Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories

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.

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

“…BLE has the advantage of being implemented as a smartphone application because a beacon, which acts as a transmitter, can be installed in the environment and a smartphone can be used as a receiver and attached to a positioning object. However, it has the disadvantage of being easily affected by interference, reflection, and diffraction by metals [16]. When visible or IR light is used, it is not affected by metals when compared to the case of using radio waves; however, there is a disadvantage that it is necessary to consider the effect of the shielding of light, and the problem of the disturbance of the light exists in the environment [35][36][37][38].…”

A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories

“…Because Global Positioning System (GPS) signals cannot be accessed well in indoor environments, various other signals such as radio frequency identification (RFID) [1], magnetic and light sensors [2], [3], Wi-Fi [4], [5] or Bluetooth low energy (BLE) beacons [6]- [10] have been employed for this purpose. Among all approaches, most studies [7]- [12] have focused on the received signal strength indicator (RSSI) obtained from BLE beacons because of the low cost and compatibility for simultaneous scans.…”

“…Not a lot of studies have focused on this issue. The constraint-based approach [7] discussed the validity of the extracted semantic trajectory in indoor environments. However, it heavily relies on the inferred semantic graph from the floor plan rather and less considers the noisy characteristic of the RSSI signal.…”

Monitoring of Cow Location in a Barn by an Open-Source, Low-Cost, Low-Energy Bluetooth Tag System

“…Algorithms for learning the environment features were used for the localization [27]. Information about the environment structure was used to eliminate impossible locations [8,19,28]. Instead of describing the environment features, a preliminary mapping (fingerprints) of RSS from several points in the environment was performed [10,18,29], though this method required time-consuming mapping, and its accuracy can be restricted by the resolution of the sampled points.…”