Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

Georgievska, Sonja; Rutten, Philip; Amoraal, J.; Ranguelova, Elena; Bakhshi, Rena; Vries, Ben L. de; Lees, Michael; Klous, S.

doi:10.1186/s40537-019-0194-3

Cited by 18 publications

(13 citation statements)

References 44 publications

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“…Moreover, several of them focuses on studying the real effects of MAC randomization [93,94] or device de-anonymization [95,96]. There are in the literature some works that leverage PRs, e.g., in crowd detection [97] and behavior [98] or device classification [99], but we believe that there is still room for improvement.…”

Section: Rqmentioning

confidence: 99%

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Atzeni

Bacciu

Mazzei

et al. 2022

Sensors

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Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.

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Section: Rqmentioning

confidence: 99%

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Atzeni

Bacciu

Mazzei

et al. 2022

Sensors

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“…Finally, a study carried out by (Georgievska et al, 2019) determines the prevention and warning of overcrowded events. They focus on anonymous, non-participant, and indoor estimation regarding the location of smartphones.…”

Section: Related Workmentioning

confidence: 99%

Occupancy Estimation at Bus Stops Through Wi-Fi Connectivity Assessment – A Study in Guayaquil City

Cheme

Martinez

Durango

et al. 2022

Transport and Telecommunication Journal

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Guayaquil is one of the most congested cities on the American continent. Several users cannot access on-time transport service according to the demand requested at bus stops. This work presents a simulation of the Wi-Fi technology to determine the number of users present at a bus stop. The objective is to know an estimation of them in different time intervals throughout the day, which could be useful for several actions, e.g., to set up a better distribution of transportation schedules or to place the bus stops in more demanding sites by users. The methodology included simulation scenarios for user counting at the bus stops. The results depicted that Wi-Fi technology performed suitable even in high-level attenuation scenarios.

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“…Highly accurate, optical methods are generally limited in range by the visual cone of the individual sensors. Note that, at the price of a substantial accuracy loss, Bluetoothbased [18,19] or Wi-Fi-based [20,21] tracking enable larger spatial coverage per sensor.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking High-Fidelity Pedestrian Tracking Systems for Research, Real-Time Monitoring and Crowd Control

et al. 2022

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High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research allowing to quantify statistics of relevant observables including walking velocities, mutual distances and body orientations. As this technology advances, it is becoming increasingly useful also in society. In fact, continued urbanization is overwhelming existing pedestrian infrastructures such as transportation hubs and stations, generating an urgent need for real-time highly-accurate usage data, aiming both at flow monitoring and dynamics understanding. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. This is not only necessary to guarantee data quality, but also to identify systematic errors. Currently, there is no established policy in this context. In this contribution, we present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques. The suite is technology-independent and it is applicable to academic and commercial pedestrian tracking systems, operating both in lab environments and real-life conditions. The benchmark suite consists of 5 tests addressing specific aspects of pedestrian tracking quality, including accurate line-based crowd flux estimation, local density estimation, individual position detection and trajectory accuracy. The output of the tests are quality factors expressed as single numbers. We provide the benchmark results for two tracking systems, both operating in real-life, one commercial, and the other based on overhead depth-maps developed at TU Eindhoven, within the Crowdflow topical group. We discuss the results on the basis of the quality factors and report on the typical sensor and algorithmic performance. This enables us to highlight the current state-of-the-art, its limitations and provide installation recommendations, with specific attention to multi-sensor setups and data stitching.

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Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

Cited by 18 publications

References 44 publications

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Occupancy Estimation at Bus Stops Through Wi-Fi Connectivity Assessment – A Study in Guayaquil City

Benchmarking High-Fidelity Pedestrian Tracking Systems for Research, Real-Time Monitoring and Crowd Control

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