Fire Detection Using Commodity WiFi Devices

Li, Junye; Sharma, Aryan; Mishra, Deepak; Seneviratne, Aruna

doi:10.1109/globecom46510.2021.9685183

Cited by 12 publications

(1 citation statement)

References 17 publications

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“…For an interesting example, detecting motion in a room or detecting when a potential user approaches a Wi-Fi-enabled device are important applications of Wi-Fi sensing, and of interest in areas such as system wake up and environment monitoring [21]. The suitability of using Wi-Fi to sense fire, another potential application, was demonstrated in [22], which proved that there is a direct relationship between flame and channel state information (CSI) signatures. Indoor localization is also a significant application area of Wi-Fi technology.…”

Section: Related Workmentioning

confidence: 99%

Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization

Zheng

Zhang

2022

Sensors

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With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application of the standard particle swarm optimization algorithm to Wi-Fi fingerprint-based indoor localization, wherein a new two-panel fingerprint homogeneity model is adopted to characterize fingerprint similarity to achieve better performance. In addition, the performance of the localization method is experimentally verified. The proposed localization method outperforms conventional algorithms, with improvements in the localization accuracy of 15.32%, 15.91%, 32.38%, and 36.64%, compared to those of KNN, SVM, LR, and RF, respectively.

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Section: Related Workmentioning

confidence: 99%