SNOW: Detecting Shopping Groups Using WiFi

Shen, Jiaxing; Cao, Jiannong; Li, Xuefeng; Tang, Shaojie

doi:10.1109/jiot.2018.2839525

Cited by 28 publications

(12 citation statements)

References 20 publications

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“…Out of these papers that use stationary Wi-Fi sensors for their tracking, some mainly collect data for specific events rather than for daily human movement [25]- [28]; some are directed toward specific industries' applications such as investigating the movements within a hospital [29], within a university [30], within a shopping mall [31], [32], within a public district with shopping malls and office buildings [33], and among popular tourist locations on a set of islands [14]; and a few cover large enough parts of cities such as Manhattan and Copenhagen to identify the daily movement of commuters between work and home [15], [16], [22]. Although the scopes of these papers vary widely, they have certain similarities in the analysis of the data.…”

Section: A Tracking Methodsmentioning

confidence: 99%

Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data

Koh,

Zhou,

Lau

et al. 2020

Preprint

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Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this paper is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely k-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through such a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location, and we present the results obtained by applying our proposed approach on a real-world dataset collected over five months.

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Section: A Tracking Methodsmentioning

confidence: 99%

Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data

Koh,

Zhou,

Lau

et al. 2020

Preprint

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“…While human-centric sensing has found applications especially in health care [40,[43][44][45], crowd-centric sensing has an important range of potential applications in public safety, mobility planning and marketing [6,[46][47][48][49][50].…”

Section: Human-centric Vs Crowd-centric Sensingmentioning

confidence: 99%

Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

et al. 2021

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Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links.

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“…Jiaxing Shen [8] showed his grouping concept in his paper that allows multiple Wi-fi to create a group. In our case we can use this concept to connect to nearest Wi-fi and use it as a medium to send the data or information over a longer distance without any interruptions or breaks.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Power Generation and Conversion for Estimation of the Location

Malhotra¹,

Singh²

2018

IJEEE

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The paper focuses on the practicality and application of mechanical structure being used for the purpose of generating a small voltage to act as an alternative source of power and send information in case of mobile electronics and wearable electronics. The present scenario of mobile phones does not allow the mechanical structure to suit the needs and design of mobile phones. To compensate this problem mechanical design and structure can be used to generate a small amount of voltage and power microelectronics inside a mobile phone. Working with the design and weight shifting property of mechanical shafts we can provide a setup to generate a small voltage of electricity and send the required information of the mobile phone, even when the device is offline. The information maybe, location or sim card details so that a device can be tracked down even when it is switched off.

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SNOW: Detecting Shopping Groups Using WiFi

Cited by 28 publications

References 20 publications

Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data

Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data

Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

Intelligent Power Generation and Conversion for Estimation of the Location

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