TILoc: Improving the Robustness and Accuracy for Fingerprint-Based Indoor Localization

Li, Hualiang; Qian, Zhihong; Tian, Chunsheng; Wang, Xue

doi:10.1109/jiot.2020.2964875

Cited by 21 publications

(8 citation statements)

References 34 publications

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“…An array calibration method [12] and Angle of Arrival estimation algorithm were designed to achieve flexible orientation using limited resources. TILoc (Torus Intersection Localization) [13] aims to improve accuracy and robustness by mitigating noise problems and positioning a target. A lightweight privacypreserving scheme (LWP 2 ) [14] protects data privacy and location privacy at a lower cost by minimizing the least square error for an over-determined linear formulation.…”

Section: Related Workmentioning

confidence: 99%

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Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback-Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.

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Section: Related Workmentioning

confidence: 99%

“…Entropy can be used to measure the disorder of a data set which usually ranges between 0 and 1. Still, sometimes it can also be greater than 1, which (13) ∀i ∈ {1, ...., b}if (F t innbr(F (t−1) )) → valid;else → invalid indicates that there is a considerable amount of disorder, entropy can be defined as in (14):…”

Section: Kullback-leibler Divergencementioning

confidence: 99%

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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“…Another direction to enhance RSSI localization is to use pattern matching and fingerprinting based methods for reducing the influence of range measurement errors [ 17 , 19 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 37 , 38 , 39 , 40 , 41 ]. The LANDMARC indoor localization system is presented in [ 22 ] as a pattern matching method to enhance the overall accuracy of locating objects using some reference tags.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [ 27 ] proposes to use the feature-scaling-based k-nearest neighbor algorithm with RSSI. In [ 28 , 29 ], neural network and machine learning algorithms are used for RSSI fingerprints, respectively, in order to improve the localization accuracy. Even fingerprint methods offer higher accuracy and better robustness, they require more cost in the facility and more complexity, which does not meet IoT expectations [ 4 ].…”

Section: Related Workmentioning

confidence: 99%

“…Generous research effort has been devoted to developing localization methods that can compensate for the adverse effects of uncertainties in RSSI measurements [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Most of these proposed methods depend on the optimization techniques or fingerprinting, which requires high computational complexity, making them unpractical for IoT localization applications.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

El-Absi

Zheng

Abuelhaija

et al. 2020

Sensors

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Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

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UWB/IMU Fusion Localization Strategy Based on Continuity of Movement

Zhang

Bao

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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TILoc: Improving the Robustness and Accuracy for Fingerprint-Based Indoor Localization

Cited by 21 publications

References 34 publications

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

UWB/IMU Fusion Localization Strategy Based on Continuity of Movement

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