Revealing the hidden lives of underground animals with - PowerPoint PPT Presentation

Revealing the hidden lives of underground animals with Magneto-Inductive tracking Andrew Markham Stephen Ellwood Niki Trigoni David Macdonald Computing Laboratory Wildlife Conservation Research Unit University of Oxford University of Oxford 5 November 2010: ACM SenSys

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary 1 Motivation 2 Steps in MI localization 3 System Design 4 Results 5 Limitations and Lessons learnt 6 Summary Andrew Markham SenSys 2010 2/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Current tracking approaches Tracking animals above ground is relatively well researched ZebraNet - GPS-WSN Virtual Fencing - GPS-WSN TurtleNet - GPS-WSN WildSensing - RFID-WSN ... Andrew Markham SenSys 2010 3/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary What about burrowing animals? Example burrowing species: badgers Nocturnal medium sized carnivores (8 kg) Live in extensive (20 m x 10 m) setts Tunnels between 1 and 3 m deep typically 1 to 20 badgers in a sett Andrew Markham SenSys 2010 4/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary What technology can be used? How can we track animals underground ? Radio + Soil = no signal Digging out a den destroys it Ground penetrating radar (GPR) does not indicate positions of animals Andrew Markham SenSys 2010 5/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary What is not affected by soil? Magnetic fields are unaffected by: Soil Water Air Vegetation Magnetic fields are affected by metal, but luckily, there is not much of that in ancient woodlands! Andrew Markham SenSys 2010 6/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Steps in MI localization 1 Generation of magnetic fields 2 Sensing and compression 3 Information transfer 4 Localization Andrew Markham SenSys 2010 7/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Steps in MI localization 1 Generation of magnetic fields 2 Sensing and compression 3 Information transfer 4 Localization Compare with well known RF RSSI based techniques Andrew Markham SenSys 2010 7/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Step 1: Generation of magnetic fields Andrew Markham SenSys 2010 8/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Step 1: Generation of magnetic fields Andrew Markham SenSys 2010 9/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Equations Spatial distribution of field is more complex for MI RSSI MI 4 ( − 1) a d a µ 0 I C a � B z ( x , y , z ) = [ − ] r a [ r a + ( − 1) a +1 c a ] 4 π r a [ r a + d a ] a =1 4 ( − 1) a +1 z µ 0 I � B x ( x , y , z ) = [ ] 4 π r a [ r a + d a ] RSSI ( x , y , z ) = α 0 + α 1 log( r ) a =1 4 ( − 1) a +1 z µ 0 I � B y ( x , y , z ) = [ ] r a [ r a + ( − 1) a +1 c a ] 4 π a =1 � B x 2 + B y 2 + B z 2 | B | = Andrew Markham SenSys 2010 10/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Equations Spatial distribution of field is more complex for MI Can’t be treated as point source RSSI MI 4 ( − 1) a d a µ 0 I C a � B z ( x , y , z ) = [ − ] r a [ r a + ( − 1) a +1 c a ] 4 π r a [ r a + d a ] a =1 4 ( − 1) a +1 z µ 0 I � B x ( x , y , z ) = [ ] 4 π r a [ r a + d a ] RSSI ( x , y , z ) = α 0 + α 1 log( r ) a =1 4 ( − 1) a +1 z µ 0 I � B y ( x , y , z ) = [ ] r a [ r a + ( − 1) a +1 c a ] 4 π a =1 � B x 2 + B y 2 + B z 2 | B | = Andrew Markham SenSys 2010 10/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Decay Magnetic fields fall off more rapidly RSSI MI RSSI ∝ 1 | B | ∝ 1 r 2 r 3 40 dB/decade 60 dB/decade Andrew Markham SenSys 2010 11/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Decay Magnetic fields fall off more rapidly Range less than traditional radio RSSI MI RSSI ∝ 1 | B | ∝ 1 r 2 r 3 40 dB/decade 60 dB/decade Andrew Markham SenSys 2010 11/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Antennas Magnetic field controlled by size and shape of generating coil RSSI MI 10 10 5 5 0 0 5 5 10 10 10 5 0 5 10 10 5 0 5 10 Andrew Markham SenSys 2010 12/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Antennas Magnetic field controlled by size and shape of generating coil RSSI MI 10 10 5 5 0 0 5 5 10 10 10 5 0 5 10 10 5 0 5 10 Andrew Markham SenSys 2010 12/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Antennas Magnetic field controlled by size and shape of generating coil RSSI MI 10 10 5 5 0 0 5 5 10 10 10 5 0 5 10 10 5 0 5 10 Andrew Markham SenSys 2010 12/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Antennas Magnetic field controlled by size and shape of generating coil RSSI MI 10 10 5 5 0 0 5 5 10 10 10 5 0 5 10 10 5 0 5 10 Andrew Markham SenSys 2010 12/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Antennas Magnetic field controlled by size and shape of generating coil Simple to alter field patterns to optimize localization RSSI MI 10 10 5 5 0 0 5 5 10 10 10 5 0 5 10 10 5 0 5 10 Andrew Markham SenSys 2010 12/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Multipath and Penetration MI penetrates any non-metallic objects and does not suffer from multipath RSSI MI 14 14 13 13 12 12 11 11 B-field RSSI 10 10 9 9 8 8 7 7 6 6 0 0.02 0.04 0.06 0.08 0.1 0 0.02 0.04 0.06 0.08 0.1 Distance Distance Andrew Markham SenSys 2010 13/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Multipath and Penetration MI penetrates any non-metallic objects and does not suffer from multipath Environmental obstacles do not affect MI localization accuracy RSSI MI 14 14 13 13 12 12 11 11 B-field RSSI 10 10 9 9 8 8 7 7 6 6 0 0.02 0.04 0.06 0.08 0.1 0 0.02 0.04 0.06 0.08 0.1 Distance Distance Andrew Markham SenSys 2010 13/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Step 2: Sensing and compression Andrew Markham SenSys 2010 14/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Animal Tag: MI Sensor MI sensor detects signals from three orthogonal transponders Simultaneously measures RSSI Vector magnitude taken to ensure rotational invariance � B x 2 + B y 2 + B z 2 | B | = Andrew Markham SenSys 2010 15/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Energy to localize MI sensor operates at low frequency (125 kHz) and is much more energy efficient than RF RSSI MI 255 µ J 2.4 µ J Andrew Markham SenSys 2010 16/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary RSSI vs MI: Energy to localize MI sensor operates at low frequency (125 kHz) and is much more energy efficient than RF 100 times less energy - allows for continuous tracking RSSI MI 255 µ J 2.4 µ J Andrew Markham SenSys 2010 16/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Animal Tag: Power budget 0.3 0.25 Average current (mA) 0.2 Processor Data 0.15 Beacons Flash MI Sensor 0.1 0.05 0 Underground Aboveground Andrew Markham SenSys 2010 17/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Animal Tag: Power budget Note that the majority of energy is used by the CPU, not by localization 0.3 0.25 Average current (mA) 0.2 Processor Data 0.15 Beacons Flash MI Sensor 0.1 0.05 0 Underground Aboveground Andrew Markham SenSys 2010 17/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Animal Tag: Wavelet Compression Each collar typically receives 250 000 readings per day Andrew Markham SenSys 2010 18/42

Motivation Steps in MI localization System Design Results Limitations and Lessons learnt Summary Animal Tag: Wavelet Compression Each collar typically receives 250 000 readings per day 7 bytes per record (antenna id, timestamp, strength) = 1.7 Mbytes per day Andrew Markham SenSys 2010 18/42