An iBeacon based Proximity and Indoor Localization System
Faheem Zafari, Ioannis Papapanagiotou, Michael Devetsikiotis, Thomas, Hacker

TL;DR
This paper introduces an accurate indoor localization system using iBeacons, employing novel filtering algorithms that significantly improve proximity detection and localization accuracy in indoor environments.
Contribution
The paper presents new server-side algorithms (SRA and SKF) for improved proximity detection and a cascaded Kalman Filter-Particle Filter approach for enhanced indoor localization accuracy.
Findings
Proximity detection accuracy improved by up to 32%
Localization error reduced to as low as 0.70 meters in 2D
Cascaded filtering outperforms single filters in accuracy
Abstract
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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Underwater Vehicles and Communication Systems · Bluetooth and Wireless Communication Technologies
