Imperial College London

MrFaheem

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Postgraduate
 
 
 
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Contact

 

faheem16 Website CV

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zafari,
author = {Zafari, F and Papapanagiotou, I and Devetsikiotis, M and Hacker, T},
title = {An iBeacon based Proximity and Indoor Localization System},
url = {http://arxiv.org/abs/1703.07876v2},
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Indoor localization and Location Based Services (LBS) can greatly benefitfrom the widescale proliferation of communication devices. The basicrequirements of a system that can provide the aforementioned services areenergy efficiency, scalability, lower costs, wide reception range, highlocalization 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'sBluetooth Low Energy (BLE) based iBeacon solution primarily intends to provideProximity Based Services (PBS). However, it suffers from poor proximitydetection accuracy due to its reliance on Received Signal Strength Indicator(RSSI) that is prone to multipath fading and drastic fluctuations in the indoorenvironment. Therefore, in this paper, we present our iBeacon based accurateproximity and indoor localization system. Our two algorithms Server-SideRunning Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximitydetection accuracy of iBeacons by 29% and 32% respectively, when compared withApple's current moving average based approach. We also present our novelcascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoorlocalization. Our cascaded filter approach uses a Kalman Filter (KF) to reducethe RSSI fluctuation and then inputs the filtered RSSI values into a ParticleFilter (PF) to improve the accuracy of indoor localization. Our experimentalresults, obtained through experiments in a space replicating real-worldscenario, show that our cascaded filter approach outperforms the use of only PFby 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environmentsrespectively, and achieves a localization error as low as 0.70 meters in 2Denvironment and 0.947 meters in 3D environment.
AU - Zafari,F
AU - Papapanagiotou,I
AU - Devetsikiotis,M
AU - Hacker,T
TI - An iBeacon based Proximity and Indoor Localization System
UR - http://arxiv.org/abs/1703.07876v2
ER -