Atia, M.M., Noureldin, A. and Korenberg, M.J. (2013). Dynamic online-calibrated radio maps for indoor positioning in wireless local area networks. IEEE Transactions on Mobile Computing, 12(9), 1774–1787.
Au, A.W.S., Feng, C., Valaee, S., Reyes, S., Sorour, S., Markowitz, S.N., Gold, D., Gordon, K. and Eizenman, M. (2013). Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device. IEEE Transactions on Mobile Computing, 12(10), 2050–2062.
Bekkali, A., Masuo, T., Tominaga, T., Nakamoto, N. and Ban, H. (2011). Gaussian processes for learning-based indoor localization. Signal Processing, Communications and Computing (ICSPCC), 2011 IEEE International Conference on, 1–6.
Chai, X. and Yang, Q. (2007). Reducing the calibration effort for probabilistic indoor location estimation. IEEE Transactions on Mobile Computing, 6(6), 649–662.
Chang, K. and Han, D. (2014). Crowdsourcing-based radio map update automation for Wi-Fi positioning systems. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, 24–31.
Chintalapudi, K., Padmanabha Iyer, A. and Padmanabhan, V.N. (2010). Indoor localization without the pain. Proceedings of the sixteenth annual international conference on Mobile computing and networking, 173–184.
Ferris, B., Fox, D. and Lawrence, N. (2007). WiFi-SLAM using Gaussian process latent variable models. International Joint Conference on Artifical Intelligence (2480–2485). Morgan Kaufmann Publishers Inc.
Ferris, B., Hähnel, D. and Fox, D. (2006). Gaussian processes for signal strength-based location estimation. Proceedings of Robotics Science and Systems.
Hansen, R., Wind, R., Jensen, C.S. and Thomsen, B. (2010). Algorithmic strategies for adapting to environmental changes in 802.11 location fingerprinting. Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, 1–10.
Ji, S., Ma, H., Liang, Y., Leung, H. and Zhang, C. (2017). A whitelist and blacklist-based co-evolutionary strategy for defensing against multifarious trust attacks. Applied Intelligence 47(4), 1115–1131.
Jun, J., Gu, Y., Cheng, L., Lu, B., Sun, J., Zhu, T. and Niu, J. (2013). Social-Loc: Improving indoor localization with social sensing. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 14.
Kaemarungsi, K. and Krishnamurthy, P. (2012). Analysis of WLAN's received signal strength indication for indoor location fingerprinting. Pervasive and Mobile Computing, 8(2), 292–316.
Kim, Y., Chon, Y. and Cha, H. (2012). Smartphone-based collaborative and autonomous radio fingerprinting. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(1), 112–122.
Kumar, S., Hegde, R.M. and Trigoni, N. (2016). Gaussian Process Regression for Fingerprinting based Localization. Ad Hoc Networks, 51, 1–10.
Laoudias, C., Piché, R. and Panayiotou, C.G. (2013). Device self-calibration in location systems using signal strength histograms. Journal of Location Based Services, 7(3), 165–181.
Liu, K., Meng, Z. and Own, C.M. (2016). Gaussian Process Regression Plus Method for Localization Reliability Improvement. Sensors, 16(8), 1193.
Mahtab Hossain, A.K.M., Jin, Y., Soh, W.S. and Van, H.N. (2013). SSD: A robust RF location fingerprint addressing mobile devices’ heterogeneity. IEEE Transactions on Mobile Computing, 12(1), 65–77.
Mihaylova, L., Angelova, D., Bull, D. and Canagarajah, N. (2011). Localization of mobile nodes in wireless networks with correlated in time measurement noise. IEEE Transactions on Mobile Computing, 10(1), 44–53.
Pan, S.J., Kwok, J.T., Yang, Q. and Pan, J.J. (2007). Adaptive localization in a dynamic WiFi environment through multi-view learning. National Conference on Artificial Intelligence (Vol.2, 1108–1113). AAAI Press.
Park, J.G., Charrow, B., Curtis, D., Battat, J., Minkov, E., Hicks, J., Teller, S. and Ledlie, J. (2010). Growing an organic indoor location system. International Conference on Mobile Systems, Applications, and Services (271–284). DBLP.
Rai, A., Chintalapudi, K.K., Padmanabhan, V.N. and Sen, R. (2012). Zee: zero-effort crowdsourcing for indoor localization. Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, 293–304.
Rasmussen, C.E. and Williams, C.K.I. (2005). Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press.
Richter, P. and Toledano-Ayala, M. (2015). Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks. Sensors, 15(9), 22587–22615.
Seitz, J., Jahn, J., Boronat, J.G., Vaupel, T., Meyer, S. and Thielecke, J. (2010). A hidden Markov model for urban navigation based on fingerprinting and pedestrian dead reckoning. Information Fusion (FUSION), 2010 13th Conference on, 1–8.
Wu, C., Yang, Z., Liu, Y. and Xi, W. (2013). WILL: Wireless indoor localization without site survey. IEEE Transactions on Parallel and Distributed Systems, 24(4), 839–848.
Wu, C., Yang, Z. and Liu, Y. (2015a). Smartphones based crowdsourcing for indoor localization. IEEE Transactions on Mobile Computing, 14(2), 444–457.
Wu, C., Yang, Z., Xiao, C., Yang, C., Liu, Y. and Liu, M. (2015b). Static power of mobile devices: Self-updating radio maps for wireless indoor localization. 2015 IEEE Conference on Computer Communications (INFOCOM), 2497–2505.
Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y. and Sun, Q. (2017). Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems, 128(C), 71–77.
Yang, S., Dessai, P., Verma, M. and Gerla, M. (2013). FreeLoc: Calibration-free crowdsourced indoor localization. INFOCOM, 2013 Proceedings IEEE, 2481–2489.
Yu, K. and Dutkiewicz, E. (2013). NLOS identification and mitigation for mobile tracking. IEEE Transactions on Aerospace and Electronic Systems, 49(3), 1438–1452.