![](bg2.png)
as: (Awad et al., 2007); (Sichitiu et al., 2003);
(Barsocchi et al., 2009); (Paul and Wan, 2009) and
(Hyo-Sung and Wonpil, 2009). Many of these
algorithms focus on indoor localization (Awad et al.,
2007); (Barsocchi et al., 2009); (Paul and Wan,
2009) and (Hyo-Sung and Wonpil, 2009), even if
RSSI is not particularly suitable for indoor range
estimation due to walls and other obstacles that
affects the signal propagation (Akyildiz et al., 2002).
In a room it can be effective however, and also a
good alternative since GPS is not available. In
(Awad et al., 2007) they used a neural network to
estimate the distances based on RSSI. This worked
well, however the distances in their experiment were
below 5 m, making it inapplicable to animal
tracking. They used an exponential function to
predict distance from RSSI. Using regression, they
found it to be a suitable function.
In (Sichitiu et al., 2003) the researchers
performed RSSI measurements using IEEE 802.11b
network cards. These measurements show a good
correlation between RSSI and distance. The
measurements were performed at relatively short
distances, the longest being 40 meters. This makes
the topology and terrain less of a factor. They also
simulated the accuracy of their RSSI-based
localization algorithm. They were able to get a low
uncertainty. The main problem with applying their
simulation to long range applications is that their ±
25 m RSSI accuracy is too optimistic. The
experiments in this paper show that ±100 m is closer
to the truth.
Researchers have also tried using RSSI to locate
cattle in grazing fields. In (Huircán et al., 2010) they
were able to locate animals by having a high beacon
density, with only 80 m between beacons. This
makes their solution expensive for animals that
reside in a large area. To cover an area of 5000 x
5000 m, which is not unusual for sheep grazing in
the mountains, would require approximately 3900
beacons.
3 TRILATERATION AND
UNCERTAINTY
Trilateration is a well-known method for
localization. A good introduction to the topic can be
found in (Yang and Liu, 2010). To be able to
unambiguously locate an object in two dimensions
using trilateration, the following information is
necessary:
1. The position of at least three other objects.
2. The distances between the object being located
and each of the other objects.
Figure 1: Trilateration with two beacons. The object could
be located in both shaded areas.
If these are known, one can solve a set of
equations to find out the position of the last object.
Although the position of three other objects is
enough to locate an object, the accuracy improves
with every extra distance and location combination
that is known.
Applying RSSI-based trilateration to livestock
tracking is not trivial. There are especially two
problems that arise from relying on trilateration
based on other animals’ positions. The first problem
is that one needs the position of at least three other
animals. This means that at least four animals must
be fairly close to each other and three of them must
find their positions via different means. To achieve
this, the flock must be dense compared to the
maximum range of the transceivers used to send
messages between the animals. Some of the sheep
must also have a different localization method.
Localization is possible with fewer than three other
known positions, but it will lead to reduced accuracy
due to separate uncertainty areas, as illustrated in
figure 1.
The second problem is the uncertainty of the
final position estimate. The uncertainty comes from
two sources, the position of the other objects and the
distance to those objects. If the other objects were
localized via GPS their position should be fairly
certain. GPS receivers typically have an accuracy of
10 meters under normal conditions. The distance
uncertainty depends on the accuracy of the distance
measurement tool.
RSSI is generally reduced with increasing
distance and easy to obtain if both animals carry
transceivers. Therefore it is a good candidate as a
distance measurement tool. Distance is however not
the only thing affecting RSSI. Terrain, obstacles,
vegetation, antenna orientation and weather also
plays a role. With that in mind the objective of this
paper is to determine if RSSI is a suitable tool for
livestock tracking. That means to test if it can
provide acceptable uncertainty in a realistic terrain.
SENSORNETS 2012 - International Conference on Sensor Networks
238