Post-disaster Rescue in Collapsed Structures Using Wi-Fi Signals
1
Supervisor:
Dr. Guojun Wang, Pearl River Scholarship Distinguished
Professor
Director of Institute of Computer Networks,
Vice Dean of School of Computer Science and Educational
Software
Guangzhou University, Guangzhou
Muhammad Faizan Khan
(PhD Student)
Institute of Computer
Networks
Agenda:
2
Problem Background
Proposed Idea
Significance of Wi-Fi Signals
Approach to Solution
Solution Mechanism
Current Work
Future Work
Background:❖Our daily lives are becoming more reliant on large structure particularly
high-rise buildings, bridges, subways and others.
❖We often hear structural failure events around the world. A list can be
found at;
https://en.wikipedia.org/wiki/List_of_structural_failures_and_collapses
❖Hundreds of people loose their lives in these collapses.
❖Developing countries face these problems more than developed ones .
❖Its because of no such proper mechanism for hazards forecasting
although there are structural monitoring techniques available now. But
developing countries seldom benefit from those.3
Background Cont’d:
4
Margalla Tower Islamabad, Pakistan, 2005 Earthquake
Image Courtesy:
https://tribune.com.pk/story/969471/why-did-
margalla-towers-collapse-no-answer-yet/
Garment Building Collapse, Bangladesh, 2013
Image Courtesy:
https://en.wikipedia.org/wiki/2013_Savar_building
_collapse
Background Cont’d:
5
Thane Building Collapse, India,2013
Image Courtesy:
http://indianexpress.com/about/thane-
building-collapse/
Building Collapse, Kenya, 2016
Image Courtesy:
http://www.bbc.com/news/world-africa-
36178246
China Mines, 2010
Image Courtesy:
http://www.reuters.com/article/us-china-
disaster-mine-idUSKBN13S08W
Causes:1. Weather (Heavy rains, Tsunami, Katrina etc.)
2. Poor Building Structure
3. Earthquakes
4. Wars
5. Terrorist Activities
6
Image Courtesy:
http://www.fulldhamaal.com/wp-
content/uploads/2009/08/hotel-taiwan-06.jpg
Existing Solutions:❖EM, Doppler and UWB Radars [1][2][3][4][5][6][7]
✓Costly Solution
❖Cyber-Physical System exits [8][9], but;
✓ These are not much considering the rescue case
✓After collapse, sensors may fail
❖Robots[10]
❖Human Intensive Efforts which are quite risky
❖Military dogs
7
Bangladesh Rescue Efforts, 2013, Building
Collapse
Image Courtesy:
http://i2.cdn.turner.com/cnnnext/dam/assets/13
0425062801-04-bangladesh-building-collapse-
0425-horizontal-large-gallery.jpg
Proposed Idea:
“Effective use of Wi-Fi signals to save lives in post-disasterscenario”.✓Radar based techniques have provided a path way for radio signals
based post-disaster rescue.
✓The idea is to use echo reflected from humans under debris whileoperating at Wi-Fi frequency band.
8
Why Wi-Fi Signals?
1. License free band (ISM Bands), i.e., 5GHz, 2.4GHz and 900MHz
2. Availability
3. Cost Effective
4. RSSI (Received Signal Strength Indicator)
5. CSI(Channel State Information)
6. Can be used as sensors [11][12]
7. Can lead to device free communication
9
Wireless Signals as Sensors: Applications
A bird’s eye view of Wi-Fi radios as sensors is as follows:
1. Indoor localization & Navigation [13][14]
2. Human Computer Interaction [15][16]
3. Activity Monitoring [17][18]
4. Health Related Issues (Breathing, Sleeping) Monitoring [19][20][21]
5. Gesture/Emotion Recognition [22][23][24]
6. Precautionary Measures i-e Fall Detection [25][26]
7. Intrusion Detection & Security Issues [27][28]
8. Backscatter, LOS, etc. [29][30]
Can Wi-Fi Signal Detect Breathing?
11
Can Wi-Fi Signal Detect Breathing?
12
Novelty in Idea:
1. No prior work exits on the possible application of Wi-Fi signals
under debris
2. Minimal Cost
3. Saving the mankind
4. No risk at all for rescue workers
5. Efficiency in rescue
6. Pathway for ubiquitous solutions
13
Challenges:
1. Complex Collapsed Structure (Debris)
2. NLOS Communication
3. Multipath Fading and Shadowing
4. Higher Operating Frequency
Ref [31][32]
14
Image Courtesy:
Accepted Paper, “Wi-Fi Signal coverage distance
estimation in collapsed structures
Approach to Solution:
1. Creating small simulation models
2. Simulating these small models
3. Integrating to big model i.e., structure scenario
4. Simulation of whole structure model
5. Hardware Prototype step by step (Subject to time availability)
6. Deployment (If needed but subject to availability of time)
15
Solution Mechanism:
Wi-Fi Halow coverage
estimation in collapsed
structure
16
Simulating
possible frequency
bands in collapsed
structure
Echo estimation
Working with bit complex collapsed
structures
Increasing Signal Strength
Breathing model under debris
Breathing model into Wi-Fi echo
Identification of alive ones
Past & Current Work:1. Simulated Wi-Fi signal coverage distance in brick wall collapsed
structure (First paper). Has been accepted in IEEE ISPA 2017(CCF C).
2. Simulated Wi-Fi Halow Signal Coverage in brick+ concretecollapsed environments. Submitted paper in IEEE ICASSP 2018(CCF B) (Flagship Conference in Signal processing).
3. Working on increasing the strength of signal with shadowing +fading model.
4. Studying on proposing a modified patch antenna for lowerfrequency which can have better signal reception.
5. Planning to conduct site surveys in coordination with buildingdepartment of civil engineering from Guangzhou university.
17
Complex Structures & Attenuations:
18
Image Courtesy:
http://community.arubanetworks.com/t5/Community-Tribal-
Knowledge-Base/RF-Concepts-The-Basics/ta-p/25378 Image Courtesy:
http://ftp1.digi.com/support/images/XST-AN005a IndoorPathLoss.pdf
Why Low Frequency?1. Lower frequency means a longer wavelength, which means the
beam can diffract better.
2. Lower photon energy, which implies that, in general, the beamcannot excite atomic or molecular transitions as well.
3. The lower frequencies will still be stopped by metals, however,because the delocalized electrons in a metal are readily excitedby the waves.
4. BUT, a longer wavelength also means a longer antenna isrequired to generate the wave.
5. But a low frequency waves such as a radio waves cannot hassmaller bandwidths compared to the microwaves and hence itcannot carry as much information as a microwave can. Ref [33]
19
Mapping Theory to Practice:
20
Layered Approach to Debris
21
Problem Mathematics:1. Wi-Fi signal penetration in collapsed environments can be
realized by considering link budget equation
𝑃𝑟𝑥 = 𝑃𝑡𝑥 + 𝐺𝑡𝑥 + 𝐺𝑟𝑥 − 𝑃𝐿
Here PL is path loss, Gtx and Grx are transmission and
receiver antenna gains respectively and P represents power.
2. Now, Path Loss can be given as below:
𝑃𝐿 𝑑 𝑑𝐵 = 𝑃𝐿 𝑑0 𝑑𝐵 + 10 ∗ 𝛼 ∗ 𝑙𝑜𝑔10𝑑
𝑑0
Here, d is distance
22
Problem Mathematics Cont’d:3. Now, Incorporating fading, losses and attenuations;
𝑃𝐿 𝑑 𝑑𝐵 = 𝑃𝑇 − 𝑃𝑅 = 𝑃𝐿 𝑑0 𝑑𝐵 + 10 ∗ 𝛼 ∗ 𝑙𝑜𝑔10𝑑
𝑑0+ 𝑋𝑔
Xg represents collapsed structure losses
𝑋𝑔 = 𝑝 ∗ 𝐴𝐹 𝑏𝑟𝑖𝑐𝑘 + 𝑛 ∗ 𝐴𝐹 𝐶𝑜𝑛𝑐𝑟𝑒𝑡𝑒 + 𝐹𝑎𝐹
4. Minimum detectable signal can be given as:
𝑃𝑡𝑥 + 𝐺𝑡𝑥 + 𝐺𝑟𝑥 − 𝑃𝐿 ≥ 𝑀𝐷𝑆(𝑃𝑒)
✓If minimum detectable signal is above than pre-defined threshold, we can detect
the signal.
23
Simulation Parameters
24
Operating Frequency 900MHz
Transmission Power 30dBm, 10dBm
Antenna Gains 6dBi, 24dBi
Debris Type Brick, Concrete
Thickness of Brick 10.5”
Thickness of Concrete 8”
Attenuation of Brick 10.5” 7dB at 900MHz
Attenuation of Concrete 8” 23dB at 900MHz
Minimum Debris Layers 6 to 9
Maximum Debris Layers 12 to 17
Minimum Detectable Signal
Threshold
-90dBm
Fading Factor 25dB
Results (Less Layers):
25
Results (Higher Layers):
26
Analyzing the Results:
Worst Case Scenario:
✓With higher number of layers (12+5) (brick+ concrete)1. The signal coverage is in millimeter with 30dBm power
2. The signal coverage is 1/tenth of meter with 10dBm
Best Case Scenario:
✓With lower number of layers (6+2) (brick+ concrete)1. The signal coverage is half meter with 30dBm power
2. The signal coverage is 1.5m with 10dBm
27
Future Work:
How to increase coverage distance in collapsed structures?
1. Can create sub-regions of debris to map more devices
2. Can increase signal strength through mesh network
3. Can utilize alive sensors from cyber-physical systems
4. Can improve the antenna design
5. Can improve the directional patterns of antenna
6. Can remove clutter/noises
7. Can exploit Fresnel zone concepts for far-field communication
28
References:1) J. Li, L. Liu, Z. Zeng and F. Liu, "Advanced Signal Processing for Vital Sign Extraction With Applications in UWB
Radar Detection of Trapped Victims in Complex Environments," in IEEE Journal of Selected Topics in AppliedEarth Observations and Remote Sensing, vol. 7, no. 3, pp. 783-791, March 2014.
2) G. Grazzini, M. Pieraccini, F. Parrini, A. Spinetti, G. Macaluso, D. Dei, and C. Atzeni, “An ultra-wideband high-dynamic range gpr for detecting buried people after collapse of buildings,” in Proceedings of the XIII InternarionalConference on Ground Penetrating Radar, June 2010, pp. 1–6.
3) L. Crocco and V. Ferrara, “A review on ground penetrating radar technology for the detection of buried or trappedvictims,” in 2014 International Conference on Collaboration Technologies and Systems (CTS), May 2014, pp.535–540
4) R. M. Narayanan, “Earthquake survivor detection using life signals from radar microdoppler,” in Proceedings of the1st International Conference on Wireless Technologies for Humanitarian Relief, ser. ACWR ’11. New York, NY,USA: ACM, 2011, pp. 259–264. [Online]. Available: http://doi.acm.org/10.1145/2185216.2185288
5) A. DiCarlofelice, E. DiGiampaolo, M. Feliziani, and P. Tognolatti, “Experimental characterization of electromagneticpropagation under rubble of a historic town after disaster,” IEEE Transactions on Vehicular Technology, vol. 64,no. 6, pp. 2288–2296, June 2015
6) F. JalaliBidgoli, S. Moghadami, and S. Ardalan, “A compact portable microwave life-detection device for findingsurvivors,” IEEE Embedded Systems Letters, vol. 8, no. 1, pp. 10–13, March 2016.
7) Z. Li, H. Lv, Y. Zhang, G. Lu, S. Li, X. Jing, and J. Wang, “Detection of trapped survivors using 270/400 mhz dual-frequency ir-uwb radar based on time division multiplexing,” in 2014 IEEE Topical Conference on BiomedicalWireless Technologies, Networks, and Sensing Systems (BioWireleSS), Jan 2014, pp. 31–33.
29
References Cont’d:8) M. Z. A. Bhuiyan, J. Wu, G. Wang, Z. Chen, J. Chen, and T. Wang, “Quality-guaranteed event-sensitive data
collection and monitoring in vibration sensor networks,” IEEE Transactions on Industrial Informatics,vol. 13, no. 2, pp. 572–583, April 2017.
9) J. Wang, Z. Cheng, L. Jing, and T. Yoshida, “Design of a 3d localization method for searching survivors afteran earthquake based on wsn,” in 2011 3rd International Conference on Awareness Science andTechnology (iCAST), Sept 2011, pp. 221–226.
10) https://www.cs.cmu.edu/news/snake-robot-searches-mexico-city-quake-survivors
11) Z. Zhou, C. Wu, Z. Yang and Y. Liu, "Sensorless sensing with WiFi," in Tsinghua Science and Technology,vol. 20, no. 1, pp. 1-6, Feb. 2015.
12) S. Savazzi, S. Sigg, M. Nicoli, V. Rampa, S. Kianoush and U. Spagnolini, "Device-Free Radio Vision forAssisted Living: Leveraging wireless channel quality information for human sensing," in IEEE SignalProcessing Magazine, vol. 33, no. 2, pp. 45-58, March 2016.
13) Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, and Sachin Katti. 2015. SpotFi: Decimeter LevelLocalization Using WiFi. In Proceedings of the 2015 ACM Conference on Special Interest Group on DataCommunication (SIGCOMM '15). ACM, New York, NY, USA, 269-282
14) X. Wang, L. Gao, S. Mao and S. Pandey, "CSI-Based Fingerprinting for Indoor Localization: A DeepLearning Approach," in IEEE Transactions on Vehicular Technology, vol. 66, no. 1, pp. 763-776, Jan.2017.
30
References Cont’d:15) Kamran Ali, Alex X. Liu, Wei Wang, and Muhammad Shahzad. 2015. Keystroke Recognition Using WiFi Signals. In
Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom '15).
ACM, New York, NY, USA, 90-102.
16) Jue Wang, Deepak Vasisht, and Dina Katabi. 2014. RF-IDraw: virtual touch screen in the air using RF signals. In
Proceedings of the 2014 ACM conference on SIGCOMM (SIGCOMM '14). ACM, New York, NY, USA, 235-246.
17) J. Wang; X. Zhang; Q. Gao; H. Yue; H. Wang, "Device-free Wireless Localization and Activity Recognition: A Deep
Learning Approach," in IEEE Transactions on Vehicular Technology , vol.PP, no.99, pp.1-1
18) Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. 2015. Understanding and Modeling of WiFi
Signal Based Human Activity Recognition. In Proceedings of the 21st Annual International Conference on Mobile
Computing and Networking (MobiCom '15). ACM, New York, NY, USA, 65-76.
19) Hao Wang, Daqing Zhang, Junyi Ma, Yasha Wang, Yuxiang Wang, Dan Wu, Tao Gu, and Bing Xie. 2016. Human
respiration detection with commodity wifi devices: do user location and body orientation matter?. In Proceedings of the
2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16). ACM, New York,
NY, USA, 25-36.
20) Junyi Ma, Yuxiang Wang, Hao Wang, Yasha Wang, and Daqing Zhang. 2016. When can we detect human respiration
with commodity wifi devices?. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and
Ubiquitous Computing: Adjunct (UbiComp '16). ACM, New York, NY, USA, 325-328.
31
References Cont’d:21. X. Liu, J. Cao, S. Tang, J. Wen and P. Guo, "Contactless Respiration Monitoring Via Off-the-Shelf WiFi Devices," in
IEEE Transactions on Mobile Computing, vol. 15, no. 10, pp. 2466-2479, Oct. 1 2016.
22. Ouyang Zhang and Kannan Srinivasan. 2016. Mudra: User-friendly Fine-grained Gesture Recognition using WiFiSignals. In Proceedings of the 12th International on Conference on emerging Networking Experiments andTechnologies (CoNEXT '16). ACM, New York, NY, USA, 83-96.
23. Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. 2015. Gesture Recognition UsingWireless Signals. GetMobile: Mobile Comp. and Comm. 18, 4 (January 2015), 15-18.
24. Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. 2015. Gesture Recognition Using Wireless Signals.GetMobile: Mobile Comp. and Comm. 18, 4 (January 2015), 15-18.
25. H. Wang, D. Zhang, Y. Wang, J. Ma, Y. Wang and S. Li, "RT-Fall: A Real-Time and Contactless Fall Detection Systemwith Commodity WiFi Devices," in IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 511-526, Feb. 12017.
26. S. Kianoush, S. Savazzi, F. Vicentini, V. Rampa and M. Giussani, "Device-Free RF Human Body Fall Detection andLocalization in Industrial Workplaces," in IEEE Internet of Things Journal, vol. 4, no. 2, pp. 351-362, April 2017.
27. Mengyuan Li, Yan Meng, Junyi Liu, Haojin Zhu, Xiaohui Liang, Yao Liu, and Na Ruan. 2016. When CSI Meets PublicWiFi: Inferring Your Mobile Phone Password via WiFi Signals. In Proceedings of the 2016 ACM SIGSAC Conferenceon Computer and Communications Security (CCS '16). ACM, New York, NY, USA, 1068-1079.
32
References Cont’d:28. Linsong Cheng and Jiliang Wang. 2016. How can I guard my AP?: non-intrusive user identification
for mobile devices using WiFi signals. In Proceedings of the 17th ACM International Symposium onMobile Ad Hoc Networking and Computing (MobiHoc '16). ACM, New York, NY, USA, 91-100.
29. Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, and Sachin Katti. 2016. HitchHike: PracticalBackscatter Using Commodity WiFi. In Proceedings of the 14th ACM Conference on EmbeddedNetwork Sensor Systems CD-ROM (SenSys '16). ACM, New York, NY, USA, 259-271.
30. C. Wu, Z. Yang, Z. Zhou, K. Qian, Y. Liu and M. Liu, "PhaseU: Real-time LOS identification withWiFi," 2015 IEEE Conference on Computer Communications (INFOCOM), Kowloon, 2015, pp.2038-2046.
31. S. Y. Seidel and T. S. Rappaport, "914 MHz path loss prediction models for indoor wirelesscommunications in multifloored buildings," in IEEE Transactions on Antennas and Propagation,vol. 40, no. 2, pp. 207-217, Feb 1992.
32. C. Phillips, D. Sicker, and D. Grunwald, “A survey of wireless path loss prediction and coveragemapping methods,” IEEE Communications Surveys Tutorials, vol. 15, no. 1, pp. 255–270, First2013.
33. https://www.quora.com/Why-is-low-frequency-transmitted-longer-than-higher-frequency-Why-are-microwaves-used-and-not-radio-waves-in-cellular-phones
33
34
Q&A
35
Top Related