By analyzing thermal adaptive behavior(TAB),we can access the occupant’s thermal comfort in real time and control the heating,ventilation,and air conditioning(HVAC)system accordingly to reduce energy consumption in b...By analyzing thermal adaptive behavior(TAB),we can access the occupant’s thermal comfort in real time and control the heating,ventilation,and air conditioning(HVAC)system accordingly to reduce energy consumption in buildings.Most existing methods are based on wearable devices or cameras to collect occupant behavioral information.Although these methods can effectively identify occupant behavior,they have the problem of violating user privacy.With the development of wireless technologies,human activity recognition using WiFi has the advantages of being non-invasive,privacy-friendly,and light-independent.Therefore,non-invasive TAB recognition based on WiFi technology holds great promise in human thermal comfort.However,existing research on TAB recognition based on WiFi technology lacks comprehensive and consistent conclusions.Thus,in this paper,we have surveyed the literature in recent years to guide in this area.In addition,we present the challenges and future perspectives faced by existing WiFi-based TAB technologies,e.g.,developing high-quality WiFi sensing datasets to advance the field of human thermal comfort.We hope this review will guide researchers in recognizing the great promise of WiFi sensing applications for TAB recognition in smart buildings.展开更多
WiFi sensing is critical to many applications,such as localization,human activity recognition,and contact-less health monitoring.With metaverse and ubiquitous sensing advances,WiFi sensing becomes increasingly imperat...WiFi sensing is critical to many applications,such as localization,human activity recognition,and contact-less health monitoring.With metaverse and ubiquitous sensing advances,WiFi sensing becomes increasingly imperative.However,as shown in this paper,WiFi sensing data leaks users’private attributes(e.g.,height,weight,and gender),violating increasingly stricter privacy protection laws and regulations.To demonstrate the leakage of private attributes in WiFi sensing,we investigate two public WiFi sensing datasets and apply a deep learning model to recognize users’private attributes.Our experimental results clearly show that our model can identify users’private attributes in WiFi sensing data collected by general WiFi applications,with almost 100%accuracy for gender inference,less than 4 cm error for height inference,and about 4 kg error for weight inference,respectively.Our finding calls for research efforts to preserve data privacy while enabling WiFi sensing-based applications.展开更多
With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn research...With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn researchers’great attention.Among different sensing medias,WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost.Based on different basic principles,researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition,motion tracking,indoor localization,health monitoring,and the like.To enable readers to get a comprehensive understanding of ubiquitous wireless sensing,we conduct a survey of existing work to introduce their underlying principles,proposed technologies,and practical applications.Besides we also discuss some open issues of this research area.Our survey reals that as a promising research direction,WiFi and acoustic sensing technologies can bring about fancy applications,but still have limitations in hardware restriction,robustness,and applicability.展开更多
Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing wit...Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world＇s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.展开更多
With the development of the Internet of Things(IoT)and the popularization of commercial WiFi,researchers have begun to use commercial WiFi for human activity recognition in the past decade.However,cross-scene activity...With the development of the Internet of Things(IoT)and the popularization of commercial WiFi,researchers have begun to use commercial WiFi for human activity recognition in the past decade.However,cross-scene activity recognition is still difficult due to the different distribution of samples in different scenes.To solve this problem,we try to build a cross-scene activity recognition system based on commercial WiFi.Firstly,we use commercial WiFi devices to collect channel state information(CSI)data and use the Bi-directional long short-term memory(BiLSTM)network to train the activity recognition model.Then,we use the transfer learning mechanism to transfer the model to fit another scene.Finally,we conduct experiments to evaluate the performance of our system,and the experimental results verify the accuracy and robustness of our proposed system.For the source scene,the accuracy of the model trained from scratch can achieve over 90%.After transfer learning,the accuracy of cross-scene activity recognition in the target scene can still reach 90%.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62001126 and Grant 52378092in part by the Guangdong Basic and Applied Basic Research Foundation(Project No.2021A1515110455).
文摘By analyzing thermal adaptive behavior(TAB),we can access the occupant’s thermal comfort in real time and control the heating,ventilation,and air conditioning(HVAC)system accordingly to reduce energy consumption in buildings.Most existing methods are based on wearable devices or cameras to collect occupant behavioral information.Although these methods can effectively identify occupant behavior,they have the problem of violating user privacy.With the development of wireless technologies,human activity recognition using WiFi has the advantages of being non-invasive,privacy-friendly,and light-independent.Therefore,non-invasive TAB recognition based on WiFi technology holds great promise in human thermal comfort.However,existing research on TAB recognition based on WiFi technology lacks comprehensive and consistent conclusions.Thus,in this paper,we have surveyed the literature in recent years to guide in this area.In addition,we present the challenges and future perspectives faced by existing WiFi-based TAB technologies,e.g.,developing high-quality WiFi sensing datasets to advance the field of human thermal comfort.We hope this review will guide researchers in recognizing the great promise of WiFi sensing applications for TAB recognition in smart buildings.
基金supported by the National Natural Science Foundation of China(62302265,U23A20332)Shandong Provin-cial Natural Science Foundation,China(ZR2023QF172).
文摘WiFi sensing is critical to many applications,such as localization,human activity recognition,and contact-less health monitoring.With metaverse and ubiquitous sensing advances,WiFi sensing becomes increasingly imperative.However,as shown in this paper,WiFi sensing data leaks users’private attributes(e.g.,height,weight,and gender),violating increasingly stricter privacy protection laws and regulations.To demonstrate the leakage of private attributes in WiFi sensing,we investigate two public WiFi sensing datasets and apply a deep learning model to recognize users’private attributes.Our experimental results clearly show that our model can identify users’private attributes in WiFi sensing data collected by general WiFi applications,with almost 100%accuracy for gender inference,less than 4 cm error for height inference,and about 4 kg error for weight inference,respectively.Our finding calls for research efforts to preserve data privacy while enabling WiFi sensing-based applications.
基金supported by the National Natural Science Foundation of China under Grant Nos.62172286 and U2001207the Natural Science Foundation of Guangdong Province of China under Grant Nos.2022A1515011509 and 2017A030312008the Guangdong"Pearl River Talent Recruitment Program"under Grant No.2019ZT08X603.
文摘With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn researchers’great attention.Among different sensing medias,WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost.Based on different basic principles,researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition,motion tracking,indoor localization,health monitoring,and the like.To enable readers to get a comprehensive understanding of ubiquitous wireless sensing,we conduct a survey of existing work to introduce their underlying principles,proposed technologies,and practical applications.Besides we also discuss some open issues of this research area.Our survey reals that as a promising research direction,WiFi and acoustic sensing technologies can bring about fancy applications,but still have limitations in hardware restriction,robustness,and applicability.
文摘Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world＇s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.
基金This work was supported in part by the Key Program of the National Natural Science Foundation of China(Grant Nos.61932013 and 61803212)The National Natural Science Foundation of China(Grant Nos.61873131 and 61803212)+2 种基金Natural Science Foundation of Jiangsu Province(BK20180744)China Postdoctoral Science Foundation(2019M651920 and 2020T130315)The Research Foundation of Jiangsu for“333 High Level Talents Training Project”(BRA2020065).
文摘With the development of the Internet of Things(IoT)and the popularization of commercial WiFi,researchers have begun to use commercial WiFi for human activity recognition in the past decade.However,cross-scene activity recognition is still difficult due to the different distribution of samples in different scenes.To solve this problem,we try to build a cross-scene activity recognition system based on commercial WiFi.Firstly,we use commercial WiFi devices to collect channel state information(CSI)data and use the Bi-directional long short-term memory(BiLSTM)network to train the activity recognition model.Then,we use the transfer learning mechanism to transfer the model to fit another scene.Finally,we conduct experiments to evaluate the performance of our system,and the experimental results verify the accuracy and robustness of our proposed system.For the source scene,the accuracy of the model trained from scratch can achieve over 90%.After transfer learning,the accuracy of cross-scene activity recognition in the target scene can still reach 90%.
