. Zhao, W. Shao, Q. Wang, “A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron”, Remote Sens. 11, 1293; doi:10.3390/rs11111293, 2019.
- 4. Y. Zhang, L. Lu, Y. Wang, C. Chen, “WLAN indoor localization method using angle estimation“, AEU - International Journal of Electronics and Communications, Vol.76, pp:11-17,2017.
- 5. G. Deak, K. Curran, J. Condell, “A survey of active and passive indoor localization systems, Computer Communications, Vol.35, Issue 16, pp:1939-1954, 2012.
- 6. E. Erdem, T. Tuncer, R. Doğan, “Location Determination of a Mobile Device with a Fingerprint Algorithm using a Cascade ANN model”, Vol.12, Issue 1, pp:238 – 249,2018.
- 7. I.T. Haque, “A sensor based indoor localization through fingerprinting”, Journal of Network and Computer Applications, Vol.44, pp:220-229,2014.
- 8. Z. Wu, E. Jedari, R. Muscedere, R. Rashidzadeh, “Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization”, Computer Communications, Vol.83, pp:64-71, 2016.
- 9. M. Oussalah, M. Alakhras., “Multivariable fuzzy inference system for fingerprinting indoor localization, Fuzzy Sets and Systems, Vol.269, pp: 65-89, 2015.
- 10. A. Booranawong, K. Sengchuai, N. Jindapetch, “Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy”, Measurement, Vol.133, pp:370-382, 2019.
- 11. X. Fang, L. Nan, Z. Jiang, L.Chen, “Noise-aware fingerprint localization algorithm for wireless sensor network based on adaptive fingerprint Kalman filter”, Computer Networks: The International Journal of Computer and Telecommunications Networking, Vol. 124, No. C, 2017.
- 12. L. Kanaris, A. Kokkinis, G. Fortino, A. Liotta, S. Stavrou, “Sample Size Determination Algorithm for fingerprint-based indoor localization systems”, Computer Networks, Vol.101, pp:169-177, 2016.
- 13. N. V. T.Ngo, J. G. Kim, “Sequential learning for fingerprint based indoor localization“,AEU - International Journal of Electronics and Communications, Vol.71, pp:105-109, 2017.
- 14. S. K. Rajesh, M. Hegde, N. Trigoni, “Gaussian Process Regression for Fingerprinting based Localization”, Ad Hoc Networks, 51, 2016. 15. A. Saber, K. Fekher, B. Abbas, R. Abderrezak, L.K. Med, A. Mohamed, “A new fuzzy logic based node localization mechanism for wireless sensor networks”, Future Gener. Comput. Syst. pp:1–15. 2017.
- 16. S. Tuncer, T. Tuncer, “Indoor localization with bluetooth technology using artificial neural networks”, IEEE International Conference on Intelligent Engineering Systems, Bratislava, Slovakia, pp. 213–217, 2015.
Year 2020,
, 925 - 931, 30.09.2020
Abstract
Project Number
208E070
References
- 1. G. Félix, M. Siller E. Álvarez “A fingerprinting indoor localization algorithm based deep learning”, 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp.1006-1011, 2016
- 2. Z. Liu, B. Dai, X. Wan, X. Li, “Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network”, Sensors, 19, 4597; doi:10.3390/s19204597, 2019.
- 3. R. Wang, Z. Li, H. Luo, F. Zhao, W. Shao, Q. Wang, “A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron”, Remote Sens. 11, 1293; doi:10.3390/rs11111293, 2019.
- 4. Y. Zhang, L. Lu, Y. Wang, C. Chen, “WLAN indoor localization method using angle estimation“, AEU - International Journal of Electronics and Communications, Vol.76, pp:11-17,2017.
- 5. G. Deak, K. Curran, J. Condell, “A survey of active and passive indoor localization systems, Computer Communications, Vol.35, Issue 16, pp:1939-1954, 2012.
- 6. E. Erdem, T. Tuncer, R. Doğan, “Location Determination of a Mobile Device with a Fingerprint Algorithm using a Cascade ANN model”, Vol.12, Issue 1, pp:238 – 249,2018.
- 7. I.T. Haque, “A sensor based indoor localization through fingerprinting”, Journal of Network and Computer Applications, Vol.44, pp:220-229,2014.
- 8. Z. Wu, E. Jedari, R. Muscedere, R. Rashidzadeh, “Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization”, Computer Communications, Vol.83, pp:64-71, 2016.
- 9. M. Oussalah, M. Alakhras., “Multivariable fuzzy inference system for fingerprinting indoor localization, Fuzzy Sets and Systems, Vol.269, pp: 65-89, 2015.
- 10. A. Booranawong, K. Sengchuai, N. Jindapetch, “Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy”, Measurement, Vol.133, pp:370-382, 2019.
- 11. X. Fang, L. Nan, Z. Jiang, L.Chen, “Noise-aware fingerprint localization algorithm for wireless sensor network based on adaptive fingerprint Kalman filter”, Computer Networks: The International Journal of Computer and Telecommunications Networking, Vol. 124, No. C, 2017.
- 12. L. Kanaris, A. Kokkinis, G. Fortino, A. Liotta, S. Stavrou, “Sample Size Determination Algorithm for fingerprint-based indoor localization systems”, Computer Networks, Vol.101, pp:169-177, 2016.
- 13. N. V. T.Ngo, J. G. Kim, “Sequential learning for fingerprint based indoor localization“,AEU - International Journal of Electronics and Communications, Vol.71, pp:105-109, 2017.
- 14. S. K. Rajesh, M. Hegde, N. Trigoni, “Gaussian Process Regression for Fingerprinting based Localization”, Ad Hoc Networks, 51, 2016. 15. A. Saber, K. Fekher, B. Abbas, R. Abderrezak, L.K. Med, A. Mohamed, “A new fuzzy logic based node localization mechanism for wireless sensor networks”, Future Gener. Comput. Syst. pp:1–15. 2017.
- 16. S. Tuncer, T. Tuncer, “Indoor localization with bluetooth technology using artificial neural networks”, IEEE International Conference on Intelligent Engineering Systems, Bratislava, Slovakia, pp. 213–217, 2015.
There are 15 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Project Number | 208E070 |
| Publication Date | September 30, 2020 |
| Submission Date | February 21, 2020 |
| Published in Issue | Year 2020 |
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