Int. J. Communications, Network and System Sciences, 2010, 3, 697-702
doi:10.4236/ijcns.2010.38093 Published Online August 2010 (http://www.SciRP.org/journal/ijcns)
A Sensor Awakening Algorithm for Wireless Multimedia
Sensor Networks Thr ee Dimensional Target Tracking
Jing Zhao1,2, Jianchao Zeng2
1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China
2Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology,
Taiyuan, China
Received May 13, 2010; revised June 22, 2010; accepted July 24, 2010
Abstract
For node awakening in wireless multi-sensor networks, an algorithm is put forward for three dimensional tar-
get tracking. To monitor target dynamically in three dimensional area by controlling nodes, we constract vir-
tual force between moving target and the current sense node depending on the virtual potential method, then
select the next sense node with information gain function, so that when target randomly move in the specific
three dimensional area, the maximum sensing ratio of motion trajectory is get with few nodes. The proposed
algorithm is verified from the simulations.
Keywords: Wireless Multimedia Sensor Network, Multimedia Sensor, Sense Area, Possible Sense Area, Three
Dimensional Target Tracking, Information Gain, Virtual Potential
1. Introduction
Wireless sensor networks (WSNs) have drawn a more
attention in the last few years [1], including traditional
wireless sensor networks and wireless multimedia sensor
networks(WMSNs). Since the traditional wireless sensor
networks only provide simple sensing data such as temp-
erature, humidity and so on [2], so not as to meet the re-
quirement of more complicated and precise data applica-
tions. WMSNs differ from the traditional wireless sensor
networks due to their characteristic of directivity and are
more interest in intensive information data (e.g. video,
image) [3].
The theory of the virtual force is often used to solve
coverage problem in WSNs. It was first proposed in the
research of the mobile robotics route plan and obstacle
avoidance by Khatib [4]. Howard et al [5] applied it to
the coverage problem of WSNs, then the technique was
proved to be useful for such problem in [6,7], the virtual
potential field could cause the repel force between sen-
sors. The force that repelled each other made sensor
spread from dense to sparse area. Tao et al. and Zhao et al.
[8,9] present a virtual potential field based coverage-en-
hancing algorithm for directional sensor networks, where
overlap is reduced and the ratio of coverage is enhanced
by forcing sensors to the most beneficial orientation un-
der repel force in terms of the Euclidean distance be-
tween ‘centroid’.
There are many target tracking algorithms for traditional
wireless sensor networks, such as following[10]: K Mec-
hitov et al [11] provide an cooperative tracking with bi-
nary-detection algorithm; In [12], based on signal inten-
sity a distributed algorithm about decentralized source
localization and tracking is put forward by Rabbat and
Nowak; Based on clusering, Friedlander D. et al. pro-
vides Dynamic space-time algorithms[13]; A Adaptive
target tracking algorithms is given by Xingbo Yu et al. by
considering tracking efficiency and nodes energy con-
sumption[14]; Gordon [15] uses particle filter algorithm
and so on, but most of them focus on the research of tar-
get location and data processing. The coverage is a fun-
damental problem in the networks [16]. Perfected cov-
erage is important to sense target area and collect useful
data. After node freedom deployment, it is a hot problem
needed to be urgently solved that how control sensors to
cover motion trajectory efficiently and wholly [17] espe-
cially for three dimensional target tracking.
In this paper, focus on three dimensional target track-
ing application, we structure virtual force between mov-
ing target and the current sense node based on the virtual
potential method, and control node rotation to get maxi-
mum sensing ratio of motion trajectory with few sensors,
and define the warning round, intersection and informa-
tion gain area, then select the next sense node with in-

J. ZHAO ET AL.
698
formation gain function so that as few nodes as possible
are used to get the maximum probability of motion tra-
jectory sensing when target randomly move in the spe-
cific three dimensional area.
2. Sensing Model
2.1. Concepts
Prior to build sensing model of WMSNs, some definiti-
ons are given below:
● Sense Cone.
Sensing cone is the three dimensional area being
sensed by multimedia sensor in three dimensional target
area, shown with shadow area in Figure 1.
● Possible Sense Area.
Possible sense area is a sphere being sensed by multi-
media sensor rotation in three dimensional target area,
shown with all sphere in Figure 2.
● Sense Direction.
In three dimensional target area, three dimensional
coordinate axis is built through sensor position, sense
direction, that is

, are expressed by ),,(



those
respectively are vidicon’s offset angles to the X, Y AND
Z coordinate planes.
● Neighboring Sensor.
The neighboring sensors are the sensors whose dis-
tances are less than 2R, where the R is the radius of Pos-
sible Sense Area.
● Neighboring Area and Neighboring Sensor of Inter-
section.
The neighboring area of intersection is the circle area
whose radius to intersection is less than R and the sen-
sors in this circle are neighboring sensor of intersection.
● Warning Round.
The warning round is the circle whose radius is less
than R, when target arrive at warning circle, sense node
Figure 1. Sensing cone of a sensor.
Figure 2. Possible sensing area of a sensor.
will warn other neighboring sensors of this intersection
and the next sense node begin to be selected.
● Intersection.
The intersection is the join point of warning round and
target motion trajectory and target will continue moving
with direction at this point.
● Centroid.
The “centroid” point denotes center of the mass in ph-
ysics. Here, the “centroid” is defined as the center of sen-
se cone, so the sector’s turning around the location is vie-
wed as the centroid’s rotation. The “centroid” point of
sense cone is at the symmetric axis and its distance to
sensor location is


3/)sin(2R.
● Coverage Leak.
The coverage leak is the uncovered motion trajectory
of target.
● Coverage Ratio of Target.
That is ratio of motion trajectory length covered by all
sense nodes’ possible sense area to whole motion trajec-
tory length.
2.2. Sensing Models
Unlike an isotropic sensor, a multimedia sensor has a co-
ne sensing area shown in Figure 1 and aimed at maxim-
izing covered motion trajectory with a minimum number
of sensors, it is assumed not to move and only to rotate ar-
ound sensor position after randomly deployed, so forms
sphere possible sensing area, as shown in Figure 2.
Considering sense cone and possible sense sphere ,we
define sensing model with 5-tuple, shown as follows:
),,,,(  RP , there P is sensor’s location, R is radius of
sector’area,

shows sensor’s sense direction,

sh-
ows half of separation angle of sensing cone,

is rota-
tional speed, that is sensor rotational speed for sensing
moving target.

J. ZHAO ET AL.
699
3. A Sensor Awakening Algorithm for
Wireless Multimedia Sensor
Networks Three Dimensional
Target Tracking (SATTT)
3.1. Problem Definition
We shall study the problem of node scheduling in wire-
less sensor networks. To begin with, some necessary ass-
umptions have to be made: 1) the localization informa-
tion of moving target can be achieved with certain eq-
uipments built in sensors such as radar, infrared aids, and
angle measuring device; 2) the exact sensing angle and
position of sensor can be gained by itself; 3) all nodes in
network are homogeneous, whose sensing angle and ra-
dius are same as each other; and 4) all sensor nodes are
deployed randomly.
In real monitor applications, the target randomly enter
the specific three dimensional area and randomly move
in this area till it leaves the area. Then the controlling of
sensing nodes, i.e., awaking as less nodes as possible to
monitor the target gets inevitable. Aiming at the surveill-
ance for target randomly moving in the specific area, this
paper develops a node controlling algorithm for three di-
mensional target sensing with randomly deployed nodes
in wireless multimedia sensor network.
3.2. Idea of Algorithm
Entering the specific three dimensional area, the target
will be sensed by the one node, so others sensors turn to
be asleep. Then the virtual gravitational force between
target and sensing node can be calculated based on virt-
ual potential theory and used to control the node turned
around following with the moving target. When the targ-
et moves at the warning around of the node possible sen-
sing sphere, the intersection is formed. According to the
moving angle of target at the intersection, the area of in-
formation gain is set to calculate the gain values of the
neighbor nodes of the intersection. The node having the
maximum gain value will be selected to be the next sense
node. At the same time, the current sensing node is tur-
ned to be asleep. The above process repeats until the tar-
get goes away the specific area.
3.3. Sensor Rotation Way
Entering the specific three dimensional area, the target
will be sensed by one nodes. Then the virtual force betw-
een target and sensing node can be calculated based on
virtual potential theory and used to control the node to
rotate following with the target movement.
i
i
ir
r
kF 0
2
1
1 (1)
where 1
k is density of field; i r0 is a vector of unit
length and describes the direction of force from “cen-
troid” point of node i to target location; i
r describes
distance between “centroid” point of node i and target
location. When target is sensed by sensor i, sensor i
will rotate following with target moving under virtual
force coming from target, so sensor rotational speed is
decided by the target moving speed.
3.4. Selection of Sensor Node
When target moves at the boundary of the sense node,
selecting the next effective sense node is very important
to target tracking.
● Formation of Intersection.
When the target moves at the warning round of the
sense node, the intersection point of warning round and
motion trajectory forms the intersection of sense node, as
shown in Figure 3. Where, the bigger sphere shown with
mesh grid describes possible sense round of sensor i,
the smaller solid sphere is warning round, black line MN
describes target motion trajectory, A is the intersection of
sense node i.
● Setting area of Information Gain.
In this paper, information is the covered length of mo-
tion trajectory that is uncovered before, gain is increasing
and describes information increasing, so the area of in-
formation gain is the area where uncovered motion tra-
jectory is covered by next sense node, that is explained in
Figure 4, where MA is the target moving direction at
Figure 3. Intersection of sense node.

J. ZHAO ET AL.
700
Figure 4. Information gain area.
intersection A, PQ is vertical plane of MA and through
intersection A, neighboring area of intersection A is di-
vided equally, the half sphere POQ pointed at by MA is
the area of information gain. The area is a estimation
area where the sensor the most likely covers coming mo-
tion trajectory.
● Calculation of Information Gain and Selection of Next
Sense Node.
In real monitor applications, the scheduling of sensing
nodes, i.e., awaking as less nodes as possible to monitor
the target gets inevitable, so for getting the maximum
information gain , the next sense node is so far so good
from point A and the distance from the next sense node
to Linear AE is as close as possible, that is explained
with ichnography in Figure 5, where X is random sensor
in information gain half sphere, XY describes the dis-
tance from X to Linear AE, AX is the distance from X to
intersection A, information gain is determined by AX
and XY, shown as follows:
2
AX
inf XY k (2)
4. Simulation Results
The Simulation Results of controlling algorithm and the
effect of sensor parameters on coverage ratio is given in
this section.
4.1. Simulation Results of the SATTT
To simulate the algorithm, 20 isomorphic sensors with
sensing radius 60 m and separation angle 
45 of sens-
ing cone are deployed randomly in a region of 500 × 500
3
500m. Entering this specific three dimensional area
randomly, the target will move randomly till living it.
The simulation results are shown in Figures 6-7, where,
Figure 6 shows the initial sensors deployment in the
region and little spheres show location of sensors; Fig.7
shows the coverage ratio of target motion trajectory,
where the target enter the area from A and leaves from B
after randomly moving in the three dimensional area, and
spheres show awaked sensors for sensing target, the red
line with stare dots is target motion trajectory whose
coverage ratio is %26.32.
When 200 isomorphic sensors are deployed randomly,
the simulation results are shown in Figures 8-9, cover-
age ratio is %100 .
4.2. Effects of Sensor Number on Coverage Ratio
To simulate the effect of sensor number on coverage
ratio, sensors with sensing radius 60m and separation
angle 
45 of sensing sector are deployed randomly in a
Figure 5. Forecast of sense node.
X
Y
Z
X
Y
Z
400
300
200
100
0
Y
Z
500
500
400
400
300 300
200 200
100
500
00
100
X
Figure 6. Initial deployment of 20 sensors.

J. ZHAO ET AL.
701
X
Y
Z
A
B
X
Y
Z
X
Y
Z
A
B
500
400
300
200
100
0
X
Y
Z
500
500
400
400300 300
200 200
100 100
00
Figure 7. Node scheduling of 20 sensors, p = 32.26\%.
X
Y
Z
X
Y
Z
500
400
300
200
100
0
X
Y
Z
0 0
100 100
200 200
300 300
400
400
500
500
Figure 8. Initial deployment of 200 sensors.
X
Y
Z
A
B
X
Y
Z
A
B
500
400
300
200
100
100
200
300
400
500
0
00
X
Y
Z
100 200 300 400 500
Figure 9. Node scheduling of 200 sensors, p = 100\%.
region of 500 × 500 × 500 m3. We provide the mean re-
sults of many simulations with 50, 100, 150 and 200 sen-
sors in Figure 10. It shows that as the number of nodes
increases, the coverage ratio of motion trajectory in-
crease too.
4.3. Effects of Sense Radius on Coverage Ratios
To simulate the effect of sense radius on coverage ratios,
200 sensors with separation angle 
45 of sensing sector
are deployed randomly in a region of 500 × 500 × 500 m3.
We provide the mean results of simulations with sensing
radius 60 m, 80 m and 100 m in Figure 11. It shows that
the coverage ratio of motion trajectory increases follow-
ing with increasing of nodes sensing radius and nearly
reaches p = 100% when radius is 100.
Effects of sensor number on coverage ratio Effects of sensor number on coverage ratio
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Coverage ratio of targetCoverage ratio of target
50 100 150 20
0
Number of sensorNumber of sensor
Figure 10. Effects of sensor number on coverage ratio.
Effects of sense radius on coverage ratios Effects of sense radius on coverage ratios
Coverage ratio of targetCoverage ratio of target
Sense radius of sensorSense radius of sensor
1
0.95
0.8
0.7
0.6
0.5
506570758085 90 95100
0.9
0.85
0.75
0.65
0.55
Figure 11. Effects of sense radius on coverage ratio.

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5. Conclusions
Concentrating on node controlling, the authors put for-
ward an algorithm for three dimensional target tracking
in wireless multi-sensor networks. The corresponding al-
gorithm working depends on the virtual potential method.
To monitor target dynamically by controlling nodes,
we structure virtual force between moving target and the
current sense node, then select the next sense node with
information gain function so that as few nodes as possi-
ble are used to get the maximum probability of motion
trajectory sensing when target randomly move in the spe-
cific three dimensional area till target living it. The pro-
posed algorithm is verified from the simulations.
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