A measurement-based blocking distribution for improving localization in warehouse environments

Rabeah, Naif; Aditya, Sundar; Alawwad, Faisal; Molisch, Andreas F.; Behairy, Hatim

doi:10.1109/iceaa.2016.7731546

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Rabeah et al [131] discuss the problem of blocked LoS in an indoor environment such as a warehouse where the presence of storage racks as well as different impediments greatly influence the presence of LoS. The authors obtain the blocking distribution which can then be used for accurate localization.…”

Section: B Device Based Localizationmentioning

confidence: 99%

A Survey of Indoor Localization Systems and Technologies

Zafari¹,

Gkelias²,

Leung³

2017

Preprint

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Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an upto-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

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Section: B Device Based Localizationmentioning

confidence: 99%

A Survey of Indoor Localization Systems and Technologies

Zafari¹,

Gkelias²,

Leung³

2017

Preprint

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show abstract

“…However, in the recent years lots of other devices, such as smartwatches, fridges, even bulbs or toothbrushes, have joined the world of connected devices. This trend seeks to help the users in every aspect of their life (Atzori et al, 2010) with IoT applications in smart homes (Bergeron et al, 2018), cities (Islam et al, 2018) or warehouses (Rabeah et al, 2016). With smart sensors almost everywhere, IoT applications will require or benefit from a reliable and accurate localisation of certain devices.…”

Section: Introductionmentioning

confidence: 99%

WiFiNet: WiFi-based indoor localisation using CNNs

Hernández¹,

Parra²,

Corrales³

et al. 2021

Expert Systems with Applications

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Different technologies have been proposed to provide indoor localisation: magnetic field, bluetooth , WiFi, etc. Among them, WiFi is the one with the highest availability and highest accuracy. This fact allows for an ubiquitous accurate localisation available for almost any environment and any device. However, WiFi-based localisation is still an open problem.In this article, we propose a new WiFi-based indoor localisation system that takes advantage of the great ability of Convolutional Neural Networks in classification problems. Three different approaches were used to achieve this goal: a custom architecture called WiFiNet designed and trained specifically to solve this problem and the most popular pre-trained networks using both transfer learning and feature extraction.Results indicate that WiFiNet is as a great approach for indoor localisation in a medium-sized environment (30 positions and 113 access points) as it reduces the mean localisation error (33%) and the processing time when compared with state-of-the-art WiFi indoor localisation algorithms such as SVM.

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“…The received signal is sampled at the sampling rate of 41.3 ps to obtain the precise range measurement. Similarly, authors in [139], demonstrated a precise UWB based system for warehouse application where the presence of racks affects the LOS. They obtain the blocking distribution and use them to improve the accuracy of the system.…”

Section: State-of-the-artmentioning

confidence: 99%

High resolution indoor localization system using ultra wideband impulse radio

Vashistha¹

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Assoc. Prof. Choi Look Law for providing me the opportunity to pursue higher degree. I am also thankful to him for providing me the invaluable project direction and imbibing in me, the professional ethics of work. I would also like to thank my thesis advisory committee Assoc. Prof. Tay Wee Peng and Assoc. Prof. A. S. Madhukumar for their invaluable suggestions. I extend my sincere thanks to Prof. Tay for his rigorous reviews, which has improved the quality of my thesis. Their excellent cooperation and suggestion provided me with an impetus to work and made the completion of work possible.Next, I am thankful to my mother Nikita Sharma who has raised me up in life against all odds with many sacrifices. I will always be indebted to her for the unconditional love, care, support, and blessings. Many thanks to my brother Ankur Vashistha for being a source of inspiration and showing me the directions in life. I want to extend a deepest gratitude to my wife Shubha Sharma who is a constant source of love, support and motivation for me. She encouraged me and stood by me in my lowest times. I want to thank all my family members for their continuous support.I am thankful to my research group members Dr. Manmohan Sharma, Mr. Arif Hussain for their help and suggestions during the period of my work. I am also thankful to all the staff members of

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A measurement-based blocking distribution for improving localization in warehouse environments

Cited by 4 publications

References 6 publications

A Survey of Indoor Localization Systems and Technologies

A Survey of Indoor Localization Systems and Technologies

WiFiNet: WiFi-based indoor localisation using CNNs

High resolution indoor localization system using ultra wideband impulse radio

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