RSSI Measurements 1
The goal of this experiment is to attain a near optimal beacon spacing to reduce cost of buying beacons, while preserving the highest possible accuracy. Since accuracy is dependent on many factors, we initially focus on estimating the maximum distance to one beacon. We expect to measure signal error caused by attenuation, which is the gradual loss of quantity which passes through a substance or medium.
Indoor Positioning is a wide area of research with many unsolved problems and different techniques. There is also an annual competition by Microsoft. Some research indicates that "[...] one should assume 1dB of attenuation per metre of an indoor office / residential environment.“ [1] Others claim „[…] that when the RSSI is above the sensitivity threshold (about -87 dBm), the packet reception rate (PRR) is at least 85%.“ [2] On the other hand performing these experiments in an indoor office can show that RSSI is a bad estimator of link quality. [3]
We've measured on a terrace (6th floor) in Berlin Mitte at around 5pm. The weather was cloudy, 2°-5°C with a humidity of about 85%. The Beacon has been attached to a cardboard box about 1 meter above the floor, facing towards the devices (circle 1). The devices have been placed on foam cubes at the same height, facing towards the beacon (circle 2).
We've advertised iBeacon frames using the BlueBeacon Maxi from BlueUp at in interval of 100 ms with the radio Tx power set to +- 0 dBm.
- Hardware revision: 2.0
- Firmware revision: 5.12
- Software revision: S110-v.8.0.0
- Radio Version: 4.0 Bluetooth Smart (Bluetooth Low Energy)
- Radio Frequency: 2.402 to 2.480 GHz
- Radio Module: Nordic SoC nRF51822 (MCU and transceiver)
- Antenna: Printed meandered planar F-antenna
We've used Android devices from 4 different manufacturers:
- Google Pixel (Android P, DP 1)
- Samsung Galaxy S8 (Android O, 8.0.0)
- HTC U11 (Android O, 8.0.0)
- BlackBerry KEYone (Android N, 7.1.1)
All devices were unlocked, purchased in Germany, had WiFi disabled and only the BLE Indoor Positioning App (f30e55e) running.
We measured with all devices listed above at each distance for at least 60 seconds to make sure that no temporary spikes are affecting the results.
Figure 1 and 2 show the calculated mean RSSI (using the MeanFilter) with a sliding window of 3 seconds.
Figure 1
Figure 2
We calculated the RSSI variance with a sliding window of 10 seconds and extracted the standard deviation from it. Figure 3 and 4 focus on the standard deviation in relation to the distance. To use the RSSI later for our distance calculation we utilize the log distance path loss model. Since this function is logarithmic, a difference of 1 dBm represents a smaller distance for higher RSSI values. Therefore, the same standard deviation at a higher distance is worse, because of the resulting distance due to the log loss distance calculation.
Figure 3
Figure 4
We can observe that in our experiment there is no clear relationship between the standard deviation of the RSSI values and the distance to the beacon. We assume this is due to multipath propagation. When averaging all devices in Figure 4, we can see that the standard deviation drastically increases after 3m and is relatively constant at 10 meters and further away. Furthermore, we can observe significant differences between the four tested devices. The Google Pixel and the Blackberry KEYone produced significant less deviation, than the HTC U11 and the Samsung Galaxy S8.
[1] R. Faragher und R. Harle, „An analysis of the accuracy of bluetooth low energy for indoor positioning applications“, in Proceedings of the 27th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+’14), 2014, S. 201–210.
[2] K. Srinivasan und P. Levis, „RSSI is Under Appreciated“, 2006, S. 5.
[3] K. Benkic, M. Malajner, P. Planinsic, und Z. Cucej, „Using RSSI value for distance estimation in wireless sensor networks based on ZigBee“, 2008, S. 303–306.