Han Jun Bae and Lynn Choi*
Since the adoption of Bluetooth Low Energy (BLE) in the Bluetooth standard in 2010, BLE beacons are emerging as
one of the most viable solutions for indoor localization due to its power efficient architecture, short scan duration, low cost
chipset, and wide adoption in the devices. The existing indoor positioning systems based on BLE beacons employ the
classical fingerprinting (FP) technique where user terminals collect signals from the beacons and do most of localization
computations, requiring significant power consumption on user devices. However, constant power consumption
on limited battery life of a mobile device can be problematic when it comes to supporting server-oriented tracking
applications. To address this issue, we have proposed a new fingerprinting technique called inverse fingerprinting
(Inv-FP), which is a server side BLE fingerprint system where most of the positioning computations are done by BLE
sniffers and servers, thus minimizing the computation overhead of user devices. However, the absolute positioning
schemes such as FP and Inv-FP do not use the current position estimate to determine the next position. This leads to
discontiguous, irregular route prediction especially when the positioning accuracy is low, since it does not reflect the
continuity of the position change according to the movement of the user. In contrast, a relative positioning scheme such
as Pedestrian Dead Reckoning (PDR) determines the current position based on the previous position, reflecting the
continuity of the position change but it cannot estimate the current position without the initial position. In this paper, we
implement both FP and Inv-FP and evaluate their performance in small and large-scale testbeds. We analyze various
characteristics of Inv-FP in comparison with the classical beacon based FP, and demonstrate that Inv-FP can match
the performance of FP but with minimal power consumption on user devices. In addition, we propose a new localization
algorithm that can combine Inv-FP with PDR. By integrating PDR with Inv-FP, we show that localization error can be
reduced by reflecting the advantages of each method.
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