An Efficient and Robust Fingerprint-Based Localization Method for Multifloor Indoor Environment

Fingerprint-based indoor localization is one of the most promising solutions for various Intelligent Internet of Things (IIoT) systems. However, recent studies show that the key design challenges of current fingerprint-based localization techniques come from the following three aspects: 1) temporal variation caused by various patterns of IIoT device operations and stochastic fluctuation of wireless signals; 2) spatial unevenness of collected received signal strength indicator (RSSI) samples due to complex multifloor environments; and 3) high feature sparsity of collected RSSI samples in large areas. To address these challenges, we present a localization architecture for multifloor indoor localization in multibuilding environment and accordingly propose a fingerprint-based localization method (referred to as GrowNetLoc) based on gradient boosting neural network (GrowNet) and long short-term memory (LSTM) network. Regarding building/floor identification, the gradient ensemble model GrowNet is utilized for extracting the mapping relationship between uneven RSSI samples and building/floor indices. Regarding location estimation, the LSTM network is adopted as one layer of base learner to extract temporal features of RSSI samples, and a gradient boosting strategy is further used for overcoming the sample sparsity issue and improving the location estimation performance. Extensive experiments are conducted on real datasets and the results demonstrate that GrowNetLoc has superior localization accuracy and robustness performance compared with the existing methods.