Network Traffic Classification Using WiFi Sensing

Li, Junye; Mishra, Deepak; Seneviratne, Aruna

doi:10.1007/978-3-030-68110-4_3

Cited by 3 publications

(3 citation statements)

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“…These signal transformations are dependent on the signal length, which in turn is dependent on the underlying traffic type. This model, in addition to results from prior work [42], lead us to believe that the measured CSI will have some dependence on the underlying traffic type. We hypothesize that this will impede accurate sensing outcomes if those traffic types were not accounted for during the training phase.…”

Section: Variation In Csi With Packetmentioning

confidence: 73%

“…We utilise the Probability Mass Function (PMF) to perform qualitative analysis of the CSI amplitudes, which was first adopted in [42]. It is a metric which demonstrates the distribution of CSI amplitudes for a given OFDM subcarrier, and can be expressed as:…”

Section: Probability Mass Function Of Csi Amplitudementioning

confidence: 99%

“…Firstly, the non-uniformity of the transmission characteristics for these media types creates inequality in the attainable CSI sampling rate for Rx. Furthermore, It has been shown in prior work that the underlying CSI varies when harvested from varying traffic types [42]. Here, we recall that the operating principle of Wi-Fi sensing is to correlate changes in CSI with changes in the physical channel.…”

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confidence: 99%

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Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Sharma,

Li,

Mishra

et al. 2024

Energies

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Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

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Section: Variation In Csi With Packetmentioning

confidence: 73%

Section: Probability Mass Function Of Csi Amplitudementioning

confidence: 99%

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confidence: 99%

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