AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROUGH ZIGBEE WSNS

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 3, June 2013
DOI : 10.5121/ijwmn.2013.5305 61
AN IMPROVED DECENTRALIZED APPROACH FOR
TRACKING MULTIPLE MOBILE TARGETS
THROUGH ZIGBEE WSNS
Tareq Alhmiedat1
, Amer O. Abu Salem2
, Anas Abu Taleb3
1
Department of Computer Science, Zarqa University, Zarqa, Jordan
t.alhmiedat@zu.edu.jo
2
Department of Computer Information Systems, Zarqa University, Zarqa, Jordan
abusalem@zu.edu.jo
3
Department of Computer Science, Isra University, Amman, Jordan
anas.taleb@ipu.edu.jo
ABSTRACT
Target localization and tracking problems in WSNs have received considerable attention recently, driven
by the requirement to achieve high localization accuracy, with the minimum cost possible. In WSN based
tracking applications, it is critical to know the current location of any sensor node with the minimum
energy consumed. This paper focuses on the energy consumption issue in terms of communication
between nodes whenever the localization information is transmitted to a sink node. Tracking through
WSNs can be categorized into centralized and decentralized systems. Decentralized systems offer low
power consumption when deployed to track a small number of mobile targets compared to the centralized
tracking systems. However, in several applications, it is essential to position a large number of mobile
targets. In such applications, decentralized systems offer high power consumption, since the location of
each mobile target is required to be transmitted to a sink node, and this increases the power consumption
for the whole WSN. In this paper, we propose a power efficient decentralized approach for tracking a
large number of mobile targets while offering reasonable localization accuracy through ZigBee network.
KEYWORDS
Localization, Tracking, Decentralized, Wireless Sensor Networks, ZigBee
1. INTRODUCTION
In recent years, advances in signal processing have led to manufacturing small, low power,
inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from
the traditional wireless networks in two critical aspects: first, the signal processing in WSN is
performed in a fully distributed manner, unlike in traditional wireless networks. Second, due to
the limited computation capabilities of sensor networks, it is essential to develop an energy and
bandwidth efficient signal processing algorithms.
A sensor network is composed of sensor nodes which are small in size, low in cost, and have
short communication range. A sensor node usually consists of four sub-systems as follows:
1. Computing subsystem, which is responsible for functions such as execution of the
communication protocols and control of sensors
2. A sensing subsystem, that is responsible for sensing the environmental characteristics,
such as temperature and humidity

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3. A communication subsystem, this consists of a short radio range used to communicate
with neighbouring nodes
4. A power supply subsystem, which includes a battery source that provides energy to
sensor node.
WSN technology is exciting with unlimited potentials in various application areas including:
environmental, medical, military, transportation, homeland defence, crisis management,
entertainment, and smart spaces [1, 2, 3, 4, 5]. Researchers have focused on diverse aspects of
WSN, such as hardware design, routing, data aggregation and localization [6, 7]. Recently,
WSN-based localization and tracking issues have received much attention, driven by the need to
accomplish high localization accuracy with the minimum cost, this is because:
1. In many applications, the location itself is the information of interest,
2. Several routing protocols are based on the sensor nodes' locations,
3. Transferring sensors' measurements without incurring the sensors' locations is an
unproductive task.
The authors of this paper focused on diverse aspects of tracking mobile targets through
distributed sensor networks, such as the localization method [8], tracking multiple mobile
targets through ZigBee networks [9], and the data aggregation and prediction methods [10, 11].
In this paper, we focus on the communication between nodes when tracking a large number of
mobile targets through ZigBee WSNs, which further intends to reduce the power consumption
for WSNs.
WSNs based Localization systems have been researched and addressed extensively in several
works [12, 13, 14]. In real time tracking applications, it is essential to continuously transmit the
mobile targets' locations to a sink node, in order to display its current position online. Tracking
a large group of mobile targets through hundreds of sensor nodes requires the transmission of
the location for each mobile target from reference nodes to the sink node, which normally
results in a series of hops through the network. Each of these hops increases the consumption of
the limited energy, and therefore participates failures within the network as the energy of the
reference nodes becomes increasingly impaired.
In this paper, we categorize the tracking systems into centralized and decentralized. In
centralized tracking systems, localization information might be transmitted to a sink node to
obtain the localization information, while in decentralized systems; localization information is
obtained from each mobile target itself, and then transmitted to a sink node. Figure 1 depicts the
main idea for both systems (centralized and decentralized). As presented, assume a mobile
target is in the range of 4 reference nodes. In centralized systems, a total number of 4 reference
messages would be transmitted to the sink node, whereas in the decentralized systems, only a
single message that includes the current location of the mobile target, is transmitted to the sink
node, as processing the mobile target's location would take place at the reference or the mobile
target node itself.

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Computation
method
Computation
method
4 msgs received
each time
1 msg received
each time
Sensor Node Mobile Target
Central
Computer
Centralised Localisation Decentralised Localisation
Figure 1. Centralized vs. decentralized localization methods
Tracking a few number of mobile targets through a decentralized approach offers low power
consumption [10]. However, when it comes to tracking a large number of mobile targets
through distributed sensor nodes, then high power consumption is expected. For that reason, this
paper presents a new efficient tracking system, which basically reduces the power consumption
required to track multiple mobile targets through ZigBee WSNs. In this paper, we will refer to
the following terms:
- Reference node (r): the node with fixed known 2-d coordinates,
- Mobile target (t): the node with unknown location, which requires estimating the
location of itself,
- Group leader (g): the mobile target node with the minimum number of hops to a sink
node is selected to be a group leader in order to aggregate the localization information
obtained from adjacent mobile targets, and transmit to a sink node.
In this paper, our contribution lies on the following aspects:
1. Study the existing tracking systems, and categorize them into: centralized and
decentralized systems,
2. Design, develop, and implement a power efficient decentralized tracking system using
ZigBee WSNs, which intends to minimize the power consumption when tracking a
large number of mobile targets,
3. Evaluate the proposed system in this paper, and compare it with the existing systems
proposed recently.

64
2. RELATED WORK
In this section, we summarize the existing centralized and decentralized tracking systems. First,
we start with centralized tracking systems. According to [15], centralized based systems have
two advantages over the decentralized ones: first, reference nodes are not required to have
processing capabilities. Second, the irregularity and mobility of the mobile target requires
frequent data communications among reference nodes to have good localization accuracy,
which destroys the main advantage of decentralized localization systems.
The works presented in [16, 17, 18] include a RSS based localization system through
centralized communication among sensor nodes. In such systems, localization information is
transmitted to a sink node in order to process and compute the localization coordinates for each
mobile target at the sink node. The work presented in [19] investigated the performance of
centralized cooperative positioning algorithm, in which all information has to be collected at a
central entity for positioning target nodes. The system proposed in [20] includes a novel
computing architecture for WSNs to overcome the Kalman Filter drawbacks, which divides the
positioning system into three components: measurement, pre-processing, and data processing.
Second, decentralized or distributed localization techniques; which include that each sensor
node is responsible for determining its location with only limited communication with nearby
nodes. Decentralized systems have been widely used due to the low energy required compared
to the centralized systems. However, this kind of systems requires attaching a localization and
computation mechanism to each reference node. In [21], a hybrid technique called RObust
Position Estimation (ROPE) was proposed, which allows sensors to locate their locations with
no need to a centralized computation facility. ROPE provides a location verification mechanism
that verifies the location claims of the sensors before data collection. Thus, ROPE allows the
sensors to estimate their own location without the assistance of a central authority.
The system proposed in [22] involves a MDS-MAP technique for calculating the positions of
nodes with only basic information that is likely to be already available. MDS-MAP technique
involves starting with the given network connectivity information, and an all-pairs shortest-
paths algorithm is used to assess the distance between each possible pair of nodes. A
decentralized tracking system based on a camera sensor device is proposed in [23] which
intended to reduce the bandwidth requirements. The images captured are required to be
processed at each camera sensor node with the objective of the extracting location of mobile
targets. The proposed approaches in [24, 25, 26] are decentralized based localization systems,
since all the communication and processing are undertaken in the sensor node itself.
3. AN IMPROVED DECENTRALIZED TRACKING APPROACH
In WSN systems, long lifetime requirement of sensor nodes has led us to find out new horizons
for reducing the power consumption upon nodes. Consuming less energy in tracking
applications is a primary objective in designing WSN systems, as each sensor node is usually
supported by batteries which could be difficult to replace. The proposed decentralized approach
includes grouping the mobile targets in a given area, and selecting a group leader for each group
of mobile targets. The group leader is responsible for:
1. Collecting localization information from other mobile targets in its range,
2. Aggregating and transmitting the received localization information to a sink node.

65
The proposed system in this paper is based on decentralized localization computation. Thus,
localization information is processed at the mobile target device, in which each mobile target
has the capability to compute its current location using the system proposed in [6]. Only the
final coordinates are transmitted to a sink node. This reduces the amount of messages
transmitted over the network, and hence shrinks the power-consumption for each reference
node. In this section, we discuss the proposed decentralized tracking system, which consists of 3
main phases; initialization, localization, and grouping. Figure 2 depicts the idea of the improved
decentralized approach.
Reference node Mobile Target Central Computer
Figure 2. The main idea of the improved decentralized approach
3.1. Initialization phase
This phase includes starting the network, and assigning a network address to reference nodes in
the tracking area of interest. Then, mobile targets enter the tracking area and obtain network
addresses. Afterwards, each mobile target checks the reference nodes in its range, in order to
position itself when required.
3.2. Localization phase
In this phase, each mobile target localizes itself based on the RSS values obtained from adjacent
reference nodes. The localization approach proposed in [6] is deployed as a localization method
to estimate the mobile targets’ locations. This process is repeated every f seconds to estimate the
mobile targets’ locations simultaneously.
3.3. Grouping phase
This phase involves selecting a group leader for the mobile targets set in the same transmission
range. The main reasons behind choosing a group leader is to reduce the amount of messages
transmitted to the sink node over the WSN, and hence conserving the amount of energy
consumed during the tracking process.

66
To illustrate this phase, assume { }crrrrR ...,,,, 321= represents the reference nodes in the tracking
area of interest, with total number c, and { }ettttT ...,,,, 321= represents the mobile targets with
total number e. Assume that { }ztttS ...,,, 21= is a set of mobile targets in a given area set within
the same transmission range, with a total number z, and each mobile target t is covered by at
least three reference nodes to triangulate its current position. Each mobile target checks the
number of hops between itself and the sink node. The mobile target with the least number of
hops to the sink node is elected to be a group leader. The group leader should have the
following characteristics:
1. It has the minimum number of hops to the sink node,
2. It has a reasonable amount of energy, over a certain threshold.
The group leader receives the localization information from other mobile targets in its range,
then aggregates, and transmits this information to the sink node. The flowchart for the
algorithms running on a mobile target, and the group leader nodes are presented in Figures 3
and 4, respectively.
Figure 3. The algorithm implemented on each mobile target node

67
Figure 4. The algorithm implemented on the group leader node
4. SIMULATION EXPERIMENTS
The proposed system is evaluated through implementation using NS2 simulator environment. In
order to evaluate the proposed approach in this paper, a total number of three different
approaches have been implemented and tested. First, a centralized localization approach, which
includes transmitting the localization information from each reference node covering the mobile
target to a sink node. Second, a decentralized tracking system, this includes that each mobile
target estimates its current position and transmits it to a sink node. And third, an improved
decentralized approach, which involves selecting a group leader which placed close to the sink
node in order to transmit the localization information through the minimum number of hops.
The system proposed in this work was implemented using ZigBee network standard. ZigBee is a
low power consumption, low data rate, and low cost wireless communication standard designed
to be used in home automation and remote control applications. ZigBee network standard
performs three roles: coordinator, router, and end-device. Only one ZigBee coordinator is
required for each WSN, it starts a network formation. ZigBee router is an optional network
component; it participates in multi-hop routing of messages. While ZigBee end-device is
utilized for low power operation, and is not allowed to participate neither in association nor

68
routing. In this project, we used a ZigBee coordinator as a sink node and a number of ZigBee
routers as reference and mobile target nodes.
4.1. Reference and mobile target platform
There are two different types of devices in our test-bed: reference nodes and mobile targets.
Reference nodes include stationary sensor nodes with known positions distributed over the
tracking area of interest, whereas the mobile targets have the ability to localize themselves.
Both, reference and mobile targets were considered as router nodes. Table 1 presents simulation
parameters used in our simulation experimental test-bed.
Table 1. Simulation parameters
Parameter name Parameter value
Packet size 127 bytes
InitEng 27.00 mA.h
rxPower 49.00 mA.h
txPower 44.00 mA.h
Simulation time 360 seconds
Number of nodes 56 nodes
Number of mobile targets 10
Average hops 5
Radio model TwoRayGround
Antenna type OmniAntenna
Grid size 75 x 65 m2
Routing protocol AODV
Mac protocol MAC/802.15.4
4.2. Reference and mobile target platform
Our experiment test-bed consists of 56 reference nodes distributed over the tracking area of
interest, and a sink node placed on the right corner as depicted in Figure 5.
Figure 5. The experimental test-bed

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5. SYSTEM EVALUATION
In this section, three scenarios are examined (centralized, decentralized, and improved
decentralized), based on evaluating two performance metrics: the total number of messages
transmitted over the network, and the power-consumption.
5.1. Communication cost
One of the critical issues in developing a tracking system for sensor nodes is the issue of battery
lifetime. Sensor devices are designed to work for periods of several months to a few years. The
system proposed in this paper aims to minimize the total number of messages transmitted over
the network in order to reduce the amount of energy consumed. In this section, we evaluate the
total number of messages transmitted to the sink node for the three different scenarios discussed
above (centralized, decentralized, and improved decentralized).
In centralized localization approach, every reference node senses the mobile target has to
transmit its readings to a sink node. The total number of messages transmitted over the network
is represented in Equation 1.
f
lmrn ××=1 (1)
where r, m, l and f refer to the number of reference nodes, the number of mobile targets located
in one transmission range, total experiment time, and frequency rate (how often the mobiles'
locations have to reach the sink node) respectively. For instance, assume the total experiment
time is l = 60 seconds, the number of reference nodes r = 3, and the number of mobile targets is
m = 10, then the total number of messages transmitted to the sink node is n1 = 900 (where f = 2
seconds).
On the other hand, in the basic decentralized approach, two types of messages are transmitted
over the network. First, the messages transmitted between reference nodes and mobile targets
represented in Equation 1. Second, the messages transmitted between mobile targets and a sink
node, as represented in Equation 2.
f
lmn ×=2 (2)
As in the previous example, consider the total experiment time is l = 60 seconds, the number of
reference nodes r = 3, and the total number of mobile targets is m = 10. Then the average
number of local messages will be n2 = 900. These messages are not required to reach a sink
node, since they are required to be exchanged between reference nodes and mobile targets.
Whereas the total number of messages sent to the sink node is 300 messages. Consequently,
there is a significant improvement in the decentralized over centralized approaches.
The improved decentralized approach proposed in this paper, deals with three types of messages
transmitted over the network, are as follows:
1. Local messages: indicates messages exchanged between each mobile target and
reference nodes, which represented in Equation 1.
2. Group messages: represents messages transmitted between the group leader and other
mobile targets in its range. This kind of messages does not affect the energy of
reference nodes (wireless sensor nodes), as communications are processed between the
mobile targets themselves, which represented in Equation 3.
3. Global messages: represents total number of messages transmitted between the group
leader and the sink node, as represented in Equation 4.

70
( ) f
lmn ×−= 13 (3)
f
lm
n ×=
5
4 (4)
In equation 4, the total number of messages is divided by 5, as each packet could contain the
locations and network addresses for a maximum number of 5 mobile targets. For the same
example mentioned above, assume the total experiment time is l = 60 seconds, the number of
reference nodes r = 3, and the number of mobile targets is m = 10. Then the total number of
local messages is n1 = 600, the total number of group messages is n3 = 180, and the total
number of global messages is n4 = 40.
As discussed above, the improved decentralized approach does not require a large number of
messages to be exchanged between mobile targets and reference nodes, even when a large
number of mobile targets are required to be positioned. Calculations are taken place at the
mobile target side, with no need for centralized computations. Figure 6 shows a comparison of
the messages required to be transmitted among the three scenarios. The centralized approach
requires transmitting a high number of messages between reference and sink nodes, whereas the
implemented decentralized approach requires exchanging less messages when the total number
of mobile targets is small. At the meanwhile, the improved decentralized approach achieves the
least number of messages required to be transmitted over the network mainly when the density
of mobile targets is high.
Figure 6. Total number of messages for the three systems
In this work, we aimed to reduce the amount of messages exchanged between wireless sensor
nodes in the tracking area of interest. Figure 7 presents the total number of messages exchanged
between mobile targets in the three scenarios. As presented, the improved decentralized
approach achieves a few more number of messages than centralized and decentralized systems.
Numberofmessages
Time (seconds)
centralized
decentralized
improved decent.

71
Figure 7. Total number of messages exchanged between mobile targets for the three systems
5.2. Power-consumption
The previous section evaluates and compares the total number of transmitted messages for the
three scenarios. Each transmitted message requires a particular amount of power. In this section,
the power-consumption for each approach is evaluated. The power consumption for the
centralized cnE , decentralized dcE and improved decentralized impE represented in the
Equations 8, 9, and 10, respectively.
ܲሺ݊ሻ ൌ ሺ݊ ൈ ܲ௧௫ሻ ൅ ሺ݊ ൈ ܲ௥௫ሻ (7)
‫ܧ‬௖௡ ൌ ܲሺ݊ଵሻ ൈ ݄ (8)
‫ܧ‬ௗ௖ ൌ ൫ܲሺ݊ଵሻ ൅ ܲሺ݊ଶሻ൯ ൈ ݄ (9)
‫ܧ‬௜௠௣ ൌ ሺܲሺ݊ଵሻ ൅ ܲሺ݊ସሻ ሻ ൈ ݄ (10)
where P(n) is the energy required to transmit a number of packets n, Ptx is the amount of energy
required to transmit a single packet, Prx is the amount of energy required to receive a single
packet, and h is the number of hops. Figure 8 compares the battery life of the three approaches
(centralized, decentralized, and improved decentralized). As shown, the improved decentralized
method achieves longer battery life than both the centralized and decentralized approaches, as it
requires less number of messages to be exchanged between mobile targets and a sink node.
In Figure 9, the power consumption for the group of mobile targets (m = 10) are estimated in
three systems (centralized, decentralized and improved decentralized). As shown, the improved
decentralized system achieves the highest power consumption. However, there is a slight
difference in power consumption between the improved decentralized, centralized and
decentralized systems. The mobile target devices are easy to maintain in case if any target
device's battery has run out.
Numberofmessages
Time (seconds)
Mob_Centralized
Mob_decentralized
Mob_improved centralized

72
Figure 8. Evaluating the battery life for the reference nodes in the three systems
Figure 9. Evaluating the battery life for the mobile target nodes in the three systems
6. DISCUSSION
In WSN, energy has three main consumers: signal processing, data transmission, and hardware
operations. In this research work, we aim to reduce the power consumption for both signal
processing and data transmission. In the improved decentralized approach, reference nodes are
not required to process the mobile targets' locations. Moreover, the total number of messages
transmitted over the network is reduced.
Centralized based localization approaches [16, 17, 18] are much more power consuming than
decentralized approaches [21, 22, 24, 25], since in centralized systems, usually the following
details are required to be transmitted to a sink node: sensor id, target id, packet number and
sensor-to-target distance. Therefore, deploying centralized systems with a large density of
sensor nodes requires high power-consumption, as each localization message must be
transmitted to a sink node via multi-hop network.
Powerconsumption(mA)
Time (seconds)
Centralized
Decentralized
Improved Decent.
Powerconsumption(mA)
Time (seconds)
Mob_centralized
Mob_decentralized
Mob_improved decentralized

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Decentralized tracking solutions are more attractive for large sensor networks including
thousands of nodes, as there is no need to process the localization information at a sink node
side, since all the computations are processed at each mobile target, and therefore the final
coordinates are only requested to be transmitted to a sink node. The proposed decentralized
systems in [21, 22, 24, 25] are efficient for tracking a single or a few number of mobile targets.
Though, these systems are impractical in terms of power consumption to track a large number
of mobile targets, as this requires a huge number of messages to be exchanged between mobile
targets and the sink node, and this will increase both the power consumption and messages
dropping rate. For both systems (centralized and decentralized), increasing the number of
mobile targets will increase the number of transmitted messages to the sink node.
An improved decentralized approach is proposed in this paper in order to shrink the total
number of messages transmitted over the network and hence decrease the power-consumption.
In this system, reference nodes are not required to have high processing capabilities, since
computations are taking place at each mobile target node. Grouping mobile targets which
set/placed in the same transmission range will increase the tracking efficiency in terms of power
consumption.
The proposed improved decentralized tracking system achieves low power consumption, and is
applicable to track several mobile targets simultaneously with the minimum communication
cost, in such applications necessitate tracking a large number of mobile targets. The system
proposed in this paper can be deployed in different types of applications including: tracking a
large number of employees in a workplace, tracking passengers in an airport, and tracking fire-
fighters in hazard situations.
7. PAPER SUMMARY
One of the critical technical issues which must be addressed in developing sensor networks for
object tracking applications is energy conservation. In this paper, a decentralized tracking
approach is proposed to track the location of multiple mobile targets with minimum
communication cost among sensor nodes. The proposed approach achieves an efficient tracking
system in terms of localization and power-consumption by grouping the mobile targets which
set in the same transmission range.
The proposed system aims to increase the lifetime of reference nodes by reducing the total
amount of messages transmitted over the network. The mobile target itself requires processing
its localization and transmitting it to a group leader. Therefore, it reduces the total number of
messages transmitted to the sink node, and consequently reduces the total amount of energy
consumed in the tracking process. Performance evaluation in terms of communication cost and
power-consumption was conducted using NS2 simulator.
In this work, all of the reference nodes were considered as router nodes, which means that
reference nodes have to be awake all the times, and this increase the power consumption for
such WSN based tracking systems. For future work, we aim to involve the end-device nodes in
the tracking process in order to minimize the power consumption, while achieving reasonable
localization accuracy.

74
REFERENCES
[1] Hamdi, M., Boudriga, N., Obaidat, M. S. (2008) “ WHOMoVeS: An optimized broadband
sensor network for military vehicle tracking”, International Journal of Communication Systems,
Vol. 21, Issue 3, pp. 277-300, ISSN:1074-5351.
[2] Son, B.; Her, Y.; Kim, J. (2006) “A design and implementation of forest-fires surveillance
system based on wireless sensor networks for South Korea Mountains”. Int. J. Comput. Sci.
Netw. Secur.(IJCSNS) 6, 124–130.
[3] Antoine-Santoni, T., Santucci, J., Gentili, E., De; Silvani, X., Morandini, F. (2009)
“Performance of a protected wireless sensor network in a fire”. Analysis of fire spread and data
transmission. Sens. J. 9, 5878-5893
[4] Lloret, J., Garcia, M., Bri, D. and Sendra. S. (2009) “A Wireless Sensor Network Deployment
for Rural and Forest Fire Detection and Verification. Sensors”. Vol. 9 Issue: 11. pp. 8722-8747.
[5] Resch, B., Mittlboeck, M., Girardin, F., Britter, R. and Ratti, C. (2009) “Real-time Geo-
awareness - Sensor Data Integration for Environmental Monitoring in the City”. In Proceedings
of the IARIA International Conference on Advanced Geographic Information Systems & Web
Services – GEOWS2009, Cancun, Mexico, pp. 92-97.
[6] Abusaimeh, H, and Shuang-Hua Y. (2009) "Reducing the transmission and reception powers in
the AODV." Networking, Sensing and Control, 2009. ICNSC'09. International Conference on.
IEEE.
[7] Abu Taleb, A, Dhiraj K. P, and Taskin K. (2009) "A Technique to Identify and Substitute Faulty
Nodes in Wireless Sensor Networks." Sensor Technologies and Applications,
SENSORCOMM'09. Third International Conference on. IEEE.
[8] Alhmiedat, T. A., and Yang, S. H. (2008) “A ZigBee-based mobile tracking system through
wireless sensor networks”. International Journal of Advanced Mechatronic Systems, 1(1), 63-70.
[9] Alhmiedat, T. A. and Yang, S., (2009) “Tracking Multiple Mobile Targets Based on ZigBee
Standard”, Proceedings of the 35th
Annual Conference of the IEEE Industrial Electronics
Society, Porto, Portugal.
[10] Alhmiedat, T. A. and Yang, S., (2008) “A Decentralized Low-Power ZigBee-based Localization
Approach”, Proceedings of the 14th
International Conference on Automation & Computing,
Brunel University, Uxbridge, UK, pp. 41.
[11] Alhmiedat, T. A., & Yang, S. H. (2011) “An improved energy efficient approach for wsn based
tracking applications”. Proceedings of the 5th
IADIS Multi Conference on Computer Science and
Information Systems, Rome, Italy.
[12] Amundson, I, and Xenofon K. (2009) "A survey on localization for mobile wireless sensor
networks." Mobile Entity Localization and Tracking in GPS-less Environnments, 235-254.
[13] Alhmiedat, T. Abu Taleb, A. and Bsoul, M. (2012) “A Study on Threats Detection and Tracking
for Military Applications Using WSNs”, International Journal of Computer Applications
40(15):12-18.
[14] Alhmiedat, T. A. and Yang, S., (2007) “A Survey: Localization and Tracking Mobile Targets
Through Wireless Sensor Network”, Proceedings of the PGNet Conference, Liverpool, UK, pp.
48.
[15] Benavoli, A., and Chisci, L. (2007) “Improved estimation for object localization via sensor
networks”, Networking, Sensing and Control, IEEE International Conference, pp. 302-306,.
[16] Alippi, C., and Vanini, G. (2006) “A RSSI based and calibrated centralized localization
technique for Wireless Sensor Networks”, in Proc. of IEEE International Conference on
Pervasive Computing and Communications (PerCom), Pisa, Italy, pp. 301–305.
[17] Sugano, M., Kawzoe, T., Ohta, Y., and Murata, M. (2006) “Indoor Localization System using
RSSI Measurement of Wireless Sensor Network based on ZigBee Standard.

75
[18] Doherty, L., Pister, K. S. J., and El Ghaoui, L, (2001) “Convex Position Estimation in Wireless
Sensor Networks,” IEEE INFOCOM, vol.3, pp. 1655-1663.
[19] Mensing, C, and Jimmy, N. (2010) "Centralized cooperative positioning and tracking with
realistic communications constraints." Positioning Navigation and Communication (WPNC),
2010 7th
Workshop on. IEEE.
[20] Zhao, Y, Yuan Y, and Marcel K, (2011) "Comparing centralized Kalman filter schemes for
indoor positioning in wireless sensor network", Indoor Positioning and Indoor Navigation
(IPIN), 2011 International Conference on. IEEE.
[21] Lazos, L., Poovendran, R., and Capkun, S. (2005) “ROPE: robust position estimation in wireless
sensor networks,” Fourth International Symposium on Information Processing in Sensor
Networks (IEEE Cat. No. 05EX1086), pp. 31.
[22] Shang, Y., Fromherz , M. P. J., Ruml, W., and Zhang, Y. (2003) “Loclization from mere
connectivity,” Proceedings of the International Symposium on Mobile Ad Hoc Networking and
Computing (MobiHoc), pp. 201-212.
[23] Sanchez-Matamoros, J. M., JRM-D. Dios, and A. Ollero. (2009) "Cooperative localization and
tracking with a camera-based WSN." Mechatronics, 2009. ICM 2009. IEEE International
Conference on IEEE.
[24] Bulusu, N., Bychkovskiy, V., Estrin, D., and Heidemann, J. (2002) “Scalable, ad hoc deployable
rf based localization”, Grace Hopper Celebration of Women in Computing Conf, Vancouver,
British Columbia, Canada.
[25] He, T., Stankovic, J. A., Huang, C., Abdelzaher, T., and Blum, B. M. (2003) "Range-Free
Localization Schemes for Large Scale Sensor Networks," Proceedings of the Annual
International Conference on Mobile Computing and Networking, MOBICOM, pp. 81-95.
[26] Cakiroglu, A, and Cesim E. (2010) "Fully decentralized and collaborative multilateration
primitives for uniquely localizing WSNs." EURASIP Journal on Wireless Communications and
Networking, Vol. 2010, Article ID: 605658,
Authors
Dr. Tareq Alhmiedat: is a professor assistant in Computer Science
Department at Zarqa University. He received a Ph.D. in Computer
Science from Loughborough University, Loughborough, UK, 2009. MSc
in Software Engineering from the University of the West of England,
Bristol, UK, 2006, and a BSc degree from Applied Science University,
2004. His research interests including tracking mobile targets through
Wireless Sensor Networks, Robotic Systems, and Home automation and
remote control application.
Dr. Amer O. Abu Salem: is a professor assistant in the department of
Computer Information Systems at the Faculty of Information Technology
in Zarqa University. He received his Ph.D in Computer Science from
Anglia University (UK, 2010), M.Sc. in Computer Science from Al-Bayet
University (Jordan, 2004), and BS.c. in Computer Science from Al-Bayet
University (Jordan, 1998). Dr. Abu Salem’s main research interests lie
with the design and implementation of programming languages, mobile
system technology, Wired and wireless communications, and program
semantics.

76
Dr. Anas Abu Taleb: is a professor assistant in the department of Computer
Science at Isra University, Amman, Jordan. He received a Ph.D. in
Computer Science from Bristol University, UK, 2010, MS.c. in Computer
Science from University of the West of England, UK, 2007, and BS.c.
degree in Computer Science from Prince Sumaya University for
Technology, Jordan, 2004. Dr. Abu Taleb has published several journal
and conference papers in sensor networks. In addition to sensor networks,
Dr. Abu Taleb is interested in network fault tolerance, routing algorithms,
and cloud computing.

AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROUGH ZIGBEE WSNS

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AN IMPROVED DECENTRALIZED APPROACH FOR TRACKING MULTIPLE MOBILE TARGETS THROUGH ZIGBEE WSNS