Review- ďBLED One Touch InformationĒ
Proximity¬†based¬†services¬†(PBS)¬†require¬†high¬†precision¬†detection,¬†energy¬†efficiency,¬†wide¬†range¬†of¬†reception,¬†low¬†cost¬†and¬†availability.¬†Most¬†of¬†the¬†existing¬†technologies, however,¬†can¬†not¬†meet¬†those¬†requirements.¬†Apple's¬†Low¬†Energy¬†Bluetooth¬†(BLE),¬†called¬†onecandidate during this domain and has become an almost industry standard for PBS. However, due to its various limitation, it suffers from poor accuracy in proximity detectionReceived Signal Strength Indicator (RSSI) to enhance proximity detection accuracy of BLE device, we present two algorithms that address the inherent flaws in BLE Beacons current proximity detection approach. Our first algorithm, sever side running average (SRA), uses the pathlossmodel based estimated distance for proximity classification. Our second, extreme Kalman Filter (SKF) algorithm, uses Kalman Filter together with SRA, ourexperimental result show that SRA and SKF perform better than the present moving average approach utilized by BLE Beacon. SRA ends up in a couple of 29% improvement while SKF ends up in a couple of 32% improvement over the present approach in proximity detection accuracy.