  Indoor localization and Location Based Services (LBS) can greatly benefit
from the widescale proliferation of communication devices. The basic
requirements of a system that can provide the aforementioned services are
energy efficiency, scalability, lower costs, wide reception range, high
localization accuracy and availability. Different technologies such as WiFi,
UWB, RFID have been leveraged to provide LBS and Proximity Based Services
(PBS), however they do not meet the aforementioned requirements. Apple's
Bluetooth Low Energy (BLE) based iBeacon solution primarily intends to provide
Proximity Based Services (PBS). However, it suffers from poor proximity
detection accuracy due to its reliance on Received Signal Strength Indicator
(RSSI) that is prone to multipath fading and drastic fluctuations in the indoor
environment. Therefore, in this paper, we present our iBeacon based accurate
proximity and indoor localization system. Our two algorithms Server-Side
Running Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximity
detection accuracy of iBeacons by 29% and 32% respectively, when compared with
Apple's current moving average based approach. We also present our novel
cascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoor
localization. Our cascaded filter approach uses a Kalman Filter (KF) to reduce
the RSSI fluctuation and then inputs the filtered RSSI values into a Particle
Filter (PF) to improve the accuracy of indoor localization. Our experimental
results, obtained through experiments in a space replicating real-world
scenario, show that our cascaded filter approach outperforms the use of only PF
by 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environments
respectively, and achieves a localization error as low as 0.70 meters in 2D
environment and 0.947 meters in 3D environment.
