Wireless Sensor Network, 2010, 2, 828-837
doi:10.4236/wsn.2010.211100 Published Online November 2010 (http://www.SciRP.org/journal/wsn)
Copyright © 2010 SciRes. WSN
3-D Grid-Based Localization Technique in Mobile Sensor
Jia Li1, Lei Sun1, Wai Yee Leong2, Peter H J Chong1
1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
2Singapore Institute of Manu facturing Technology, 71 Nanyang Drive, Singapo re
E-mail: ehjchong@ntu.edu.sg
Received August 10, 2010; revised September 21, 2010; accepted October 25, 2010
Considering the environmental protection, forest fire becomes a more and more serious problem and requires
more concerns. This paper provides an efficient method for fire monitoring and detection in forests using
wireless sensor network technology. The proposed technique estimates the location of a sensor node based
on the current set of hop-count values, which are collected through the anchor nodes’ broadcast. Our algo-
rithm incorporates two salient features; grid-based output and event-triggering mechanism, to improve the
accuracy while reducing the power consumption. Through the computer simulation, the output region ob-
tained from our algorithm can always cover the target node. In addition, the algorithm was implemented and
tested with a set of Crossbow sensors. Experimental results demonstrated the high feasibility and worked
well in real environment.
Keywords: Wireless Mobile Sensor Networks, Forest Fire Detection, Localization Technique
1. Introduction
Wireless Sensor Networks (WSNs) provide unprece-
dented opportunities for monitoring areas of interests
such as chemical factory, homes and offices, with low-
cost, low-power and multi-functional sensors. As such,
WSNs attract considerable amount of attention from re-
searchers all over the world. Usually one should use a
large number of sensor nodes to deploy a WSN because
these sensors generally are small in size and can only
communicate within short distances. Information can be
collected from a WSN node through the base station.
However, the collected information would be meaningless
if we could not determine the location of a WSN node.
Consequently, fast, efficient and low-cost localization
techniques are highly desirable for WSNs applications.
The key idea of WSN localization is to allow some
sensor nodes to know their own location at all time. Such
nodes, usually called anchors, may be equipped with
Global Positioning System (GPS) or be fixed ly placed at
pre-determined positions with known coordinates. For
the sake of low cost, most sensor nodes do not know
their locations. These nodes with unknown location in-
formation are called non-anchor nodes. Interestingly,
their locations can be estimated by applying WSN local-
ization techniques [1].
Localization techniques in WSNs are classified into
two groups: range-based and range-free techniques. Ran-
ge-based techniques use sophisticated hardware to con-
duct complex measurements on distance or angle of sig-
nal arrival to obtain location estimates. Typical range
-based localization schemes includes those using re-
ceived signal strength (RSS) [2], time of arrival (TOA)
[3], angle of arrival (AOA) [4], and time difference of
arrival (TDOA) [5]. Noteworthily, range-based localiza-
tion techniques are applicable only when the non-anchor
node of interest is within communication range of the
anchor nodes. Due to the expensive hardware require-
ment, range-based techniques are generally considered as
high-cost solutions. Consequently, this shortcoming un-
fortunately hinders them from being applied for forest
fire surveillance, which is normally formed by millions
of sensor nodes.
Range-free algorithms estimate the location of a sen-
sor only based on the connectivity between non-anchor
nodes and anchors. Three typical existing range-free
techniques are the DV-hop [6], Monte-Carlo Localiza-
tion (MCL) [7] and Monte-Carlo Box (MCB) [8] algo-
rithms. The general principle of these techniques is that
localization can be estimated from the proximity con-
straints, which are defined by a sensor node of interest
Copyright © 2010 SciRes. WSN
being in the transmission ranges of other sensor nodes.
For the authors’ best knowledge, none of aforementioned
localization techniques is exclusively designed forest fire
surveillance. Consequently, when the existing localiza-
tion techniques were applied in forest fire surveillance
systems, they would inevitably result in certain disad-
vantages such as high complexity, low efficiency and
large power co nsumption.
This paper aims to contribute a novel localization
technique for forest fire surveillance by monitoring and
tracking groups of an imals using WSN technology. The
proposed technique based on the existing range-free
techniques and improves the accuracy. In brief, we pro-
pose to attach sensor nodes to selected animals. When-
ever the temperature sensed at these animals’ proximity
rises beyond a predefined threshold, the localization
module in the sensor would be activated and the sub-
jects’ motion paths are analyzed. The region of forest
fire is estimated based on two indicators: (1) a group of
animals are observed to run away from a certain area,
and (2) the temperature sensed around the animals’ sur-
rounding environment is higher than a predefined thre-
The rest of this paper is organized as follows. Section
2 revisits the related localization algorithms and Section
3 presents our proposed localization algorithm. In Sec-
tion 4, the simulation results are discussed, which are
followed by the experimental results in Section 5. Finally,
Section 6 concludes the paper.
2. Related Works
Our proposed localization technique is range-free, which
adopts similar assumptions as the existing three algo-
rithms. In the following, we shall revisit the three exist-
ing range-free localization techniques in order to lay the
foundation for the presentation of our proposed localiza-
tion technique.
2.1. DV-Hop Technique
DV-hop technique [6] uses a mechanism that similar to
the classical distance vector routing. The algorithm’s
implementation is comprised of three steps. First, each
anchor broadcasts an announcement message to be
flooded throughout the network, which contains the an-
chors ID, location and a hop-count parameter initialized
to 0. Each receiving node equip ped with a counter main-
tains the minimum counter value of hops from itself to
every anchor. When the announcement message received
at a node, it will update hop-count and ignore the higher
hop-count value received before, then forward the broadcast
message to their neighbor nodes. Through this me ch a ni s m,
every node in the network can get the shortest distance,
hop-count value. In the second step, it estimates an av-
erage single hop distance, which is then broadcasted as a
correction to the entire network. Finally, the unknown
nodes compute their locations by multiplying the hop-
count values with average hop distance. In the end of this
step, once a node can calculate the distance to more than
3 anchors, it can use centroid formula to estimate its lo-
cation notified with point. DV-Hop is defined as point-
based localization method because the output of localiza-
tion is a point. This technique can produce a relatively
high accuracy in netwo rks where sen sor nodes ar e even ly
distributed and the objects to be tracked are static.
2. 2 . MCL Technique
The Monte Carlo Localization (MCL) [7] is the first
technique exclusively developed to track mobile sensor
nodes. The algorithm calculates a set Lt of N location
samples, each of which represents a possible location of
the node to be tracked at time t. Initially, at t = 0, MCL
assumes that the node has no knowledge about its posi-
tion; hence the first sample set L0 consists of N random
samples which are selected within the deployment area.
At each time step, the set {}
lis updated based on possi-
ble movement of the node and new observations on the
node’s connectivity to the anchor nodes. This process
can be divided int o two p hases:
1) Prediction: In this phase, the node uses its previous
location and maximum velocity, vmax to predict its possi-
ble new location. For example, if the node was at loca-
tion 1
at time t – 1, its current location i
l should be
within a circle with radius dmax from 1
l, where dmax is
the maximum distance that a mobile node can move
within each time interval. Hence, from the old sample
the algorithm randomly selects a new sample
lwithin the circle centered at 1
l with radius dmax. By
this way, from the previous sample set 11
, a
new sample set {}
Ll can be predicted.
2) Filtering: In this phase, the node can eliminate
some predicted samples obtained from the prediction
phase based the connectivity between the node and the
anchors which set up some space constrain to the node
location. For example, if node M can hear an anchor A,
its location must be within a distance r from A, where r is
the radio range of the node/anchor. All location samples
which fall out of this area ought to be eliminated. Con-
sequently, the number of valid samples may drop below
N due to elimination, hence re-sampling (repeating the
prediction and filtering phase) is used to maintain N lo-
cation samples at each time step.
Finally, the estimated location of the node at time t is
the average of all N sample values in the sample set Lt.
Copyright © 2010 SciRes. WSN
2.3. MCB Algorithm
Monte-Carlo Localization Boxed (MCB) technique [8]
was developed based on the MCL technique. The major
difference between MCB technique and MCL is on how
to withdraw a new sample. In the prediction phase of
MCB, new location samples are generated based on the
following information: (1 ) information about the anchors
heard by the mobile nod e, (2) the maximum velocity vmax
and (3) the node’s previous location. This would signifi-
cantly reduce size of the area from which the new sam-
ples are withdrawn, thus improving the efficiency of
prediction phase. Consequently, MCB reduces the num-
ber of re-sample iterations and speeds up the conver-
gence. It is necessary to review the approach used to de-
termine the area B from which location samples are
1) Initialization: At t = 0, the node has no knowledge
about its location. Let B0 denote the initial ‘anchor box’
from which the first sample set L0 is drawn.
If the node is not connected to any anchor,
0{(0,); (0,)}
Bxy where xr and yr is the maximu m x
and y coordinate of the deployment area. The first sam-
ple set L0 consists of N samples selected randomly within
the deployment area. Otherwise, B0 is constructed from
the location of all anchors that the node can communi-
cate with:
(,); (,)Bxx xx (1)
Let (xj, yj) denote the coordinates of the anchor j and
Na denote the total number of anchors heard:
min max
min max
max(), min()
max(), min(); 1....
2) At each time step t: when there exists a previous
sample set Lt-1 (i.e. the sample set is no longer empty as
in Initialization), for each old sample 1
l from the old
set Lt-1. We construct a square of size 2dmax centered at
the old sample. This new box is built from each sample
in the old set Lt-1 and is called a sample box:
min maxminmax
(, ),(,)
iii ii
Bxxyy (3)
denote the coordinates in 1
l, we have
minmin1 max
maxmax1 max
minmin1 max
maxmax1 max
max, ,
min, ,
max, y,
min, y,1....
The area from which new samples are withdrawn
would be the overlap of this square and the anchor box is
shown in Figure 1.
Figure 1. Determine Anchor Box [8].
3. Proposed Localization Technique
The proposed localization technique reduces computa-
tional complexity and improves efficiency by the two
features: (1) Grid-based Output: The algorithm divides
the monitored area into an n × n grid structure and gen-
erates an output region as the estimated node location;
which may consist of one or many neighboring grids; (2)
Event-triggering Mechanism: We proposed to adopt sleep
-wake cycles for the localization module in the sensor
nodes in order to save the scarce battery power of sensor
nodes without sacrificing the algorithm’s efficiency. In
other words, the localization module does not operate
continuously; instead, it is only activated whenever the
temperature obtained by the sensing module exceeds a
predefined threshold. Furthermore, the proposed local-
ization technique adopts similar assumptions as the MCL
and MCB algorithm, which are: (1 ) Anch or nodes, which
are equipped with GPS or fixedly-placed at pre-known
locations, are allowed to know their location all the time;
(2) The transmission range of all anchor nodes is identi-
cal and equal to R.
3.1. Proposed Algorithm
The basic idea is to let the monitored area, e.g. the forest
or nature reserve park, be bounded within x-coordinates
(0, Xs) and y-coordinates (0, Ys). The algorithm first di-
vide s the area into an n × n grid structure and places four
fixed anchor nodes at four corners, one mobile anchor
(attached at a firefighting helicopter) with height h mov-
ing in a certain trail whose projection is a circle includ-
ing the center of the area, as shown in Figure 2. In par-
ticular, if the area is large or not of a square shape, we
may repeat this arrangement over and place more an-
chors at corners of each n × n grid structure. For the sake
of clarity, the smallest area unit is named as grid and
each n × n grid structure as square. As shown in Figure
3, on the ground plane, the dimension of each grid is
Copyright © 2010 SciRes. WSN
Anchor 1
Anchor 2 Anchor 3
Anchor 4
Anchor 5
Figure 2. A square consists of four fixed anchors and one
mobile anchor.
Figure 3. A square consisting of 16 grids in a 4 × 4 structure.
r × r, where r = R×cos45°.
Similar to the DV-hop technique, our algorithm re-
quires a set of distance information from a sensor node to
each anchor node. Whenever the temperature sensed at a
sensor node rises above a predefined threshold, the node
would send a packet containing the set of hop-counts to
the base station, which will be the inputs of the algorithm.
The current set of hop-counts is
, ,..., a
Hhhhwhere c
h is current number of hop s
from the sensor node to anchor node j, and Na is the
number of anchor nodes. The output of our algorithm is
the estimated rectangular region-based location Best of
node M expressed in terms of left-bottom and right-top
vertices’ coordinates (xmin, ymin) and (xmax, ymax).
The tentative nod e location is determined based on Hc.
For example, consider Na=5 and
= ,,,,
C ccccc
hhhhh, for each values c
h in Hc, it is
implied that the distance between the corresponding an-
chor and the node to be sensed is
dh RhR
 
. The algorithm estimates the
location of sensor node M as follows:
The algorithm determines the region c
B which is the
h-hop coverage area of anchor Aj with coordinates (XAj,
YAj), and c
B is a square with side lengthc
hr. The up-
per-bound and lower-bound for x and y coordinates of
max{0, }
min{, }
max{0, }
min{, }
rA j
XX rh
For the mobile anchor above the area, only its projec-
tion is focus, and th e trail will be a square shaped instead
of circle in order to simplify the process. The hop count
is also obtained based on its projection. The 3-D to 2-D
conversion is accomplished as shown in Figure 4.
Next, the algorithm finds the overlap of all regions, i.e.,
B, where j is 1, 2, …, Na. Then,
12 a
ccc c
BBB B (6)
The region Bc would be the tentative estimated grid
-based location of sensor node M. The upper-bound and
lower-bound for x and y coordinates of this rectangular
region are:
min1,min 2,min,min
max1,max 2,max,max
min1,min 2,min,min
max1,max 2,max,max
max{ ,,...,}
min{ ,,...,}
max{ ,,...,}
min{ ,,...,}
As shown in Figure 5, Bc could be either a single grid,
e.g., for input Hc = {3, 3, 2, 3, 2} in Figure 5(a) or larger
grid, e.g., for Hc = {2, 0, 0, 0, 3} in Figure 5( b ).
If the resulted Bc is null, the algorithm will return the
entire monitored area as an output, i.e.,
min max
min max
ccc c
Figure 4. 2-D model with 4 fixed anchors and 1 mobile an-
Figure 5. Estimated region for node M for (a) Hc =
[3,3,2,3,2]; (b) Hc = [2,0,0,0,3].
Anchor 5
Anchor 1
Anchor 2Anchor 3
Anchor 4
Anchor 5
Anchor 1
Anchor 2 Anchor 3
Anchor 4
Anchor 5
(a) (b)
Copyright © 2010 SciRes. WSN
Besides the coordinates of each anchor, the height h of
the mobile anchor is also a significant variable retrieved
from the sensors. In the real implementation, as the
height increases, a larger transmission power is required
in order to communicate with the on-ground node to be
sensed. As shown in Figure 6, the target node t is at
point C, the mobile anchor with height h is at point A,
and the projection of the mobile anchor is at point B. If
the minimum transmission power used to communicate
between point B and point C is PB, the received power,
PC, at point C is given by
PP d
where d is the distance between points B and C and n is
the path-loss exponet. Since the mobile anchor is at point
A, a larger transmission power, PA, at point A is required
so that at point C,
PP lPhd
  (10)
Using (9) and (10), the required transmit power, PA, at
point A is
From (11) we can see that h is important because PA is
increases with h. Thus, during the real implementation,
PA should be adjusted based on h.
3.2. Proposed Algorithm vs. DV-Hop
Our proposed algorithm as introduced in Section 3.1 has
several common ideas with the DV-Hop algorithm in-
troduced in Section 2.1. The comparisons are stated in
this section. Firstly, although our proposed algorithm
requires input similar to DV-Hop including a set of hop
-count unit from one node to each anchor, the estimate
hop distance is no longer needed and in addition, the
calculation of centroid formula is also unnecessary. We
assume the monitoring area, e.g. certain forest region,
can be divided into an n-by-n grid structure, where the
diagonal of grid is equal to the transmission rang of
Figure 6 Illustration graph with mobile anchor at A, node
to be sensed at C.
every anchor. Therefore, the hop distance is an identical
constant value as well as one h op is the same as on e grid.
And then, the algorithm’s output will be a region con-
sisting of integer number of grids. It’s obviously that the
grid-based localization process and output are different
from DV-Hop’s which needs less calculation.
Moreover, nodes in the network have limited energy
resources and weak processing capabilities. If the algo-
rithm uses most of a sensor’s energy to locate itself, it
will have no more left to perform tasks. On the other
hand, considering many applications for wild environ-
ment which is hard to replace the battery of sensors, the
sensors should service as long as possible for the optima
utilization. Therefore, minimizing the power consump-
tion of sensors is needed. As compared to the point-
based localization, grid-based localization is less com-
plicated yet with less processing time, thus it reduces
calculations, saves power consumption and considerably
enhances the sensor’s lifetime.
4. Simulation Results
The proposed localization algorithm under the condition
of 4 fixed anchors and 1 mobile anchor (called mobile
anchor model) as well as only 4 fixed anchors (called
basic model) is simulated in Matlab environment. Accu-
racy test results, location test results and the comparison
results between basic model and the mobile anchor mod-
el will be illustrated in the followings.
4.1. Accuracy Test
The key performance metric of localization algorithms is
the output accuracy. This test is to prove that the output
region can always cover the target node. During the
simulation process, many sample nodes are randomly
generated, labeled in green color, and through the algo-
rithm proposed, the corresponding outputs will be la-
beled out in red co lor. Figure 7 shows 2 of the accuracy
test results under mobile anchor model at n = 10.
Through the simulation result, the accuracy of the al-
gorithm is 100% which implies that the estimated out-
puts can always cover the targeting nodes. Even in the
real implementation, due to the environmental effect,
sometimes the transmission range might be less than the
Figure 7. Accuracy test results at n = 10 (2 samples are dis-
Copyright © 2010 SciRes. WSN
default value, the accuracy of the algorithm is still 100%.
The reason is when the transmission range is a smaller
value, the hop count might be change to a larger value,
and thus, the estimated output region will be slightly
larger than the simulation resu lt, but it still can cover the
targeting node. So, the algorithm always performs a 100%
4.2. Location Test
The location test investigates the size of the estimated
output region for a target node on different locations over
the monitored area. The test randomly generates 2000
node-location samples with coordinates (x, y) within the
monitored area with [0,], [0,]
Xy Y. The moni-
tored area is divided into a 10 × 10 grid structure. Each
node-location (x, y) with respect the four anchor nodes is
then transformed to the current hop-count values, which
serve as the input for our algorithm. The test subsequently
estimates the grid-based location for each sample using
our algorithm, calculates the area of the estimated region
in grid unit, and plots the estimated output region’s area
as a function of its node-location as shown in Figures 8
and 9.
In Figure 8, it only consists of four fixed anchors at
Figure 8. Location test of 4 fixed anchors (basic model).
Figure 9. Location test of 4 fixed anchors and 1 mobile an-
chor (mobile anchor model).
the corner of the monitoring area whereas in Figure 9,
the result is obtained under the mobile anchor model
which introduces one more mobile anchor moving
around the center. It is observed that under the mobile
anchor model, the maximum size of the output region is
18 to 20 grids and the average output is around 10 grids.
While in the basic model, the maximum size of the out-
put region reaches 80 to 90 grids at the center and the
average output size is around 40 grids. In general, the
output is desirable to be a region with as small as possi-
ble area to quickly locate the fire’s actual position.
Therefore, the smaller the estimated region’s area is, the
better the algorithm’s accuracy is. It is clear that after
adding the mobile anchor, the size of the output region
reduced fro m 40 % to 10% on average, and the peak out-
puts at the center are also diminished, such that an
equally distributed size of the output regions are gener-
4.3. Performance Comparison between Basic
Model and Mobile Anchor Model
In this test, we study the performance between basic
model and mobile anchor model in terms of the prob-
abilities of outputting different sizes of regions. The CDF
curves are plotted for these two models as shown in Fig-
ures 10 and 11. In each test, the size of the monitoring
area is fixed, and 5000 node-location samples are ran-
domly generated. Figure 10 shows the results when the
monitoring area is a 10-by-10 grid structure and Figure
11 shows the result when the area is a 12-by-12 grid
structure. The x-axis indicates the output size and the
y-axis corresponds to the probabilities of outp utting such
region size.
From the result it can be seen that the curves for the
mobile anchor model are always higher than that of the
basic model, which implies that the addition of this mo-
bile anchor increases the probability of outputting a
05 10 15 20 25
% Output P robability
out put size (g ri d No.)
2-D Fixed Anc hors
3-D Mobi le A nch or and F ixed A n chors
Figure 10. CDF Curves at n = 10.
Copyright © 2010 SciRes. WSN
05 10 1520 25
% O utput Probabi li ty
out put si z e (gri d No. )
2-D Fixed A nchors
3-D Mobil e A nchor and F i xed A nchors
Figure 11. CDF Curves at n = 12.
small size region and improves the performances of the
As for a large size of the monitoring area, the prob-
ability of ou tputting o f a region with grid size less than 5
is very small for both models. However, as the output
grid size increases, the performance of mobile anchor
model performs better than basic model. In fact, the mo-
bile anchor improves the algorithm more for a larger
monitoring area. This is also observable if the compari-
son is done between them as shown in the Figures, in
which the tendencies of the two curves go further away
as n increases to 12.
5. Experiment Results
Two experiments have been conducted to test the pro-
posed localization technique. As shown in Figure 12, the
two experiments were implemented in the soccer field
with a square of size 60 m × 60 m.
The monitoring area was divided into 3 × 3 grids so
that, each grid occupies a 20 m × 20 m area. The trans-
mission power of each sensor node was set as –1dBm for
which, the radio transmission range could reach around
30 m, which was approximately equal to the diagonal
length of each grid. Four Anchors were placed at the four
corners of the region and four relay nodes were placed at
the four points indicated as A, B, C, and D. To simulate
the animals’ motion, an experimenter holding the sensor
node was moving inside the region from the grid marked
“1”, “2” “3”, and until the middle grid marked “9”. At
the base station side which was connected to a PC ter-
minal, the monitoring software would analyze the data
sent by the sensor nodes and then output the grid region
which the examiner was in.
5.1. Hardware Requirements
Table 1 shows the hardware list during the implementa
Figure 12. Monitoring Area Layout.
Table 1. Hardware List.
Hardware Model Image
Gateway board MB520
Mote sensor data acqui-
sition board MTS300CA
Mote MPR2400CA
Workstation Any model
tion. For further information about each hardware com-
ponent, Interested readers can refer to the Crossbow
MICAz datasheet [9 ], MPR-MIB User s Manual [10] and
MTS/MDA Se nsor Board Users Manual [1 1].
5.2. Results for Experiment 1
The experiment started as the examiner walking from
Anchor 1. Under this condition, the node held by the
examiner could communicate with Anchor 1 directly,
communicate with Anchor 2 through relay nodes A and
B, communicate with Anchor 3 through relay nodes C
and A, and communicate with Anchor 4 through relay
nodes D and A. So the set of hop-counter values received
at base station was H = {1, 3, 3, 3}. By using the formula
12 a
ccc c
BBB Bto calculate the overlap area,
the output was then generated and as shown in Figure
Next, when the examiner reached the border of grid 1
and grid 2, where the hop-counter value was H = {1, 2, 3,
3}, the system would output a line in between those two
grids, which represented the overlapped region as shown
in Figure 14.
As the process continued, the examiner would reach
purely inside grid 2, so that, the set of hop counters
would be H= {2, 2, 3, 3}. But by using the formula
12 a
ccc c
BBB B to calculate the overlap region,
the output would be a two-grid sized region which is
Copyright © 2010 SciRes. WSN
Figure 13. H = {1, 3, 3, 3}.
Figure 14. H = {1, 2, 3, 3}.
shown in Figure 15.
The output size in this situation was increased to 2
grids, which caused the decrease of the localization ac-
curacy. The second experiment would show the proposed
method to improve accuracy, and it will be shown in
detail later in this Section.
Continue with the current experiment, when the ex-
aminer reached the border of grid 2 and grid 3, where the
set of hop counters was H = {2, 1, 3, 3}, the output is
shown in Figure 16.
Then, Figure 17 shows the output region whe n the
examiner was purely inside the grid 3 and H = {3, 1, 3,
The experiment continued until the examiner reached
the middle grid where the set of hop counters received
was H = {2, 2, 2, 2}. Figure 18 shows the output result.
5.3. Results for Experiment 2
With all the other conditions no change, experiment 2
was implemented with one modification, which was to
introduce a mobile Anchor. As the simulation results
Figure 15. H = {2, 2, 3, 3}.
Figure 16. H = {2, 1, 3, 3}.
Figure 17. H = {3, 1, 3, 3}.
showed in the previous section, by adding a 3-D mobile
Anchor, the accuracy of localization could be largely
increased, as shown in Figure 8 to Figure 9. For con-
venient implementation, this experiment 2 only used the
projection of the 3-D mobile anchor trail as the route.
The added mobile Anchor was carried by a remote
Copyright © 2010 SciRes. WSN
Figure 18. H = {2, 2, 2, 2}.
control car and moving inside the region along the path
A-B-C-D as shown in Figure 12 .
First, if examiner holding the node was at the location
indicated by “E” as shown in Figure 19, where the
hop-counter values was H = {2, 2, 3, 3}, the algorithm
without mobile anchor could on ly calculate out the result
as shown in Figure 20. Then for the case with mobile
Anchor, if the mobile Anchor was moving near the loca-
tion “C” or “D” and broadcasting a message, this mes-
sage would be relayed by one node either located at “A”
or “B” and then received by the node. The base station
could then analyze this message, as long as the message
was not directly received by the node, the base station
would know that, the node was not within one grid dis-
tance from the mobile Anchor, so that, grid 9 would be
filtered out, and the grid 2 would be the final output as
shown in Figure 21.
But the point needs to be noticed is that this mobile
Anchor method is not able to filter o ut the unwanted grid
every time. For example, if the examiner holding the
node was still inside grid 2, but at the same time, the
mobile Anchor was moving near to location “A” and “B”,
then the node could communicate with the mobile An-
chor directly which meant that, the node was within one
grid distance away from the mobile Anchor. After this
information was known by base station, it could not do
further analysis because no matter the node was inside
grid 2 or grid 9 could both communicate with the mobile
Anchor directly. So as a conclusion, the possibility for
filtering unwanted grid in this condition could reach up
to 50%.
The 50% possibility in this implementation is large
enough to be accepted, because this implementation ex-
periment is a very basic model (3 × 3 grid, small moni-
toring area), and it is only aimed to verify the availab ility
of the mobile Anchor method. As proved by the simula-
tion results showed in the previous section, if th e mobile
Anchor is introduced to a much larger monitoring area, it
Figure 19. The Location of Examiner.
Figure 20. H = {2, 2, 3, 3} without Mobile Anchor.
Figure 21. H = {2, 2, 3, 3} with Mobile Anchor.
will be more efficient.
6. Conclusions
This paper presents a localization technique particularly
tailored for forest fire surveillance systems. By adopting
grid-based output and event-triggering mechanism, the
Copyright © 2010 SciRes. WSN
proposed algorithm can reduce computational complex-
ity, improve output accuracy and reduce power con-
sumption. Computer simulations were conducted to ver-
ify that the proposed algorithm is efficient. Furthermore,
two implementation experiments have been conducted
with the proposed algorithm. For the first experiment
without mobile Anchor, 66.67% of the scenarios could
return single-grid location estimation. The second ex-
periment with mobile Anchor would further increase the
accuracy by 50%. Experimental results have shown that
the proposed algorithm reduces the complexity of im-
plementation, and it can provide considerable accuracy.
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