Decentralized Indoor Localization Based on A Sparse Gaussian Process with Reduced-Dimensional Inputs for Real-Time Sensing and Training on IoT Devices

As a large number of Internet of Things (IoT) devices are deployed in the field, there arises huge potential of edge computing for indoor localization on those devices. Conventional indoor localization based on a centralized server with substantial computational resources, often covering a number of multistory buildings, cannot easily adapt to time-varying indoor electromagnetic environments due to its high cost of fingerprint database update and model retraining; the centralized server is also susceptible to security breaches. To address these issues, we propose a decentralized indoor localization framework, leveraging models based on a Sparse Gaussian Process with Reduced-dimensional Inputs (SGP-RI) deployed to IoT devices for a smaller service area, which can quickly adapt to time-varying indoor electromagnetic environments through real-time sensing and retraining. The experimental results based on a multibuilding, multifloor static database and a single-building, single-floor dynamic database, demonstrate the feasibility of the proposed framework, where the SGP-RI with less than half the training samples can produce localization performance comparable to the standard Gaussian process (GP) with the whole training samples.