GraphLoc: a graph-based method for indoor subarea localization with zero-configuration

Chen, Yuanyi; Guo, Minyi; Shen, Jiaxing; Cao, Jiannong

doi:10.1007/s00779-017-1011-7

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…Very recently, Zhen et al have presented BigLoc [13] which specifically attempts to solve the localisation problem in large indoor spaces using hundreds of access points for floor level localisation. Chen et al propose a method Graph Loc [8] which requires no upfront calibration. Their graph based method does however require a physical floor plan and crowdsourcing in order to collect a large amount of RSSI data.…”

Section: Related Workmentioning

confidence: 99%

“…The second approach attempts to perform the calibration in a (partially) unsupervised manner. The downside of the former is that it is often a time consuming and convoluted process requiring an expert, while the latter usually relies on crowdsourcing data gathered from multiple people over time [8]. Both contradict our initial aim of a SHiB deployed and setup in their home by individual users.…”

Section: Introductionmentioning

confidence: 97%

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Understanding the quality of calibrations for indoor localisation

McConville

Byrne

Craddock

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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The efficient and effective deployment of Internet of Things (IoT) systems in real world scenarios remains a challenge, particularly in applications such as indoor localisation. Various methods have been proposed recently to calibrate localisation systems, ranging from precise but time consuming processes to those involving little explicit calibration based on a crowdsourced collection of data over time. However it is not clear how to estimate and compare the quality of a specific instance of a calibration. In this paper we present a simple yet effective method of calibrating a Smart Home in a Box (SHiB) together with a framework to combine calibrations while assessing their quality. Our empirical results demonstrate that our calibration method can be performed by untrained users in a short period of time yet is capable of up to 92% accuracy in room level localisation on free living experimental data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Understanding the quality of calibrations for indoor localisation

McConville

Byrne

Craddock

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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“…Several research works that summarize them have been published, either generally, or specifically for a certain technology and/or technique. For example, for the latter, He and Chan [ 56 ] summarizes and classifies the methods used in Wi-Fi Fingerprinting based IPS as probabilistic or deterministic; Chen et al [ 66 ] classifies the localization methods based on received Wi-Fi signal strength into geometric-based and fingerprinting-based schemes; Güvenc and Chong [ 57 ] provides an overview of ToA-based localization methods and classifies them into methods for Line-of-sight (LOS) and Non-line-of-sight (NLOS) scenarios; in contrast, Yassin et al [ 72 ] conducts a general overview of methods covering the basic non-collaborative positioning techniques (Triangulation, Scene Analysis, and Proximity).…”

Section: Introductionmentioning

confidence: 99%

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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“…Recent years have witnessed an increasing attention on indoor positioning in view of its importance to indoor locationbased services, such as indoor advertising [1], patient activity monitoring [2] and indoor location recommendation [3]- [5]. Although a few indoor positioning methods have been proposed (e.g., UWB [6], RFID [7], Bluetooth [8], WiFi [9] and infrared-based techniques [10]), they have some inherent limitations: Ultrasound is vulnerable to indoor reflection and scattering, RFID and Bluetooth-based positioning require extra infrastructure and have small coverage range, Infrared-based positioning cannot cross walls or other obstacles, WiFi-based positioning is unstable due to heterogeneous devices and dynamic environment status.…”

Section: Introductionmentioning

confidence: 99%

Learning Sequence-Based Fingerprint for Magnetic Indoor Positioning System

2019

Self Cite

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Indoor magnetic-based positioning has attracted tremendous interests in recent years due to its pervasiveness and independence from extra infrastructure. Existing methods for indoor magnetic-based positioning are either point-based fingerprint matching or sequence-based fingerprint matching using the raw magnetic field strength. However, the magnetometers in smartphones are vulnerable to a few factors such as user's postures and walking speed, which causes the magnetic field strength corresponding to a location often shift in time or exhibit local distortions, thus greatly limits the positioning performance of existing methods rely on raw magnetic field strength. To this end, we observe the differences among magnetic field strength sequences are mainly attributed to small local segments, and design a new sequence-based fingerprint based on the differences among small local segments of raw MFS sequence to represent raw MFS sequence for indoor positioning. To demonstrate the utility of our proposed sequence-based fingerprint, we have performed a comprehensive experimental evaluation on two datasets, the results show that the proposed approach can significantly improve positioning performance compare with baseline methods.

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GraphLoc: a graph-based method for indoor subarea localization with zero-configuration

Cited by 11 publications

References 28 publications

Understanding the quality of calibrations for indoor localisation

Understanding the quality of calibrations for indoor localisation

Collaborative Indoor Positioning Systems: A Systematic Review

Learning Sequence-Based Fingerprint for Magnetic Indoor Positioning System

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