To avoid wildlife-human conflict several solutions are used, like electrical fences, the most expensive solution. Nowadays, technology enables alternative and cheaper approaches for conservation projects. A technological device was developed to detect elephants, moving on their habitat, and predict and react by avoiding confrontation with man. The devices were tested in field experiments, and proved to be efficient in capturing floor vibration, and air-sound signals. Collected data also enabled the estimation of the vibration-source by calculus (using triangulation), revealing the importance of the methodology for real-time location and tracking of high mass animals (e.g. elephants). Building up a mesh of devices, separated 25 m from each other, is estimated as possible to monitor and identify different animals (by discriminating patterns) in an area, like a virtual fencing system. Though the devices may be effective for animal behaviour research, or even animal communication analysis, or other Biology field, other applications outside Biology are possible for them, like monitoring of: rock-falling, micro seismic railway, infrastructures, and people movements.
The purpose for using a vibration sensor is related with its specifications and application. There are several types of vibration sensors, and they all have different performances. Geophones, for example, are generally used as ground sensors in seismic studies [
Ground-vibrations can be produced by a walking being, or by an earthquake, or by rocks free falling from a cliff. However, an earthquake releases much more energy than anthropogenic activities on the surface. Seismologists classify seismic events by its magnitude [
According with a frequency scale, sound waves are categorized into: infrasonic waves (below 20 Hz), audible waves (20 Hz - 20000 Hz; lie within the range of sensitivity of the human ear), and ultrasonic waves (have frequencies above the audible range) [
Sound waves, for example, “travel through room-temperature air with a speed of about 343 m/s, travelling with higher speeds through most solids” [
Accelerometers are used as microseismic sensors [
The new device aims at overcoming issues found in other attempts to do geofencing based on vibration sensing.
To estimate the location of a source-vibration using stationary stations, we can use techniques based on elapsed time or vibration intensity. For example, in the seismic monitor solutions, an elapsed time technique is used to estimate the epicentre of an earthquake. In our case, due to the short distances between the sensors, a vibration intensity solution is a more efficient approach. Advances in electronic sensors, namely, triaxial MEMS accelerometers, gave a significant contribution to implement these solutions at an affordable cost. These modern sensors enable us to sense very small vibrations, with high axial accuracy.
To estimate the location of a vibration source using the intensity received by stationary vibration sensors, we use the mathematical model described below.
Vibration intensity (In) at a certain distance (r) is given by the following equation, where Isource is the intensity of the source of the vibration.
I n = I source π r 2 (1)
Ground waves propagate in very different ways (P-waves, S-waves, Rayleigh waves, etc.), so In is given by the vector sum of the intensity sensed on each axis (x, y, z):
I n = ( I x _axis ) 2 + ( I y _axis ) 2 + ( I z _axis ) 2 (2)
Considering that the Intensity of the vibration source ( I source ) is the same for all 4 sensors, then:
I 1 r 1 = I 2 r 2 = I 3 r 3 = I 4 r 4 (3)
The distance between the vibration source and the sensor (rn) can be expressed using the coordinates of the location of the source (xsource, ysource) and the sensor (xn, yn), as follow:
r n = ( x source − x n ) 2 + ( y source − y n ) 2 (4)
The intensity of the vibration source (Isouce) is unknown, so we need to estimate the location using the ratio of the intensity received by a pair of sensors (
I 1 r 1 = I 2 r 2 (5)
Drilling down the Equation (5) using the x, y version of the rn, we obtain the Equation (6), where x1, y1, I1, are known values from sensor 1, x2, y2, I2 are known values from sensor 2.
The Equation (6) represents the relation between xsource and ysource variables. This relation can be geometrically represented by a line of all possible points for the location of the source of the vibration. The diagram below (
(−100.0) and the location of sensor2 = (100.0).
As shown above, whenever the ratio between the intensities measured is not 1, the line is represented by an elliptic curve. Excluding some particular cases, the interception of two elliptic curves will be two points. So, a third independent curve is required to obtain one single interception point that represents the location of the source of the vibration:
I 1 r 1 = I 2 r 2 I 1 r 1 = I 3 r 3 I 1 r 1 = I 4 r 4 (7)
A representation of the scenario described above is given by
As shown above, a minimum of four sensor are needed to estimate the location
of the vibration source, and the vibration must be sensed in all of them. Whenever the lines interception does not represent an exact match (i.e. a single point), we will get a cloud of nearby points, obtained from the interception of each pair of lines. In this case, the average point represents the estimated location of the vibration source, and the distance to the furthest point will be the estimation error.
To test the new method proposed above, field trials were made which used four prototype vibration sensing units, developed specifically for this project. The diagram of each unit is shown in
The field setup consisted in turning on units 1, 2, 3 and 4, and placing them on the ground. Devices were connected to a portable PC, a Microsoft Surface Pro 3, connected to a Wireless LAN network named “iSense”, and a SciLab version 5.5.2. was run (the custom application) do receive and show the signals
Issue | Specifications |
---|---|
Power | 5 VDC, 70 mA |
Dimensions | 90 × 50 × 17 mm |
Weight | 60 grams |
Vibration sensing | High-performance and low noise tri-axial MEMS accelerometer |
Maximum acceleration sensing range | ±2 g |
Lowest frequency sensitivity | 0.001 Hz |
Signal non-linearity | below 0.1% |
Signal noise density | 45 µg/Hz1/2 |
Signal conversion | 32 bits precision (1 bit represents 0.19 µg) with > 20 noise free bits at 1000 sps |
Sampling rate | Programmable, 1000 sps as default |
Signal conversion integral nonlinearity (INL) | ±2.5 ppm of full scale range (FSR) |
Low drift internal signal reference | 2 ppm/˚C |
Inter-axis interference (crosstalk) | −120 dB at 1 kHz |
Ultra-low signal distortion | 0.000022% |
Micro Controller Unit (MCU) | Embedded; for local data processing |
Wireless data communication | Wi-Fi 802.11 b/g/n with maximum transmission power of +18 dBm |
Antenas | Embedded; 2.4 GHz |
GPS receiver | Embedded; 48 channels, signal sensitivity of −163 dB・m, Accuracy lower than 2 meters for best scenario and time sync with an accuracy of 33 ns (good conditions) |
IP communications | With a data stream rate of 256 ksps for 1000 sps signal sampling rate |
from the units.
We performed field trials to collect data that allowed us to analyse the following parameters: 1) Maximum sensing distance determination of the prototypes; 2) Location source vibration (math calculation); and 3) Identification of distinct signal patterns of ground vibrations.
The trials were conducted in the winter season (air temperature ranged from 9˚C - 12˚C, and soil humidity was approximately 90%) in 2016 in a pine forest with stabilised sand soil from dunes (modern sedimentary deposits) at the following coordinates 40˚34'41.6"N 8˚43'54.2"W (place 1), and in 2017, at the following coordinates 40˚33'55.73"N; 8˚29'42.86"W (place 2), Aveiro, Portugal. This second location corresponds to a soccer game field with a homogeneous floor of pliocene-pleistocene sands and the Triassic Eirol sandstone [
In each field trial, and with the propose to create a standard ground vibration signal, we repeated the dropping of the 8 kg mass, from a height of about 1 meter from the soil, to simulate a vibration source. For each spot marked to drop of the mass, we repeated three times the procedure. This was done in experiments with the four prototypes in line, or in a square distribution setup on the floor. Also, the ground-signals generated by an 80 kg running man were collected by the prototypes. A third kind of signals were generated by the reproduction of audio record [
To test the maximum sensing distance, we placed one sensor on the floor and simulated a sequence of vibrations at a known distance from the sensor (
To estimate the location of the signal source we need to know the relative distances between the units and to receive clear signal in the four units. The field trial described in
To assess the spectral diversity of the signals according with the type of the signal
source, we used the same field, and simulated a vibration episode using different sources, such us, the drop of an 8 kilogram weight, a man running, and the reproduction of African savannah elephants recorded sound [
The distance sensing performance of the units for several different signal sources, and the unit setup is shown in
The graph shows that a man running causes lower soil vibrations, when compared with the 8 kg mass weight. The maximum sensing distance is
approximately 15 meters for this setup.
The experimental data is consistent with the theoretical model of the mechanical wave’s intensity (the equation number 1).
I n = I source π r 2
The following diagrams of the
Considering the estimated errors obtained above for each point, the global average error is ±0.232 meters. Having in consideration the dimension of this array of sensors, with an interval of 10 meters between sensors, the average error is 4.64% of the size of the sensors array.
Using the signals collected from the field trial, we obtained the spectrograms shown in Figures 8-10.
As shown in the spectrograms (Figures 8-10), distinctive and unique patterns are gotten for the different events. The data obtained from the field trials also showed the repeatability of this pattern so we can establish a relation between the pattern and the event. For the purpose of this solution, a library of signatures will be required to allow the system to recognize events along the virtual fence. This library can be developed using a machine learning approach, that is, continuous expansion of the library by cross-checking unknown events.
MEMS-based digital sensors have their advantages, since these sensors offer new capabilities compared with conventional arrays of geophones, because they “provide better vector fidelity thanks to its accurate calibration (amplitude and orthogonality), broadband linear response (from DC to 800 Hz) and low distortion (< −90 dB)”; also integration of the sensor with the station electronics allows size/weight reduction provide complete digital transmission, from the sensor to the central unit, which is less sensitive to electromagnetic pick-up, cross-talk, and leakage offers the potential to reduce costs while improving data quality [
HWC is a critical aspect of any wildlife conservation initiative. From the human perspective and as referred in Woodroffe et al. (2014) [
Pitman et al. (2017) [
To mitigate this conflict, fencing has been a widely used approach to define the borders of protected areas. The physical solid barrier created by the fences, has proven to be an effective way to enforce the separation between humans and wildlife. However, and as discussed by Woodroffe et al. (2014) [
The “loxophone” solution provides: an affordable cost, since it uses nowadays technology, and avoids expensive geophone sensors; wireless mesh network, avoiding the need of long cables and time consuming installations; high sensitivity, since the MEMS sensor is a high resolution Analog to Digital Converter to capture very low vibrations; 3 axis analysis to improve sensing capability for all waves independently of the polarity of the wave when crossing the sensor, contrasting with mono axial sensors; GPS data to identify the location of the sensor and provide time synchronization, and to support triangulation calculus to determine location of the source-vibration; a viable solution to implement medium to large size geofences, since traditional solution are expensive and complex for such scale. As potentialities for this technology the research team became aware
that vibration sources produce spectral signatures that enable the identification of the vibration cause; e.g. if an animal produces a specific signature, it is possible not only to know “where” it is, but also “what” is crossing the virtual fence. As shown on results, we can obtain distinctive spectral signatures that can be
related with different events and vibration sources. Günther et al. (2004) [
With this solution, the location and identification not only of the large size wildlife, but of pouching activities is possible.
The field trials conducted with these prototype sensors shown that we can locate the vibration source with an error below 5%. Considering the objective of this solution, we believe that this provides a good level of accuracy to track events along the virtual fence.
We found from the results above that the location accuracy and system sensitivity is dependent on the distance between sensors. So, the shorter is the distance between sensors, higher will be the level of sensitivity and accuracy of the virtual fence, but more sensors per kilometre will be required.
Considering the sensing distance obtained from the trials, we believe that an interval between sensors of about 25 meters will provide enough sensitivity and accuracy to detect and track large size wild animals, such us elephants. For this solution scenario, we forecast that the sensors network mesh would cost less than 25% of the traditional fencing costs per kilometre. This forecast, based on components and industrial process costs simulation, allows us to offer an effective virtual fence at a cost that would enable medium to large fencing.
R.A., S.C. and M.J.P. collaborated from the first stage of the investigation, making contributions to test the equipment, data acquisition, data analysis, and writing the manuscript. S.C. developed the hardware device, the SciLab instructions set and the mathematical model.
This project was supported by the authors whom disclaim any conflict of interests.
The intellectual property of the prototypes belongs to S.C. and he owns the rights to use this solution in commercial applications.
Anastácio, R., Cardoso, S. and Pereira, M.J. (2018) Spy out to Protect: Sensing Devices for Wildlife Virtual Fencing. Open Journal of Ecology, 8, 192-208. https://doi.org/10.4236/oje.2018.83013