Chen CLai A.IWu PRUEY-BEEI WU2023-06-092023-06-09202223274662https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124176396&doi=10.1109%2fJIOT.2022.3147644&partnerID=40&md5=ce2fc1c5e9a3c745ffabb659f2434b6ahttps://scholars.lib.ntu.edu.tw/handle/123456789/632371To fulfill the need for high accuracy indoor positioning in many location-based services (LBS) and the emerging Internet of Things (IoT) applications, in this paper we propose a novel scene-analysis positioning solution of Multi-Detector Deep Neural Network (DNN) architecture, with preprocessing steps, model optimization techniques, and variance estimation methods. During the off-line site-surveying phase in our approach, fingerprint databases are created by purposely built robotic surveying devices traversing the target site to gather perceivable Wi-Fi and other signals including to create spatial positioning models for further use in the online positioning phase. The intricate non-linear relationship between fingerprints and spatial positions are thus resolved by the Multi-Detector DNN in our approach. Hyper-parameter analyses were conducted to further optimize our proposed Multi-Detector model in terms of complexity, achieving at least 6.7 times of parameter complexity reduction while retaining <1% degradation of 0.9m (3ft) positioning accuracy level. IEEEDeep Neural Network; Detectors; Estimation; Feature extraction; Fingerprint recognition; Indoor positioning; Internet of Things; Internet of Things; Model Optimization.; Neural networks; Wi-Fi Fingerprinting; Wireless fidelity[SDGs]SDG9[SDGs]SDG11Complex networks; Deep neural networks; Feature extraction; Indoor positioning systems; Internet of things; Location based services; Surveys; Telecommunication services; Wireless local area networks (WLAN); Features extraction; Fingerprint Recognition; High-accuracy; Indoor positioning; Model optimization; Model optimization.; Multi-detectors; Neural-networks; Wi-fi fingerprinting; Wireless fidelities; Wi-Fijournal article10.1109/JIOT.2022.31476442-s2.0-85124176396