Paper Menu >>
Journal Menu >>
Wireless Sensor Network, 2011, 3, 121-124
doi:10.4236/wsn.2011.34014 Published Online April 2011 (http://www.SciRP.org/journal/wsn)
Copyright © 2011 SciRes. WSN
Classification of Object Tracking Techniques in W ir eless
Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan
Received February 19, 2011; revised March 9, 2011; accepted March 15, 2011
Object tracking is one of the killer applications for wireless sensor networks (WSN) in which the network of
wireless sensors is assigned the task of tracking a particular object. The network employs the object tracking
techniques to continuously report the position of the object in terms of Cartesian coordinates to a sink node
or to a central base station. A family tree of object tracking techniques has been prepared.In this paper we
have summarized the object tracking techniques available so far in wireless sensor networks.
Keywords: Object Tracking in WSN, Classification of Object Tracking Techniques in WSN
The techniques are mainly classified based on (see Fig-
- Network architecture used
- Algorithm or technique used
- Type of sensors used
- Number of targets to be tracked.
- Technology used for implementation.
Each of these categories has been described carefully
in section 2, 3, 4, 5 and 6 respectively.
2. Network Architecture
The following are the main types of network architectures
that can be used for object tracking in sensor networks as
shown in Figure 2.
Cluster based architecture.
Tree based architecture.
2.1. Cluster Based Architecture
In cluster based architecture there are several sensor
nodes and for a certain group of nodes, they are assigned
a cluster leader or cluster head. The ordinary nodes just
sense the reading and send them to the cluster thereby
shifting the burden from them to the cluster head. The
cluster head is normally a high energy and high resource
node. The introduction of cluster heads can reduce suffi-
cient cost of network as one can deploy low cost low en-
ergy sensor nodes .
The cluster based architecture of wireless sensor net-
works can also be classified as
- Static clustering
- Dynamic clustering
Tracking in WSN
Figure 1. Basic classification of tracking techniques in
Copyright © 2011 SciRes. WSN
Figure 2. Further classification of techniques on the basis of
network architecture used.
2.1.1. Static C l usteri ng
In static clustering the cluster heads are assigned to the re-
spective sensor nodes at the time of formation of network
and they can not be changed. This means that throughout
the working of wireless sensor network the nodes remain
attach to the same cluster head as they were pre assigned
2.1.2. Dynamic Clustering
Again the dynamic clustering scheme can be of two types
- Prediction based or Proactive clustering.
- Non prediction based.
188.8.131.52. Prediction Based or Proactive Clustering
This scheme is mostly employed in a network of sleep
sensors, where most of the sensors stay in the sleep mode.
In prediction based clustering when a target moves from
the region of one cluster head to the other, the current
cluster head has to make an estimation or prediction about
where the target is moving and correspondingly wakeup
a cluster head where the target is moving [1,3].
184.108.40.206. Non Prediction Based Clustering
This is similar to the scheme described under the heading
of dynamic clustering. This scheme is used in a network
of non sleep sensors. Here the energy saving is not an
issue instead the proper selection and the life time of clus-
ter head is an issue. So based in some criteria based on
the application environment a cluster head selection al-
gorithm is run on each individual node, and the nodes co-
llaboratively select the cluster head.
2.2. Decentralized Architecture
In the decentralized architecture, there is no cluster head
type of central entity in the network and in this case all
the network nodes are at the same level in terms of work
responsibility. So the information regarding the target lo-
calization travels through the network to a central base
station that is not the part of wireless sensor network. That
base station can be a computer or some other computa-
tion entity. It runs an algorithm through which it can es-
timate the current location of the target .
2.3. Tree Based Architecture
In this case a tree structure is maintained across the net-
work. The tree is rooted at the node that is closest to the
target. Thus as the target moves some nodes get added to
the tree and some get deleted. This scheme reduces the
overhead in terms of energy and information flow, as the
information flows from the root to the end or periphery
of the network through a particular route, as the informa-
tion flows is controlled so energy consumption automat-
ically gets controlled [5-9].
3. Algorithm or Technique
According to algorithm or technique used the tracking te-
chniques can be classified as (see Figure 3)
- For the network of sleepy sensors.
- Target reporting.
- Target chasing.
For the network of sleepy sensors there is a further clas-
sification based on whether prediction heuristics are used
or sensor scheduling is used.
3.1. For the Network of Sleep Sensors
As all the sensors are in sleep mode. Thus there needs to
be a mechanism through which the sensors can be woken
up when the target approaches in the region of sleepy sen-
sors. To wake up either of the methods can be used:
- Prediction heuristics.
- Sensor scheduling.
In the network of
reporting Target chasing
Figure 3. Classification of tracking techniques in WSN ac-
cording to algorithm or technique used.
Copyright © 2011 SciRes. WSN
3.1.1. Prediction Heuristics
In this case most of sensors stay in sleep mode. The cur-
rent node predicts the future movement and location of
the target and correspondingly wakes up the nodes in the
region where the target is moving. The critical perfor-
mance parameters for prediction based reporting are the
miss rate and energy consumption. The network or the re-
gions where the current wireless sensor nodes were ac-
The heuristics reflect the prediction model that can be
used for the prediction based object tracking in WSN.
The prediction heuristics that can be used can either of
In this heuristic it is assumed that the target’s future
speed and direction will be the same as it is currently
Here it is assumed that the future direction and move-
ment of the target will be equal to the average of pre-
vious direction and movement.
Here also the average of past readings is carried out
except it is exponential weighted average, which means
that more weight is assigned to near future reading than
the far future readings .
3.1.2. Sensor Schedulin g
Instead of incorporating prediction based scheme sensors
can be scheduled for their wakeup and sleep time.
In this scheme it is to be determined that which sen-
sors stay in awake over the time in order to have an ap-
propriate trade off between the tracking performance and
the overall sensor usage. The objective is to minimize the
estimation errors while still reducing the sensor usage
over a period of time .
3.2. Target Reporting
In target reporting only the information target location in
terms of Cartesian coordinates is sent to a sink node or
some central entity. Thus the data continuously travels th-
rough the network. Thus the main task here is to device
efficient routing and target location calculation tecni-
ques in order to minimize the overall energy consump-
tion by the network and minimizing the tracking error
3.3. Target Chasing
In target node the sink node has to physically follow the
target. Thus the sink node has to continuously consult the
neighboring nodes and the information of target has to be
disseminated in the network for sink to follow the target
4. Type of Sensors
The tracking techniques are widely different depending
on the type of sensors used: (see Figure 4)
- Ordinary sensors.
- Binary sensors.
4.1. Ordinary Sensors
The ordinary sensor network consists of the type of sen-
sor nodes that operate on original values of signals. Thus
the distance, speed and direction of target have to be cal-
culated on the basis of signal strength measured by the
4.2. Binary Sensors
The binary sensors work only on two binary values.
They can just detect the presence or absence of the target
in their sensing range by signaling either by 1 or 0. Thus
the tracking mechanism in this case is more complicated
than ordinary sensor networks [4,12].
5. Number of Targets Used
The tracking technique can be either for single target
tracking or multiple target tracking. (see Figure 5)
5.1. Single Target Tracking
Tracking in single target is relatively simple. Less data is
produced that results in a low traffic in the network. Less
traffic is easier to handle and the routing mechanism is not
Type of sensors
Ordinary sensors Binary sensors
Figure 4. Classification of tracking techniques on the basis
of types of sensors.
Number of targets
Figure 5. Classification on the basis of number of targets
Copyright © 2011 SciRes. WSN
5.2. Multiple Target Tracking
In this case the location of multiple targets has to be track-
ed simultaneously. Increasing the number of targets to be
tracked increases the network traffic and thus more com-
plex routing schemes and energy minimization tecniques
have to be incorporated to compensate for the network
6. Technology Used for Implementation of
Tracking in Wireless Sensor Networks
Various technologies can be used for implementation of
tracking techniques in wireless sensor networks includ-
ing Zigbee, Bluetooth etc. Mostly the technology which
uses omni directional antennas can be used for the imple-
mentation of tracking in wireless sensor networks. This
is because the presence of target has to felt, what ever the
direction of motion of target is. The omni directional an-
tennas solve the purpose of tracking.
7. Conclusion and Results
● A wide range of technologies, network architectures
and types of sensors are available for tracking in wireless
● There is a performance tradeoff in energy, tracking
error and other performance parameters.
● To design a wireless sensor network for object tracking
or to do research in proposing new techniques the classi-
fication of techniques has to be kept in to mind with their
relative trade offs to achieve affective results.
 Y. Q. Xu, J. L. Winter and W.-C. Lee. “Prediction Based
Strategies for Energy Saving in Object Tracking Sensor
Networks,” Proceedings of IEEE International Confe-
rence on Mobile Data Management, Berkeley, 19-24
January 2004, pp. 346-357.
 H. Yang and B. Sikdar. “A Protocol for Tracking Mobile
Targets Using Sensor Networks,” Proceedings of IEEE
Workshop on Sensor Network Protocols and Applications
Anchogare, May 2003, pp. 71-81.
 E. Olule, G. J. Wang, M. Y. Guo and M. X. Dong,
“RARE: An Energy-Efficient Target Tracking Protocol
for Wireless Sensor Networks,” Prceedings of IEEE In-
ternational Conference on Parallel Processing Work-
shops, Xian, 10-14 September 2007, pp. 76-82.
 J. Aslam, Z. Butler et. al. “Tracking a Moving Object
with a Binary Sensor Network,” Proceedings of the 1st
International Conference on Embedded Networked Sen-
sor Systems, Los Angeles, 5-7 November 2003, pp.
 C.-Y. Lin, W. -S. Peng and Y.-C. Tseng, “Efficient In
Network Moving Object Tracking in Wireless Sensor
Networks,” IEEE Transactions on Mobile Computing,
Vol. 5, No. 8, August 2006, pp. 1044-1056.
 H. T. Kung and D. Vlah. “Efficient Location Tracking
Using Sensor Networks,” Proceedings of IEEE Wireless
Communications and Networking Conference, New
Orleans, Vol. 3, March 2003, pp. 1954-1961.
 W. S. Zhang and G. H. Cao. “DCTC: Dynamic Convoy
Tree-Based Collaboration for Target Tracking in Sensor
Networks,” IEEE Transactions on Wireless Communica-
tions, Vol. 3, No. 5, September 2004, pp. 1689-1701.
 C.-Y. Lin and Y.-C. Tseng. “Structures for In-Network
Moving Object Tracking in Wireless Sensor Networks,”
Proceedings of the 1st International Conference on
Broadband Networks, San Jose, 25-29 October 2004, pp.
 W. S Zhang, G. H. Cao. “MobiHoc Poster: Optimizing
Tree Reconfiguration to Track Mobile Targets in Sensor
Networks,” Mobile Computing and Communications Re-
view, Vol. 7, No. 3, July 2003, pp. 39-40.
 Mohsin Fayyaz. “Object Tracking in Wireless Sensor
Networkls,” MSc Project Report, Department of Elec-
tronic Engineering, Queen Mary University of London,
 T. He, S. Krishnamurthy, et. al. “Energy-Efficient Sur-
veillance System Using Wireless Sensor Networks,”
the 2nd ACM International Conference on
Mobile Systems, Applications and Services, Boston, 6-9
June 2004, pp. 270-283. 1-58113-793-1/04/0006
 N. Shrivastava, R. Mudumbai, et. al. “Target Tracking
with Binary Proximity Sensors: Fundamental Limits, Mi-
nimal Descriptions and Algorithms,” Proceedings of the
4th ACM International Conference on Embedded Net-
work Sensor Systems, Boulder, 1-3 November 2006, pp.
 S. Oh, L. Schenato, et. al. “A Hierarchical Mul-
tiple-Target Tracking Algorithm for Sensor Networks,”
Proceedings of IEEE International Conference on Ro-
botics and Automation, Barcelona, 18-22 April 2005, pp.