Emergent Behaviour in Collaborative Indoor Localisation: An Example of Self-organisation in Ubiquitous Sensing Systems

Kloch, Kamil; Pirkl, Gerald; Lukowicz, Paul; Fischer, Carl

doi:10.1007/978-3-642-19137-4_18

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…Regarding stand-alone positioning in the non-collaborative phase, the most used methods were PDR [ 43 , 63 , 92 , 97 , 98 , 100 , 101 , 102 , 106 , 107 , 110 , 117 , 118 , 120 , 123 , 127 , 128 , 129 , 131 , 141 , 144 , 145 , 146 , 147 ], Ranging [ 42 , 46 , 47 , 81 , 93 , 109 , 111 , 113 , 130 , 132 , 144 , 150 ], RSS-based [ 40 , 41 , 43 , 48 , 77 , 84 , 89 , 103 , 123 , 129 ], k -NN [ 63 , 79 , 108 , 114 , 118 , 119 ,…”

Section: Discussionmentioning

confidence: 99%

“…The study selection process, with step-wise results, is schematically depicted using the PRISMA flow diagram in Figure 2 . As a final set of eligible studies, 84 articles [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 63 , 77 , 79 , 80 , 81 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 ,…”

Section: Methodsmentioning

confidence: 99%

“…Algorithm Self-organized map D I-L S PA+CC [ 50 ] Wi-Fi Wi-Fi AoA, ToA/ToF AoA, ToA/ToF Geom. Ranging GWF N/S I-L S R [ 94 ] IEEE.802.15.4a.CSS IEEE.802.15.4a.CSS RSSI RSSI Multilateration Trilateration D W/I E PA [ 93 ] Wi-Fi Wi-Fi RSSI RSSI Ranging P. Filter N/S N/S S PA [ 92 ] IMU Magnetic Resonant S. DR RSSI PDR-B. M. Probabilistic D. Distrib.…”

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

View full text Add to dashboard Cite

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“…Following a few years later, the COSI-ICT (Science of Complex Systems for Socially Intelligent ICT ) call of 2009 attempted to go beyond the impact on individuals towards the study of interaction between networked intelligent systems and people on social level combining computer science and complex systems approaches. One of the authors (Lukowicz) coordinated the SOCIONICAL project within this initiative where the core work on collaborative indoor location as an initial example of large scale ad-hoc collaboration and model fusion emerged [36]. Focusing more closely on collaboration between human and digital ensembles was the recent FET FOCAS (Fundamentals of Collective Adaptive Systems 1 initiative.…”

Section: Eu Programsmentioning

confidence: 99%

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

Hanika¹,

Herde²,

Kühn³

et al. 2019

Preprint

Self Cite

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The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.

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“…Bluetooth transmits beacons similar to 802.11 radios and the strength of the signal received from these beacons can be captured and measured to provide a range estimate. Kloch et al [6] investigate effects in Collaborative Indoor Localization as an example of self-organizing in ubiquitous sensing systems, using Bluetooth to correct Pedestrian Dead Reckoning (PDR) drift. They analyze the collaborative approach as a solution to the indoor localization problem, and found that when using PDR in isolation the variance grows bigger as people are walking.…”

Section: A Capture -Bluetoothmentioning

confidence: 99%

To wireless fidelity and beyond — CAPTURE, extending indoor positioning systems

Cullen

Curran

Santos

et al. 2014

2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS)

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Most of the current research into indoor localization has surrounded the problem of positioning accuracy, with attempts to solve this using a myriad of technologies and algorithms. One of the problems that seems to be somewhat overlooked is the issue of coverage in an indoor localization solution. The mostly unobstructed views of the Global Positioning System (GPS) which requires a mere 30 satellites to provide global coverage never had these problems. Unfortunately unobstructed views are not something that can be achieved in most indoor environments and economical as well as physical barriers can prevent the installation of an infrastructure to achieve total coverage. In this paper we propose a solution to this issue of indoor coverage by deploying a solution to extend the range of a positioning system -Cooperatively Applied Positioning Techniques Utilizing Range Extension (CAPTURE). CAPTURE provides a system to locate devices that cannot be reached by an in-house location based system. It presents a unique contribution to research in this field by offering the ability to utilize devices that currently know their location within a Location Based Solution (LBS), to help evaluate the position of unknown devices beyond the range capacity of the LBS. Effectively extending the locating distances of an Indoor LBS by utilizing the existing mobile infrastructure without the requirement for additional hardware. CAPTURE uses the Bluetooth radios on mobile devices to estimate the distance between devices, before inserting these range estimates into a trilateration algorithm to ascertain position. CAPTURE has been tested through experiments carried out in a real world environment, proving the capacity to provide a solution to the ranging issue.

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Emergent Behaviour in Collaborative Indoor Localisation: An Example of Self-organisation in Ubiquitous Sensing Systems

Cited by 5 publications

References 5 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

To wireless fidelity and beyond — CAPTURE, extending indoor positioning systems

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Emergent Behaviour in Collaborative Indoor Localisation: An Example of Self-organisation in Ubiquitous Sensing Systems

Cited by 5 publications

References 5 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

To wireless fidelity and beyond &#x2014; CAPTURE, extending indoor positioning systems

Contact Info

Product

Resources

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To wireless fidelity and beyond — CAPTURE, extending indoor positioning systems