Abstract
Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as doors, walls, and furnitures. Therefore, many indoor positioning techniques still extract an invalid trajectory from the disturbed RSSI. An invalid trajectory contains distant or impossible consecutive positions within a short time, which is unlikely in a real-world scenario. In this study, we enhanced indoor positioning techniques with movement constraints on BLE (Bluetooth Low Energy) RSSI data to prevent an invalid semantic indoor trajectory. The movement constraints ensure that a predicted semantic position cannot be far apart from the previous position. Furthermore, we can extend any indoor positioning technique using these movement constraints. We conducted comprehensive experimental studies on real BLE RSSI datasets from various indoor environment scenarios. The experimental results demonstrated that the proposed approach effectively extracts valid indoor semantic trajectories from the RSSI data.
Highlights
Indoor location-based services have given rise to the requirement of establishing various safety measures in IoT (Internet of Things)-enabled smart buildings in recent years
We compare the performance of our approach of Hidden Markov Model (HMM) with online Viterbi and constraints (Section 4.5.1), k-nearest neighbors (kNN) with constraints (Section 4.5.2), and Deep Neural Network (DNN) with constraints (Section 4.5.3) to the baseline naïve kNN method, adaptive bandwidth mean shift + kNN [8], unsupervised multivariate
We implement all approaches from scratch using Java except for DNN, which is provided by Tensorflow 1.15
Summary
Indoor location-based services have given rise to the requirement of establishing various safety measures in IoT (Internet of Things)-enabled smart buildings in recent years. Support these services for several applications, such as people behavior analytics, movement patterns extraction, next-to-visit recommendations, and hotspot detection. Positioning devices, such as Wi-Fi [2], BLE (Bluetooth Low Energy) [3,4,5,6,7,8], and RFID (radio frequency identification) [9], provide the trajectories using indoor positioning techniques. Indoor trajectory is a sequence of paired timestamps and visited positions of a user in indoor spaces To capture these positions, the users hold a device, such as a smartphone, that receives the RSSI (received strength signal indicator) of the positioning devices in the indoor space while walking. We require a technique called indoor positioning to estimate the user’s correct position
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