A Survey on the Potentials of Indoor Localization Systems in Production

Mieth, Carina; Humbeck, Philipp; Herzwurm, Georg

doi:10.1007/978-3-030-13535-5_11

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Localization privacy and indoor localization systems for mobile networks [16,23] iii. Indoor localization systems based on visible light [5], computer vision [6], acoustic [7], spatial models [10], industry 4.0 [11], Bluetooth & Fingerprinting [18] and pedestrian dead reckoning [28] iv. Simultaneous localization and mapping (SLAM) systems [8] v.…”

Section: Related Survey Workmentioning

confidence: 99%

Privacy-Preserving Wireless Indoor Localization Systems

ADANUR DEDETURK,

KOLUKİSA,

TONYALI

2023

Kocaeli Journal of Science and Engineering

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Recently the number of buildings and interior spaces has increased, and many systems have been proposed to locate people or objects in these environments. At present, several technologies, such as GPS, Bluetooth, Wi-Fi, Ultrasound, and RFID, are used for positioning problems. Some of these technologies provide good results for positioning outdoors whereas some others are effective for indoor environments. While GPS is used for outdoor localization systems, Wi-Fi, Bluetooth, Ultra WideBand, and RFID are used for indoor localization systems (ILSs). Today, due to the proliferation and extensive usage of Wi-Fi access points, wireless-based technologies in indoor localization are preferred more than others. However, even though the abovementioned technologies make life easier for their users, ILSs can pose some privacy risks in case the confidentiality of the location data cannot be ensured. Such an incident is highly likely to result in the disclosure of users’ identity and behavior patterns. In this paper, we aim to investigate existing privacy-preserving wireless ILSs and discuss them.

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Section: Related Survey Workmentioning

confidence: 99%

Privacy-Preserving Wireless Indoor Localization Systems

ADANUR DEDETURK,

KOLUKİSA,

TONYALI

2023

Kocaeli Journal of Science and Engineering

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“…However, UWB is known for slower adoption are, high cost for implementation and signal penetration especially inside complex buildings [7]. A survey of similar applications and system can be found in [8]. Despite the existence of encouraging works in the literature like [9] and [10], most of the existing literature does not high-light the role of 5G, machine learning and big data to cater for massive indoor positioning.…”

Section: State Of the Artmentioning

confidence: 99%

Positioning as Service for 5G IoT Networks

Boudani

Kanaris

Kokkinis

et al. 2021

2021 Telecoms Conference (ConfTELE)

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Big Data and Artificial Intelligence are new technologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms.

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“…Beside obvious benefits like reduced search efforts or a better understanding of system processes, these systems have further, secondary advantages e.g. on process transparency, assistance systems, fault management or production control [13].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the suitability of the systems for different application fields was investigated [12]. For the particular application field of production and logistics processes, a more general discussion about cross-technological potentials is outlined in [13] which especially involves possible benefits of the usage of position data. In terms of data analysis, an algorithm was proposed which finds the current most dense (i.e.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Movement Detection in Indoor Positioning Systems

Flossdorf,

Meyer,

Artjuch

et al. 2021

Preprint

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In recent years, the usage of indoor positioning systems for manufacturing processes became increasingly popular. Typically, the production hall is equipped with satellites which receive position data of sensors that can be pinned on components, load carriers or industrial trucks. This enables a company e.g. to reduce search efforts and to optimize individual system processes. In our research context, a sensor only sends position information when it is moved. However, various circumstances frequently affect that data is undesirably sent, e.g. due to disrupting factors nearby. This has a negative impact on the data quality, the energy consumption, and the reliability of the whole system. Motivated by this, we aim to distinguish between actual movements and signals that were undesirably sent which is in particular challenging due to the susceptibility of indoor systems in terms of noise and measuring errors. Therefore, we propose two novel unsupervised classification algorithms suitable for this task. Depending on the question of interest, they rely either on a distancebased or on a time-based criterion, which allows to make use of all essential information. Furthermore, we propose an approach to combine both classifications and to aggregate them on spatial production areas. This enables us to generate a comprehensive map of the underlying production hall with the sole usage of the position data. Aside from the analysis and detection of the underlying movement structure, the user benefits from a better understanding of own system processes and from the detection of problematic system areas which leads to a more efficient usage of positioning systems. Since all our approaches are constructed with unsupervised techniques, they are handily applicable in practice and do not require more information than the output data of the positioning system.

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A Survey on the Potentials of Indoor Localization Systems in Production

Cited by 7 publications

References 13 publications

Privacy-Preserving Wireless Indoor Localization Systems

Privacy-Preserving Wireless Indoor Localization Systems

Positioning as Service for 5G IoT Networks

Unsupervised Movement Detection in Indoor Positioning Systems

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