Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm

Indoor 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 calculate WiFi 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.

Automated summary

This paper proposes an indoor positioning mechanism based on LSTM, which utilizes normalization parameters to address the fluctuation issue of Wi-Fi fingerprints and applies a transfer model to adapt the RSS values from different smartphones, achieving higher positioning accuracy.