Beacon-based proximity detection using compressive sensing for sparse deployment
A proximity-based service (PBS) leverages the estimated proximity to provide users the accessibility to object or location restricted service. This paper exploits the interaction between Bluetooth Low Energy (BLE) Beacon and smartphone to set forth the fundamental building block of a beacon-based PBS system. In real-world scenarios, a beacon-based PBS system might suffer from sparse conditions when some beacons malfunction or beacons can only be deployed in a few specific positions. Motivated by such limitations, a similarity filter extended with compressive sampling matching pursuit (SF-CoSaMP) is proposed to ensure the reliability of proximity detection under such sparse conditions before smartphone proceed to retrieve the corresponding PBS. An extensive simulation with large volume of collected data has been conducted and the results prove the reliability of the proposed algorithm with high detection accuracy in an environment with sparse deployment.