Indoor WLAN localization via adaptive Lasso Bayesian inference and convex optimization

References

• Abdullah, O. (2018, August). Convex optimization via symmetrical hölder divergence for a WLAN indoor positioning system. Entropy, 20(9), 639. https://doi.org/10.3390/e20090639
   Web of Science ®Google Scholar
• Abdullah, O., & Abdel-Qader, I. (2016, May). A PNN- Jensen-Bregman divergence symmetrization for a WLAN indoor positioning system. 2016 IEEE international conference on electro information technology (EIT), Grand Forks, North Dakota, USA.
   Google Scholar
• Abdullah, O., Abdel-Qader, I., & Bazuin, B. (2016a, October). Fingerprint-based technique for indoor positioning system via machine learning and convex optimization. 2016 IEEE 7th annual ubiquitous computing, electronics & mobile communication conference (UEMCON), New York City, USA.
   Google Scholar
• Abdullah, O., Abdel-Qader, I., & Bazuin, B. (2016b, October). K-means-Jensen-Shannon divergence for a WLAN indoor positioning system. 2016 IEEE 7th annual ubiquitous computing, electronics & mobile communication conference (UEMCON), New York City, USA.
   Google Scholar
• Abdullah, O. A., & Abdel-Qader, I. (2018, September). Machine learning algorithm for wireless indoor localization. Machine Learning - Advanced Techniques and Emerging Applications. DOI: 10.5772/intechopen.74754
   Google Scholar
• Abdullah, O. A., Abdel-Qader, I., & Bazuin, B. (2017, January). Convex optimization via Jensen-Bregman divergence for WLAN indoor positioning system. International Journal of Handheld Computing Research, 8(1), 29–16. https://doi.org/10.4018/IJHCR.2017010103
   Google Scholar
• Alhamzawi, R., & Ali, H. T. M. (2018, September). The Bayesian adaptive lasso regression. Mathematical Biosciences, 303, 75–82. https://doi.org/10.1016/j.mbs.2018.06.004
   PubMed Web of Science ®Google Scholar
• Astaneh, A. A., & Gheisari, S. (2018, September). Review and comparison of routing metrics in cognitive radio networks. Emerging Science Journal, 2(4). https://doi.org/10.28991/esj-2018-01143
   Google Scholar
• Chai, X., & Yang, Q. (2007, June). Reducing the calibration effort for probabilistic indoor location estimation. IEEE Transactions on Mobile Computing, 6(6), 649–662. https://doi.org/10.1109/TMC.2007.1025
   Web of Science ®Google Scholar
• Čokl, A., Žunič-Kosi, A., & Laumann, R. A. (2019, December). Stink Bug communication with multimodal signals transmitted through air and substrate. Emerging Science Journal, 3(6), 407–424. https://doi.org/10.28991/esj-2019-01203
   Google Scholar
• Feng, C., Au, W. S. A., Valaee, S., & Tan, Z. (2009, December). Orientation-aware indoor localization using affinity propagation and compressive sensing. 2009 3rd IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), Radisson Aruba Resort, Casino & SpaAruba, Dutch Antilles.
   Google Scholar
• Feng, C., Au, W. S. A., Valaee, S., & Tan, Z. (2012, December). Received-signal-strength-based indoor positioning using compressive sensing. IEEE Transactions on Mobile Computing, 11(12), 1983–1993. https://doi.org/10.1109/TMC.2011.216
   Web of Science ®Google Scholar
• Han, S., Zhao, C., Meng, W., & Li, C. (2015, June). Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity. 2015 IEEE international conference on communications (ICC), London.
   Google Scholar
• Ji, Y., Biaz, S., Pandey, S., & Agrawal, P. (2006). ARIADNE. Proceedings of the 4th international conference on mobile systems, applications and services - MobiSys 2006, Uppsala, Sweden.
   Google Scholar
• Jin, J., Gu, Y., & Mei, S. (2010, March). An introduction to compressive sampling and its applications. Journal of Electronics Information & Technology, 32 (2), 470–475. https://doi.org/10.3724/SP.J.1146.2009.00497
   Google Scholar
• Kalistatov, K. D. (2019, May). Wireless video Monitoring of the Megacities Transport Infrastructure. Civil Engineering Journal, 5(5), 1033–1040. https://doi.org/10.28991/cej-2019-03091309
   Google Scholar
• Kung, H. T., Lin, C.-K., Lin, T.-H., & Vlah, D. (2009). Localization with snap-inducing shaped residuals (SISR). Proceedings of the 15th annual international conference on mobile computing and networking - MobiCom ’09, Beijing, China.
   Google Scholar
• Kushki, A., Plataniotis, K. N., & Venetsanopoulos, A. N. (2007, June). Kernel-based positioning in wireless local area networks. IEEE Transactions on Mobile Computing, 6(6), 689–705. https://doi.org/10.1109/TMC.2007.1017
   Web of Science ®Google Scholar
• Ladd, A. M., Bekris, K. E., Rudys, A., Kavraki, L. E., & Wallach, D. S. (2005, January). Robotics-based location sensing using wireless ethernet. Wireless Networks, 11(1–2), 189–204. https://doi.org/10.1007/s11276-004-4755-8
   Web of Science ®Google Scholar
• McGuire, M., Plataniotis, K. N., & Venetsanopoulos, A. N. (2005, March). Data fusion of power and time measurements for mobile terminal location. IEEE Transactions on Mobile Computing, 4(2), 142–153. https://doi.org/10.1109/TMC.2005.24
   Web of Science ®Google Scholar
• Milioris, D., Tzagkarakis, G., Papakonstantinou, A., Papadopouli, M., & Tsakalides, P. (2014, January). Low-dimensional signal-strength fingerprint-based positioning in wireless LANs. Ad Hoc Networks, 12, 100–114. https://doi.org/10.1016/j.adhoc.2011.12.006
   Web of Science ®Google Scholar
• Nielsen, F., Sun, K., & Marchand-Maillet, S. (2017). k-Means Clustering with Hölder Divergences. Geometric Science of Information, 856–863. doi: 10.1007/978-3-319-68445-1_98
   Google Scholar
• Orr, R. J., & Abowd, G. D. (2000). The smart floor. CHI ’00 extended abstracts on human factors in computing systems - CHI ’00, Netherlands.
   Google Scholar
• Patterson, C. A., Muntz, R. R., & Pancake, C. M. (2003, April). Challenges in location-aware computing. IEEE Pervasive Computing, 2(2), 80–89. https://doi.org/10.1109/MPRV.2003.1203757
   Web of Science ®Google Scholar
• Themelis, K. E., Rontogiannis, A. A., & Koutroumbas, K. D. (2012, February). A novel hierarchical bayesian approach for sparse semisupervised hyperspectral unmixing. IEEE Transactions on Signal Processing, 60(2), 585–599. https://doi.org/10.1109/TSP.2011.2174052
   Web of Science ®Google Scholar
• Wang, B., Zhou, S., Liu, W., & Mo, Y. (2015, January). Indoor localization based on curve fitting and location search using received signal strength. IEEE Transactions on Industrial Electronics, 62(1), 572–582. https://doi.org/10.1109/TIE.2014.2327595
   Web of Science ®Google Scholar
• Wang, X., Guo, Y., Bai, C., Liu, S., Liu, S., & Han, J. (2020, January). The effects of weather on passenger flow of urban rail transit. Civil Engineering Journal, 6(1), 11–20. https://doi.org/10.28991/cej-2020-03091449
   Google Scholar
• Youssef, M., & Agrawala, A. (2005). The horus WLAN location determination system Proceedings of the 3rd international conference on mobile systems, applications, and services - MobiSys ’05, Seattle Washington.
   Google Scholar
• Youssef, M. A., Agrawala, A., & Udaya Shankar, A. (2003). WLAN location determination via clustering and probability distributions. Proceedings of the first IEEE international conference on pervasive computing and communications, Fort Worth, Texas, USA. ( PerCom 2003).
   Google Scholar
• Zhao, Y., Li, W., Wu, J., Lu, S., & Chen, B. (2017). Compressed RSS measurement for communication and sensing in the Internet of Things. Wireless Communications and Mobile Computing, 2017, 1–11. doi: 10.1155/2017/6345316
   Google Scholar
• Zou, H. (2006, December). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418–1429. https://doi.org/10.1198/016214506000000735
   Web of Science ®Google Scholar