In this paper, for a sparse beacon network scenario,
we propose a machine learning based SF-CoSaMP
algorithm to refine the performance achieved by the
conventional SF-CoSaMP. Simulation results show
that the proposed algorithm is effective, in particular,
for some PoIs which are far from any working beacon.
Machine Learning based Proximity Detection
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Indoor proximity-based service (PBS) has been
employed as a substitution to indoor location-based
service (LBS) when mobile devices are in close
proximity regardless of their exact location
information [1]. Bluetooth low energy (BLE) beacons
are often used to get the proximity information of a
mobile device for PBS based systems.