Relative Localization of Wireless Sensor Nodes by Using the RSSI and ToA based distance estimations

Öz

Wireless sensor network is a popular area for both academic research and commercial applications. A wireless sensor network is made up of several sensor nodes connected over various wireless infrastructures such as Wi-Fi, Bluetooth, ZigBee, or WiMAX. Determining the relative or absolute position of the sensor nodes is essential information for many applications. In this work, we present a novel method for estimating the position of sensor nodes using the Received Signal Strength Indicator and Time of Arrival metrics of the preferred wireless communication infrastructure. Localization results based on both of the metrics and comparison of them with respect to distance between the nodes are presented, and a novel combined method using both the RSSI and ToA based distance estimations is presented. The proposed method estimates the position of the WSN using both methods, but the result of a single method is preferred depending on the distance between the nodes since within the first 5 m. The RSSI based method is superior to ToA and for farther distances ToA outperforms RSSI. The measurement results show that the combined method reduces the estimation error and performs better than both methods it is based on.

Anahtar Kelimeler

• RSSI
• TOA
• Localization
• Wireless Sensor Networks

RSSI ve ToA Tabanlı Mesafe Tahminlerini Kullanarak Kablosuz Sensör Düğümlerinin Bağıl Lokalizasyonu

Öz

Kablosuz sensör ağları, Wi-Fi, Bluetooth, ZigBee ve WiMAX gibi kablosuz altyapıları kullanarak bağlanan birçok sensör düğümünden oluşur. Sensör düğümlerinin göreli veya mutlak konumlarının belirlenmesi birçok uygulama için önem taşımaktadır. Sunulan çalışmamızda, tercih edilen kablosuz iletişim altyapısının Alınan Sinyal Gücü Göstergesi (RSSI) ve Varış Zamanı (ToA) metriklerinden elde edilen sonuçları birleştirerek sensör nodlarının konumunu daha yüksek başarımlı tahmin etmek için geliştirilmiş bir yöntem sunulmaktadır. Konum analizinin sonuçları ölçümlere ve nodlar arasındaki mesafelerin karşılaştırmasına dayanarak sunulmaktadır. Önerilen yöntem RSSI ya da ToA verilerine dayalı kestirim sonuçlarını düğümler arasındaki mesafeye göre tercih etmektedir, kısa mesafelerde RSSI uzak mesafelerde ise ToA tercih edilmektedir. Sonuçlar, bileşik yöntemin tahmin hatalarını azalttığını ve her iki yöntemden de daha iyi performans sergilediğini göstermektedir.

Anahtar Kelimeler

• Kablosuz Sensör Ağları
• Konumlama
• RSSI
• TOA

Kaynakça

1. [1] I. F. Akyildiz, T. Melodia, and K. R. Chowdury, “Wireless multimedia sensor networks: A survey,” Wirel. Commun. IEEE, 2007, doi: 10.1109/MWC.2007.4407225.
2. [2] S. Scheer, R. Mendes Jr., T. F. Campestrini, and M. C. Garrido, “Fundamental Test of Seismic Information and Building Damage Data Gathering System using OSHW with Wireless Sensor Network,” Sixth Annu. Int. Conf. Comput. Civ. Build. Eng., 2014, doi: 10.1061/9780784413616.053.
3. [3] A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, “Wireless sensor networks for habitat monitoring,” in Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications - WSNA ’02, 2002, doi: 10.1145/570738.570751.
4. [4] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Comput. Networks, 2008, doi: 10.1016/j.comnet.2008.04.002.
5. [5] G. Han, J. Jiang, C. Zhang, T. Q. Duong, M. Guizani, and G. K. Karagiannidis, “A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks,” IEEE Communications Surveys and Tutorials. 2016, doi: 10.1109/COMST.2016.2544751.
6. [6] A. PAL, “Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges,” Netw. Protoc. Algorithms, 2010, doi: 10.5296/npa.v2i1.279.
7. [7] T. Türkoral, Ö. Tamer, S. Yetiş, E. İnanç, L. Çetin, "Short Range Indoor Distance Estimation by Using RSSI Metric" . IU-Journal of Electrical & Electronics Engineering Vol 17 No:114 3295-3302.
8. [8] B. Neuwinger, U. Witkowski, and U. Rückert, “Ad-hoc communication and localization system for mobile robots,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, doi: 10.1007/978-3-642-03983-6_26.

9. [9] B. Yang, L. Guo, R. Guo, M. Zhao, and T. Zhao, “A Novel Trilateration Algorithm for RSSI-Based Indoor Localization,” IEEE Sens. J., vol. 20, no. 14, pp. 8164–8172, 2020, doi: 10.1109/JSEN.2020.2980966.
10. [10] Y. Yu, L. Yang, and H. Li, “An adaptive model recognition and construction method for RSSI fingerprint-based localization,” Meas. Sci. Technol., vol. 30, no. 12, 2019, doi: 10.1088/1361-6501/ab285f.
11. [11] D. Zou et al., “Design of a Practical WSN Based Fingerprint Localization System,” Mob. Networks Appl., 2019, doi: 10.1007/s11036-019-01298-4.
12. [12] M. F. Mosleh, F. A. Abed, Z. A. Hamza, Improving Indoor Localization System Using a Partitioning Technique Based on RSS and ToA. Journal of Techniques, vol. 3, no. 1, pp. 47–54, 2021 https://doi.org/10.51173/jt.v3i1.278
13. [13] A. Ahmed, A. Talpur, N. Bohra, and S. Khan, “Performance Analysis of RSSI based Localization in WSNs,” 1st Int. Conf. Comput. Sci. Technol. 10-12 April 2019 (INCCST’19), MUET Jamshoro\, no. April, pp. 10–12, 2019.
14. [14] J. Luomala and I. Hakala, “Adaptive range-based localization algorithm based on trilateration and reference node selection for outdoor wireless sensor networks,” Comput. Networks, vol. 210, p. 108865, Jun. 2022, doi: 10.1016/J.COMNET.2022.108865.
15. [15] P. K. Sahu, E. H. Wu, J. & Sahoo, . DuRT: Dual RSSI trend based localization for wireless sensor networks. IEEE Sensors Journal, 2013, Vol 13, no:8, 3115-3123. [6502185]. https://doi.org/10.1109/JSEN.2013.2257731
16. [16] S. Mitilineos, D. M. Kyriazanos, O. E. Segou, J. N. Goufas, S. Thomopoulos, "Indoor Localisation with Wireless Sensor Networks," Progress In Electromagnetics Research, Vol. 109, 441-474, 2010. doi:10.2528/PIER10062801
17. [17] M. Garcia, J. Tomas, F. Boronat, and J. Lloret, “The development of two systems for indoor wireless sensors self-location,” Ad-Hoc Sens. Wirel. Networks, 2009.
18. [18] X. Zhai, J. Yang, and L. Cui, “Wireless network localization via alternating projections with TDOA and FDOA measurements,” Ad-Hoc Sens. Wirel. Networks, 2017.
19. [19] C. Liu, S. Liu, W. Zhang, and D. Zhao, “The Performance Evaluation of Hybrid Localization Algorithm in Wireless Sensor Networks,” Mob. Networks Appl., vol. 21, no. 6, pp. 994–1001, 2016, doi: 10.1007/s11036-016-0737-1.
20. [20] M. Karmy, S. Elsayed, and A. Zekry, “Performance enhancement of an indoor localization system based on visible light communication using rssi/tdoa hybrid technique,” J. Commun., vol. 15, no. 5, pp. 379–389, 2020, doi: 10.12720/jcm.15.5.379-389.
21. [21] P. Tripathy and P. M. Khilar, “An ensemble approach for improving localization accuracy in wireless sensor network,” Comput. Networks, p. 109427, Oct. 2022, doi: 10.1016/J.COMNET.2022.109427.
22. [22] L. Zhang, Z. Wang, Z. Kuang, and H. Yang, “Three-dimensional localization algorithm for WSN nodes based on RSSI-TOA and LSSVR method,” Proc. - 2019 11th Int. Conf. Meas. Technol. Mechatronics Autom. ICMTMA 2019, pp. 498–503, Apr. 2019, doi: 10.1109/ICMTMA.2019.00116.
23. [23] M. Zaidi et al., “Cooperative Scheme ToA-RSSI and Variable Anchor Positions for Sensors Localization in 2D Environments,” 2022, doi: 10.1155/2022/5069254.
24. [24] F. B. Gunay and T. Cavdar, “Kablosuz duyarga aǧlarda RSSI, toa ve tdoa yardimiyla gezgin filo lokalizasyon modeli,” 2014 22nd Signal Process. Commun. Appl. Conf. SIU 2014 - Proc., no. April, pp. 1431–1434, 2014, doi: 10.1109/SIU.2014.6830508.
25. [25] J. Bardwell, “Converting Signal Strength Percentage to dBm Values,” WildPackets, Inc, no. November, pp. 1–12, 2002, doi: 20021217-M-WP007.
26. [26] T. Türkoral, Ö. Tamer, S. Yetiş, E. İnanç, and L. Çetin, “Relative Localization of Wireless Sensor Nodes by Using the RSSI Data,” Netw. Protoc. Algorithms, vol. 10, no. 1, p. 1, 2018, doi: 10.5296/npa.v10i1.11533.
27. [27] C. Sommer and F. Dressler, “Using the Right Two-Ray Model? A Measurement based Evaluation of PHY Models in VANETs,” Proc. ACM MobiCom, 2011.
28. [28] M. Rezar and F. Ricciato, “On the impact of time-of-departure knowledge on the accuracy of time-of-arrival localization,” Comput. Networks, vol. 176, p. 107285, Jul. 2020, doi: 10.1016/J.COMNET.2020.107285.
29. [29] WG802.11 - Wireless LAN Working Group, “IEEE 802.11v-2011 - IEEE Standard for Information technology-- Local and metropolitan area networks-- Specific requirements-- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications Amendment 8: IEEE 802.11 Wireless Netwo.” 2011.
30. [30] A. Bahr and J. Leonard, “Minimizing Trilateration Errors in the Presence of Uncertain Landmark Positions.,” in Proc. 3rd European Conference on Mobile Robots (ECMR), 2007.
31. [31] J. Cheng, J. Cheng, M. Zhou, F. Liu, S. Gao, and C. Liu, “Routing in internet of vehicles: A review,” IEEE Trans. Intell. Transp. Syst., 2015, doi: 10.1109/TITS.2015.2423667.
32. [32] M. S. Varela and M. G. Sánchez, “RMS delay and coherence bandwidth measurements in indoor radio channels in the UHF band,” IEEE Trans. Veh. Technol., 2001, doi: 10.1109/25.923063.
33. [33] L. Schauer, F. Dorfmeister, and M. Maier, “Potentials and limitations of WIFI-positioning using time-of-flight,” in 2013 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013, 2013, doi: 10.1109/IPIN.2013.6817861.
34. [34] I. Casacuberta and A. Ramirez, “Time-of-flight positioning using the existing wireless local area network infrastructure,” in 2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 - Conference Proceedings, 2012, doi: 10.1109/IPIN.2012.6418938.
35. [35] “RTL8723BU - REALTEK.” [Online]. Available: https://www.realtek.com/en /products/communications-network-ics/item/rtl8723bu. [Accessed: 19-Jan-2023].
36. [36] Y. Sun et al., “Computationally Attractive and Location Robust Estimator for IoT Device Positioning,” IEEE Internet Things J., vol. 9, no. 13, pp. 10891–10907, Jul. 2022, doi: 10.1109/JIOT.2021.3127690.