1. Device-Free Localization Ossi Kaltiokallio Department of Automation and Systems Technology Aalto University School of Science and Technology www.autsys.tkk.fi 1

2. Overview 2 Device-free localization (DFL) based on distributed processing of RSSI Objective What enables the technology? How a person can be detected and localized? DFL application briefly Experimetal results and DEMO(s)

3. Objective 3 Device-free localization (DFL) system Re-deployable, remotely configurable, and easy to use Operates in real-time Target position is estimated with minimal latency Distiributed processing of RSSI Communication overhead is minimized Limited resources of the node Enables large scale WSNs Energy efficiency Radio management

4. Electromagnetic spectrum 4

5. 5 What enables the technology?

6. 6 Sources of RSSI variability multipath fading and shadowing, antenna differences, node orientation, surrounding environment, distance, transmission power, etc.

7. Sources of Information (1/3) 7 Fading, deviation of the attenuation Fading may either be due to multipath propagation or shadowing Presence of reflectors around communication link creates multiple paths that TX can traverse result  RX ”hears” multiple copies of the transmitted signal, each propagated via different path Each signal experiences difference in attenuation, delay and phase shift Result can be either destructive or constructing

8. Sources of Information (2/3) 8 160 183 135 210 RSSI measurements of one link...

9. Sources of Information (3/3) 9 160 183 135 210... and the link next by, RSSI measurements are quite different

10. Positioning via RSSI… 10 Single link The distributed algorithm is capable of detecting LoS crossings Accuracy  very limited Possible false alerts What if accuracy is required?  the key lies in nodes number Multiple links Many nodes simultaneously can detect the person Data aggregation  coordinates of the person can be estimated if nodes positions are known a priori False alerts can be filtered out

11. Master’s thesis approach (1/2) 11

12. Master’s thesis approach (2/2) 12

13. Application in a nutshell 13 DFL System WSN Matlab Communication Distributed algorithm Positioning Tracking SITUATION AWERNESS SINK 13

14. Results of Master’s thesis 14

15. Problems and Solutions 15 Algorithm designed to only detect LoS crossings that are caused by shadowing Increse sensitivity  multipath fading events also detected LoS crossings visualized with a line The person affects the RSSI also elsewhere  model alerts with an ellipsoid Transmission interval (16 ms) Operations of the nodes optimized (10 ms)  more measurements, better results

16. Alert model 16

17. 17 … and the story continues Each alert is visualized with an ellipsoid Ellipsoid’s tend to cluster around the actual position Why not just send all RSSI measurements to the sink? Tracking accuracy enhanced with a Kalman filter

18. Experimental results (1/3) 18

19. Experimental results (2/3) 19 Test 1aTest 2cTest 7aTest 3a Node interval [m]11.5 2 Monitored area [m 2 ]1636 36 (obstructed) 64 RMSE0.190.200.220.23 Min RMSE0.17 0.200.19 Max RMSE0.220.210.230.26 Max error0.631.071.191.14 Alerts [%]16.4314.5616.3511.69

20. Demo Processing the RSSI in a distributed fashion reduces communication overhead –Alerts account only for around 10-17 % of the total number of packets (area always occupied) 20 Radio management: –Radio enabled for TX: 2.30 ms, RX: 4.05 ms –An additive increment in network lifetime Experimental results (3/3)