Indoor Localization Based on Infrared Angle of Arrival Sensor Network
1.1. Related Work: Indoor Localization Methods and Solutions
1.1.1. Systems Using RF Signal
1.1.2. Systems Using Light Sources
1.1.3. Infrastructure-Free Systems
1.2. The Overview of the Proposed Solution
- Novel IR AoA sensor, made of inexpensive off-the-shelf components, enabling AoA estimation with an error around 1°,
- Wireless sensor network, based on the proposed IR AoA sensor, which provides infrastructural support for real-time navigation,
- Localization strategy/method/algorithm, utilizing the proposed WSN and a spatial context (aisle graph), with suitable localization accuracy,
- Supermarket navigation model based on shelves graph and aisles graph,
- Server, API, and client applications suite, demonstrating both the features and the look-and-feel of the proposed system.
2. Materials and Methods
2.1. Angle-of-Arrival Sensor
- The JeeLink node, in the scheme labeled as node 1, serves as a gateway: to send commands to nodes 2 and 3, and to receive measured data from node 2.
- Node 2 is mounted on a rotating platform and attached to the sensor being calibrated.
- Node 3 is an infrared transmitter, i.e., it serves as a controlled IR radiation source with known distance and AoA relative to node 2.
2.1.3. Estimation Algorithm
2.2. Showcase Application: Supermarket Navigation
2.2.1. Wireless Sensor Network
- Select measurement → from all measurements in the set, pick the one with the highest maximum measured irradiance Eei. This step is based on the simple heuristic assuming that the highest irradiance measurement correlates with the lowest distance between the transmitter and the sensor, and, more importantly, with the lowest geometric dilution of precision (GDOP).
- Estimate location from selected measurement → selected measurement, along with the position of the corresponding sensing node, is used in the simple equation to estimate cart location:
- Estimate the location on the aisles graph → find the nearest point on the aisles graph edge from the estimated location. This step is usually straightforward since the aisles graph itself is constructed according to the positions of the sensors; thus, the distance of the estimated location from the graph tends to be zero. As will be described later, this mapping of the location to the aisles graph edges is important for the shortest path navigation to the products on the shelves.
2.2.2. Server and API
- WSN measurements retrieval and storage,
- WSN node layout management,
- Cart location estimation and update,
- Product locations management,
- Store layout management,
- User signup and login,
- Token-based authentication,
- User cart registration,
- Shopping lists management,
- Shopping list based shortest path navigation (directions).
2.2.3. Client Applications
2.2.4. Prospective IoT Services
- E1: Static-1D (empirical, laboratory settings),
- E2: Mobile-1D (empirical, laboratory settings),
- E3: Static/Mobile-2D (empirical, laboratory settings),
- E4: Large-scale Mobile-2D (simulation).
Conflicts of Interest
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|Infrastructure||RF||WiFi, Bluetooth Low Energy (BLE), |
Radio-frequency identification (RFID)
|Light||Visible Light Positioning (VLP), Infrared (IR)|
|Infrastructure-free||Magnetic, Sensor fusion, OCR|
|Method||Commercial Example||Typical Accuracy||Install. Costs||Energy Cons.||Main Drawbacks|
|VLP||ByteLight, Philips||50 cm||high||high||high computational requirements (real-time image processing) and installation costs|
|BLE||iBeacon (Apple)||>2 m||medium||low||low signal range, hard to achieve sub-meter precision|
|UWB||Sewio||30 cm||high||low||the need for precise time synchronization of anchor nodes, low range, specialized high-priced hardware design|
|WiFi||WiFiSLAM (Apple)||1–2 m||low||medium||site survey fingerprinting, high sensitivity to changes in the environment|
|Magnetic||IndoorAtlas||1–2 m||no||medium||magnetic field mapping, error increases with the size of the fingerprinting map|
|Sensor fusion||Project Tango||N/A||no||high||R&D phase, limited availability|
|IR AoA||proposed solution||3 cm (static 1D)|
20–50 cm (mobile 1D)
40 cm (static 2D)
<1 m (mobile 2D)
|low||low||low range, IR collision|
|Cart Speed||Mean Error [cm]||STD [cm]|
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Arbula, D.; Ljubic, S. Indoor Localization Based on Infrared Angle of Arrival Sensor Network. Sensors 2020, 20, 6278. https://doi.org/10.3390/s20216278
Arbula D, Ljubic S. Indoor Localization Based on Infrared Angle of Arrival Sensor Network. Sensors. 2020; 20(21):6278. https://doi.org/10.3390/s20216278Chicago/Turabian Style
Arbula, Damir, and Sandi Ljubic. 2020. "Indoor Localization Based on Infrared Angle of Arrival Sensor Network" Sensors 20, no. 21: 6278. https://doi.org/10.3390/s20216278