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In this paper, we propose hybrid building/floor classification andfloor-level two-dimensional location coordinates regression using asingle-input and multi-output (SIMO) deep neural network (DNN) for large-scaleindoor localization based on Wi-Fi fingerprinting. The proposed scheme exploitsthe different nature of the estimation of building/floor and floor-levellocation coordinates and uses a different estimation framework for each taskwith a dedicated output and hidden layers enabled by SIMO DNN architecture. Wecarry out preliminary evaluation of the performance of the hybrid floorclassification and floor-level two-dimensional location coordinates regressionusing new Wi-Fi crowdsourced fingerprinting datasets provided by TampereUniversity of Technology (TUT), Finland, covering a single building with fivefloors. Experimental results demonstrate that the proposed SIMO-DNN-basedhybrid classification/regression scheme outperforms existing schemes in termsof both floor detection rate and mean positioning errors.

Hybrid Building/Floor Classification and Location Coordinates Regression  Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale  Indoor Localization Based on Wi-Fi Fingerprinting