A Robust Indoor Positioning System Based on the Procrustes Analysis and Weighted Extreme Learning Machine

Indoor positioning system (IPS) has become one of the most attractive research fields due to the increasing demands on location-based services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g., smart phones, tablet computers, etc.) and indoor environmental changes (e.g., the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the received signal strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed signal tendency index (STI), for matching standardized fingerprints. An analysis of the capability of the proposed STI to handle device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and weighted extreme learning machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity

Robust occupancy inference with commodity WiFi

Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. in this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity wifi infrastructure. depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. we evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness