RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI DOI Creative Commons

Xiaoping Tian,

Song Wu, Xiaoyan Zhang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 4952 - 4952

Published: July 31, 2024

Wireless sensing is a crucial technology for building smart cities, playing vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless enables formulation of more scientific decisions. The dynamic events significant branch sensing. Sensing specific times durations challenging problem due to event information concealed within static environments. To effectively sense relevant occurrence, we propose method based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows statistical methods process original RSSI time series, amplifying differences between Subsequently, z-score normalization employed enhance comparability effects different events. Furthermore, setting adaptive threshold, occurrence sensed marked series. In this study, performance was tested indoor corridors outdoor urban road events, including walking, running, cycling, driving, conducted. experimental results demonstrate that can accurately marking duration with millisecond-level precision. Moreover, exhibits robust both

Language: Английский

Thermal insulation performance of composite materials using industrial hemp straws DOI

Yang Qingfeng,

Hong Zhao,

Li Mingdong

et al.

Heat and Mass Transfer, Journal Year: 2024, Volume and Issue: 61(1)

Published: Dec. 5, 2024

Language: Английский

Citations

0
RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI DOI Creative Commons

Xiaoping Tian,

Song Wu, Xiaoyan Zhang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 4952 - 4952

Published: July 31, 2024

Wireless sensing is a crucial technology for building smart cities, playing vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless enables formulation of more scientific decisions. The dynamic events significant branch sensing. Sensing specific times durations challenging problem due to event information concealed within static environments. To effectively sense relevant occurrence, we propose method based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows statistical methods process original RSSI time series, amplifying differences between Subsequently, z-score normalization employed enhance comparability effects different events. Furthermore, setting adaptive threshold, occurrence sensed marked series. In this study, performance was tested indoor corridors outdoor urban road events, including walking, running, cycling, driving, conducted. experimental results demonstrate that can accurately marking duration with millisecond-level precision. Moreover, exhibits robust both

Language: Английский

Citations

0