Resumen
Estimar la distancia a partir de la intensidad de la señal RSSI tiene varias complicaciones, especialmente al usar dispositivos no especializados o de consumidor. La señal es muy inestable y presenta importantes niveles de ruido; es fuertemente afectada por las condiciones del entorno y de los mismos dispositivos. En este artículo, en primer lugar se caracterizan experimentalmente diferentes condiciones de este ruido, luego se analizan distintas parametrizaciones del filtro de media móvil simple, para estabilizar y suavizar la señal; finalmente, se calcula mediante el Óptimo de Pareto, el valor de ventana que hace las veces de punto de equilibro para todos los datasets analizados. Los resultados experimentales muestran que el filtro de media móvil tiene notables beneficios al estimar la distancia a partir de la señal RSSI.Referencias
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[19] S. Nagaraju, L. J. Gudino, B. V. Kadam, R. Ookalkar, y S. Udeshi, “RSSI based indoor localization with interference avoidance for Wireless Sensor Networks using anchor node with sector antennas”, en 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016, pp. 2233–2237.
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[26] M. T. Ribeiro, S. Singh, y C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier”, en Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2016, pp. 1135–1144.
[27] D. V. Balandin y M. M. Kogan, “Pareto suboptimal solutions in control and filtering problems under multiple deterministic and stochastic disturbances”, en 2016 European Control Conference (ECC), 2016, pp. 2263–2268.
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[29] P. Roocks, “Computing pareto frontiers and database preferences with the rPref Package”, RJ, vol. 8, núm. 2, pp. 393–404, 2016.
[30] A. Singh y R. Mehra, “Adaptive RLS-Received Signal Strength Algorithm in Wireless Network Area for Multi-Mobile Nodes Location Estimation System”, Int. J. Comput. Appl., vol. 64, núm. 15, pp. 12–15, feb. 2013.
[31] R. Mehra y A. Singh, “Real time RSSI error reduction in distance estimation using RLS algorithm”, en 2013 3rd IEEE International Advance Computing Conference (IACC), 2013, pp. 661–665.
[32] H. Aziz, J. Lang, y J. Monnot, “Computing Pareto Optimal Committees”, en Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, United States, 2016, pp. 60–66.
[2] S. Hara et al., “Propagation characteristics of IEEE 802.15. 4 radio signal and their application for location estimation”, en Vehicular Technology Conference, 2005. VTC 2005-Spring. 2005 IEEE 61st, 2005, vol. 1, pp. 97–101.
[3] R. Piyare, “Internet of things: ubiquitous home control and monitoring system using android based smart phone”, Int. J. Internet Things, vol. 2, núm. 1, pp. 5–11, 2013.
[4] “IEEE Standard for Information technology–Telecommunications and information exchange between systems Local and metropolitan area networks–Specific requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications”, IEEE Std 80211-2012 Revis. IEEE Std 80211-2007, pp. 1–2793, mar. 2012.
[5] R.-H. Wu, Y.-H. Lee, H.-W. Tseng, Y.-G. Jan, y M.-H. Chuang, “Study of characteristics of RSSI signal”, en Industrial Technology, 2008. ICIT 2008. IEEE International Conference on, 2008, pp. 1–3.
[6] P. Bellavista, A. Corradi, y C. Giannelli, “Evaluating filtering strategies for decentralized handover prediction in the wireless internet”, en Computers and Communications, 2006. ISCC’06. Proceedings. 11th IEEE Symposium on, 2006, pp. 167–174.
[7] J. Heo y Y. Kwon, “Improved Indoor Positioning System Using BLE Beacons and a Compensated Gyroscope Sensor”, en Intelligent Robotics and Applications, 2017, pp. 69–76.
[8] E. Cabrera-Goyes y D. Ordóñez-Camacho, “Towards a Bluetooth Indoor Positioning System with Android Consumer Devices”, en 2017 International Conference on Information Systems and Computer Science (INCISCOS), 2017, pp. 56–59.
[9] E. Cabrera-Goyes y D. Ordóñez-Camacho, “Posicionamiento en espacios interiores con Android, Bluetooth y RSSI”, Enfoque UTE, vol. 9, núm. 1, pp. 118–126, mar. 2018.
[10] D. Ordóñez-Camacho y E. Cabrera-Goyes, “An Adaptive-Bounds Band-Pass Moving-Average Filter to Increase Precision on Distance Estimation from Bluetooth RSSI”, en Proceedings of the International Conference on Information Technology & Systems (ICITS 2018), 2018, pp. 823–832.
[11] S. Onofre, B. Caseiro, J. P. Pimentão, y P. Sousa, “Using Fuzzy Logic to Improve BLE Indoor Positioning System”, en Technological Innovation for Cyber-Physical Systems, 2016, pp. 169–177.
[12] N. Kuxdorf-Alkirata, T. Werthwein, A. Heinemann, y D. Brückmann, “A self-calibrating bidirectional indoor localization system”, en 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 3276–3280.
[13] M. G. Jadidi, M. Patel, y J. V. Miro, “Gaussian processes online observation classification for RSSI-based low-cost indoor positioning systems”, en 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 6269–6275.
[14] Z. Yu y G. Guo, “Improvement of Positioning Technology Based on RSSI in ZigBee Networks”, Wirel. Pers. Commun., vol. 95, núm. 3, pp. 1943–1962, ago. 2017.
[15] M. Aykaç, E. Erçelebi, y N. B. Aldin, “ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation”, Analog Integr. Circuits Signal Process., vol. 92, núm. 2, pp. 263–279, ago. 2017.
[16] K. H. Lin, C. C. Lu, H. M. Chen, H. F. Li, y C. F. Chuang, “A modified least squares iteration for indoor positioning system”, en 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2017, pp. 109–110.
[17] Q. Luo et al., “DEDF: lightweight WSN distance estimation using RSSI data distribution-based fingerprinting”, Neural Comput. Appl., vol. 27, núm. 6, pp. 1567–1575, ago. 2016.
[18] W. Xue, W. Qiu, X. Hua, y K. Yu, “Improved Wi-Fi RSSI Measurement for Indoor Localization”, IEEE Sens. J., vol. 17, núm. 7, pp. 2224–2230, abr. 2017.
[19] S. Nagaraju, L. J. Gudino, B. V. Kadam, R. Ookalkar, y S. Udeshi, “RSSI based indoor localization with interference avoidance for Wireless Sensor Networks using anchor node with sector antennas”, en 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016, pp. 2233–2237.
[20] J. A. Jayakody, S. Lokuliyana, D. Chathurangi, y D. Vithana, “Indoor positioning: Novel approach for Bluetooth networks using RSSI smoothing”, Int J. Comput. Appl., vol. 137, núm. 13, 2016.
[21] Z. Tóth, “ILONA: indoor localization and navigation system”, J. Locat. Based Serv., mar. 2016.
[22] T. Rahlf, Data Visualisation with R. New York: Springer International Publishing, 2017.
[23] T. Chai y R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature”, Geosci Model Dev, vol. 7, núm. 3, pp. 1247–1250, jun. 2014.
[24] G. Brassington, “Mean absolute error and root mean square error: which is the better metric for assessing model performance?”, presentado en EGU General Assembly Conference Abstracts, 2017, vol. 19, p. 3574.
[25] P. Refaeilzadeh, L. Tang, y H. Liu, “Cross-Validation”, en Encyclopedia of Database Systems, Springer, New York, NY, 2016, pp. 1–7.
[26] M. T. Ribeiro, S. Singh, y C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier”, en Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2016, pp. 1135–1144.
[27] D. V. Balandin y M. M. Kogan, “Pareto suboptimal solutions in control and filtering problems under multiple deterministic and stochastic disturbances”, en 2016 European Control Conference (ECC), 2016, pp. 2263–2268.
[28] L. Tock y F. Maréchal, “Decision support for ranking Pareto optimal process designs under uncertain market conditions”, Comput. Chem. Eng., vol. 83, pp. 165–175, dic. 2015.
[29] P. Roocks, “Computing pareto frontiers and database preferences with the rPref Package”, RJ, vol. 8, núm. 2, pp. 393–404, 2016.
[30] A. Singh y R. Mehra, “Adaptive RLS-Received Signal Strength Algorithm in Wireless Network Area for Multi-Mobile Nodes Location Estimation System”, Int. J. Comput. Appl., vol. 64, núm. 15, pp. 12–15, feb. 2013.
[31] R. Mehra y A. Singh, “Real time RSSI error reduction in distance estimation using RLS algorithm”, en 2013 3rd IEEE International Advance Computing Conference (IACC), 2013, pp. 661–665.
[32] H. Aziz, J. Lang, y J. Monnot, “Computing Pareto Optimal Committees”, en Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, United States, 2016, pp. 60–66.
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