skip to main content
10.1145/3321408.3321417acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesacm-turcConference Proceedingsconference-collections
research-article

RSWI: a rescue system with wifi sensing and image recognition

Published: 17 May 2019 Publication History

Abstract

Rescue robots play an important role in the disaster, which are often used to search trapped people with the help of image technology. However, image-based robots have some limitations such as high energy consumption. To address this issue, this paper introduces a rescue system combining WiFi sensing and image recognition with two intelligent robots, which is called RSWI. This rescue system consists of two module: WiFi-based module and image-based module. WiFi-based module can fast detect the trapped people based on channel state information with a low energy consumption. Once the WiFi-based module detects someone in target area, image-based module will open immediately and execute the visibility calculation method which can determine whether the current environment is suitable for using image recognition technology. If the environment permits, this system will use image recognition technology based on Faster R-CNN to confirm someone existing information. Finally, we evaluate our design in indoor and outdoor environment. The results show that this rescue system is characterized by low energy consumption, reduction of false alarm rate, and adaptation to many scenes.

References

[1]
Abdulla Amin Aburomman and Mamun Bin Ibne Reaz. 2016. A novel SVM-kNN-PSO ensemble method for intrusion detection system. Applied Soft Computing 38, C (2016), 360--372.
[2]
Kamran Ali, Alex Xiao Liu, Wei Wang, and Muhammad Shahzad. 2015. Keystroke Recognition Using WiFi Signals. In International Conference on Mobile Computing & Networking. 90--102.
[3]
A. Carbone, A. Finzi, A. Orlandini, and F. Pirri. 2005. Augmenting situation awareness via model-based control in rescue robots. In Ieee/rsj International Conference on Intelligent Robots and Systems. 3699--3705.
[4]
Ping Cheng Chen, Chung Long Pan, J. D Huang, and S. H Hong. 2015. Sensorless Sensing with WiFi. Tsinghua Science & Technology (2015), 1--6.
[5]
Shifei Ding, Xingyu Zhao, Jian Zhang, Xiekai Zhang, and Yu Xue. 2017. A review on multi-class TWSVM. Artificial Intelligence Review 2 (2017), 1--27.
[6]
Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. 2011. Predictable 802.11 packet delivery from wireless channel measurements. In ACM SIGCOMM 2010 Conference. 159--170.
[7]
Taesun Kim and Chulhun Seo. 2000. A novel photonic bandgap structure for low-pass filter of wide stopband. IEEE Microwave & Guided Wave Letters 10, 1 (2000), 13--15.
[8]
Liyuan Li, J. K. E Hoe, Shuicheng Yan, and Xinguo Yu. 2009. ML-fusion based multi-model human detection and tracking for robust human-robot interfaces. In The Workshop on Applications of Computer Vision. 1--8.
[9]
Zhongmin Pei and Zhidong Deng. 2008. A distributed location algorithm for underground miners based on rescue robot and coal-mining wireless sensor networks. In Robotics, Automation and Mechatronics, 2008 IEEE Conference on. 848--888.
[10]
Kun Qian, Chenshu Wu, Zheng Yang, Yunhao Liu, and Zimu Zhou. 2015. PADS: Passive detection of moving targets with dynamic speed using PHY layer information. In IEEE International Conference on Parallel and Distributed Systems. 1--8.
[11]
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: towards real-time object detection with region proposal networks. In International Conference on Neural Information Processing Systems. 91--99.
[12]
Wanfeng Shang, Xiangang Cao, Hongwei Ma, Hailong Zang, and Pengkang Wei. 2015. Kinect-Based Vision System of Mine Rescue Robot for Low Illuminous Environment. Journal of Sensors, 2016, (2015-11-10) 2016, 4 (2015), 1--9.
[13]
Yuxi Wang, Kaishun Wu, and Lionel M. Ni. 2017. WiFall: Device-Free Fall Detection by Wireless Networks. IEEE Transactions on Mobile Computing 16, 2 (2017), 581--594.
[14]
Chenshu Wu, Zheng Yang, Zimu Zhou, Xuefeng Liu, Yunhao Liu, and Jiannong Cao. 2015. Non-Invasive Detection of Moving and Stationary Human With WiFi. IEEE Journal on Selected Areas in Communications 33, 11 (2015), 2329--2342.
[15]
Dan Wu, Daqing Zhang, Chenren Xu, Yasha Wang, and Hao Wang. 2016. WiDir:walking direction estimation using wireless signals. In ACM International Joint Conference on Pervasive and Ubiquitous Computing. 351--362.
[16]
Xu Yang, Pengpeng Chen, Shouwan Gao, and Qiang Niu. 2018. CSI-based low-duty-cycle wireless multimedia sensor network for security monitoring. Electronics Letters 54, 5 (2018), 323--324.
[17]
Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, and Yunhao Liu. 2014. Omnidirectional Coverage for Device-Free Passive Human Detection. IEEE Transactions on Parallel & Distributed Systems 25, 7 (2014), 1819--1829.

Cited By

View all
  • (2023)WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World SystemsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.320914425:1(46-76)Online publication date: Sep-2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China
May 2019
963 pages
ISBN:9781450371582
DOI:10.1145/3321408
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. channel state information
  2. image recognition
  3. rescue robots
  4. wifi sensing

Qualifiers

  • Research-article

Funding Sources

  • Youth Foundation of Heilongjiang Province
  • National Natural Science Foundation of China

Conference

ACM TURC 2019

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World SystemsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.320914425:1(46-76)Online publication date: Sep-2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media