[논문]A User Movement Direction Detecting Method through Data Analysis of BLE Beacons and Its Implementation

Choe, Jong-gak; Kwon, YongJin

doi:10.3837/tiis.2019.10.005

A User Movement Direction Detecting Method through Data Analysis of BLE Beacons and Its Implementation 원문보기

KSII Transactions on internet and information systems : TIIS, v.13 no.10, 2019년, pp.4908 - 4922

Choe, Jong-gak (Department of Telecommunication and Information Engineering, Korea Aerospace University) , Kwon, YongJin (School of Electronics and Information Engineering, Korea Aerospace University)

Abstract ▼ AI-Helper

The popularization of smartphones in recent years has created a rich ground for online-to-offline (O2O) services based on location information. In the process of finding user locations in O2O services, BLE (Bluetooth Low Energy) beacons are widely used because the beacons are economical in many ways. The current BLE method does not specify the direction of user movement, but adding that information could enrich the user experience for various O2O services. This paper proposes a method that identifies the user movement direction through data analysis on data sets generated by a pair of BLE beacons. Also we demonstrate its implementation with examples of services that need the direction information of users in order to show the feasibility of the method proposed.

주제어

표/그림 (17)

그림 Fig. 1. Illustration of a BLE-specific location-based service.
그림 Fig. 2. Mapped distance based on received signal strength from a BLE beacon [4]
그림 Fig. 3. Detecting user movement direction with two BLE beacons
그림 Fig. 4. Ideal BLE beacon signal range
그림 Fig. 5. RSSI levels for two BLE beacons while user approached the beacon region
그림 Fig. 6. BLE beacon signal accumulation and expiration
그림 Fig. 7. Accepted RSSI value range for determining direction of movement
그림 Fig. 8. Overall algorithm to determine the direction of user movement
그림 Fig. 9. Environment setup for calibration
그림 Fig. 10. Measured signal
그림 Fig. 11. The Decision Distance
그림 Fig. 12. Missing RSSI values
표 Table 1. Participant moving from right to left
표 Table 2. Participant moving from left to right
그림 Fig. 13. Information services based on direction of user movement
그림 Fig. 14. An exemplar service using direction of user movement
표 Table 3. Components for the suggested service

AI 본문요약
AI-Helper

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가설 설정

The founding premise of this paper is based on the idea that when a user approaches a pair of beacons at different distances from afar, the RSSI of the closer beacon would appear stronger than the RSSI of the farther beacon (see Fig. 3).

제안 방법

For example, a hotel app could determine whether the user is entering or leaving the hotel and provide tour information or room/hotel information by identifying the direction of the user’s movement. In this paper, we propose a method that identifies the direction of user movement within a location-based services system using a pair of BLE beacons based on data analysis.
The organization of this paper is as follows: Section 2 reviews existing general methods that determine movement direction and use BLE beacons; Section 3 presents a method that finds user movement direction using a pair of BLE beacons based on data analysis; and in Section 4 we demonstrate our implementation of this system and evaluate it by experimentation. Possible services that would benefit from the use of direction information are suggested in Section 5, and the paper concludes in Section 6 with suggestions for further research and related work.
This paper presents a method to identify the direction of user movement using only two BLE beacons. Since the radio signals of BLE becons are really weak and affected easily by various environmental factors, so that it is difficult to use the signals directly for detecting user movement direction.
Even so, the overall assumption that signals from the beacon closer to the user have stronger RSSI values was valid. With this observation, we decided to make data sets by accumulate RSSI values to negate instantaneous errors or inconsistencies effectively, and then determine the direction of user movement by using average of each data set. We show detailed implementation of the method for determining the user movement direction based on data analysis in section 4.

성능/효과

In other words, the RSSI from the beacon on the left was measured to be stronger than the RSSI from the beacon on the right. In our experiment, a signal interval of 318.75ms produced the best results, with the most evenly distributed signal strength indicators and the fewest missing signals. Based on this observation, we chose an interval of 318.

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