Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm
AuthorsMaghdid, Halgurd S
Ghafoor, Kayhan Zrar
Singh, Pranav Kumar
Sadiq, Ali Safaa
Singh, Pranav Kumar
Rawat, Danda B.
MetadataShow full item record
AbstractIndoor localization algorithms in smart cities often use Wi‐Fi fingerprints as a database of Received Signal Strength (RSS) and its corresponding position coordinate for position estimation. However, the issue of fingerprinting is the use of different platform‐devices. To this end, we propose a Long Short‐Term Memory (LSTM)‐based novel indoor positioning mechanism in smart city environment. We used LSTM, a type of recurrent neural network to process sequential data of users’ trajectory in indoor buildings. The proposed approach first utilizes a database of normalizing fingerprint landmarks to calculateWiFi Access Points (WAPs) RSS values to mitigate the fluctuation issue and then apply the normalization parameters on the RSS values during the online phase. Afterwards, we constructed a transfer model to adapt the RSS values during the offline phase and then applying it on the RSS values from the different smartphones during the online phase. Thorough simulation results confirm that the proposed approach can obtain 1.5 to 2 meters positioning accuracy for indoor environments, which is 60 % higher than traditional approaches.
CitationMaghdid, H.S., Ghafoor, K.Z., Al-Talabani, A., A-Shakarchi, A., Singh, P.K. and Rawat, D.B. (2020) Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm, Internet Technology Letters. https://doi.org/10.1002/itl2.200
JournalInternet Technology Letters
DescriptionThis is an accepted manuscript of an article published by Wiley in Internet Technology Letters on 17/09/2020, available online: https://doi.org/10.1002/itl2.200 The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/