[Paper Review] An iBeacon based Proximity and Indoor Localization System

Faheem Zafari, Ioannis Papapanagiotou+2 more|arXiv (Cornell University)|Mar 22, 2017

Indoor and Outdoor Localization Technologies8 references51 citations

TL;DR

The paper develops server-side proximity and indoor localization using iBeacons by introducing SRA and SKF for proximity, and a cascaded Kalman Filter-Particle Filter (KFPF) for localization, achieving notable accuracy gains over standard iBeacon approaches.

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 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.

Motivation & Objective

• Improve proximity detection accuracy of iBeacons beyond RSSI-based moving averages.
• Enable accurate indoor localization using iBeacons despite RSSI fluctuations.
• Offload computation to a server to reduce user-device energy consumption.
• Develop and validate algorithms that work in 2D and 3D indoor environments.

Proposed method

• Develop Server-Side Running Average (SRA) to map RSSI to distance via a site-specific path-loss model and classify proximity zones.
• Develop Server-Side Kalman Filter (SKF) to smooth RSSI before distance conversion using a Kalman filter on RSSI and its rate of change.
• Introduce a cascade Kalman Filter-Particle Filter (KFPF) where KF smooths RSSI and PF performs non-linear localization using Bayesian filtering.
• Use RSSI and rate-of-change as KF state, and distance estimates from the path-loss model as inputs to a Particle Filter for 2D/3D localization.
• Experimentally fit environment-specific path-loss curves and compare proximity/localization performance against a moving-average baseline.

Experimental results

Research questions

• RQ1 How much can proximity accuracy be improved for iBeacons using server-side filtering over standard RSSI-based methods?
• RQ2 Can Bayesian filtering (Kalman and Particle Filters) improve indoor localization accuracy when using iBeacons?
• RQ3 Does a cascaded KF-PF approach yield measurable gains in 2D and 3D localization compared to PF alone?
• RQ4 What is the energy and latency impact of offloading computation to a server for iBeacon-based PBS/LBS?

Key findings

• SRA improves proximity detection accuracy by 29% over Apple’s moving-average approach.
• SKF improves proximity detection accuracy by 32% over Apple’s moving-average approach.
• KFPF cascade improves localization accuracy by 28.16% in 2D and 25.59% in 3D versus using PF alone.
• Localization error as low as 0.70 meters in 2D and 0.947 meters in 3D environments.
• Experiments used real-world-like rooms and iPhone 6s Plus with Gimbal Series 10 beacons, with environment-specific path-loss models fitted to RSSI data.