A Survey of Secure Target Tracking Algorithms for Wireless Sensor Networks Alma Oracevic
Suat Ozdemir
University ofBihac
Computer Engineering Department
Technical Faculty
Faculty of Engineering, Gazi University
Bihac, Bosnia and Herzegovina
Ankara, Turkey
[email protected]
[email protected]
Abstract- Tracking a target as it moves in monitored area
algorithms are also demanding applications considering the
has become an increasingly important application for wireless
scarce resources of sensor nodes. Traditional target tracking
sensor
networks
(WSNs).
Target
tracking
algorithms
algorithms are based on heavy and complex signal processing
continuously report the position of the target in terms of its
algorithms
coordinates to a sink node or a central base station. Due to the
traditional target tracking algorithms cannot be applied in
rapid
development
classification
of
classifications accuracy;
of
target
consider
other
WSNs,
there
tracking
scalability,
classification
is
no
algorithms. energy
standardized
Some
of
those
consumption
considers
network
and
topology,
position of target, mobility of target/object etc. In this paper, we have considered target tracking algorithms of WSNs from the security
point of view.
We have compared and discussed
problem of security in the most important target tracking algorithms for WSNs. To the best of our knowledge, this is the first study that analyses the target tracking algorithms in terms of security.
and
they
are
generally
centralized.
Hence,
WSNs due to their high resource requirements and limitations of sensor nodes such as limited power. Therefore, target tracking
algorithms
considering communication
of
WSNs
energy
should
conservation,
overload.
In
be
designed
bandwidth
addition,
as
target
by and
tracking
applications are generally used in mission critical applications, security is another important design issue to be considered. In mission critical applications, sensor nodes are deployed in hostile
areas
and
they
can
be
easily
captured
by
the
enemies/intruders. Such compromised nodes can be used to falsify the collected data and threaten the target tracking
Keywords-target tracking algorithms, classification of target tracking algorithms, survey, wireless sensor networks; security.
reliability. Although there are many papers that propose target tracking
algorithms
by
focusing
on
energy
conservation,
bandwidth and communication overload [5-10] there is a I.
INTRODUCTION
limited amount work considering secure target tracking in the
Specialization of the wireless ad-hoc mesh networks are wireless
sensor
characteristics organizing,
networks
(WSNs).
of ad-hoc mesh
self-healing,
WSNs
networks
wireless
and
inherit
all
like being self
adaptive
networks.
WSNs are composed of large nwnber of inexpensive wireless sensor
nodes,
deployed
in
a
physical
environment
for
observation of an event of interest. Every node can not only communicate with its neighboring nodes, but also collect, process and store data/information. A sensor node is a very small device that represents the integral part of WSNs. These nodes are being produced at a very low cost and yet with high levels of sophistication in terms of computing power, energy consumption savings, and multipurpose functionalities. WSNs are usually deployed in a remote and hostile area, which is usually called the monitored region, for monitoring purposes. Sensor nodes behave as instrwnents capable of covering large areas
providing
detailed
information.
These
nodes
are
interconnected and are used together as a single monitoring and reporting device to acquire specific types of data as desired by the application requirements [1]. Most researchers consider target tracking as one of the important
applications
of
WSNs
[2-6].
Target
tracking
literature [11-l3]. In the literature, there are several survey papers [2-4] that classifies target tracking algorithms from different aspects but not from the security point of view [11-l3]. In this paper we also give the most important works in the target tracking area; however we make the classification based on the security services. To the best of our knowledge, this is the first paper that presents a survey for secure data target tracing in WSNs. We first analyze the existing target tracking algorithms not only from the resource conswnption aspects but also from the security point of view and demonstrate what security services they provide. We explain each algorithm under its category giving special attention to security. Finally, we provide the open research problems and future research directions in secure target tracking area. The rest of paper is organized as follows: In section II, our classification of target tracking algorithms is presented. In Section III, we compare each class of algorithms in detail focusing on security aspects. Section IV depicts the open research problems and future research directions in secure target tracking. We conclude the paper in Section V.
978-1-4799-3351-8/14/$31.00 ©2014 IEEE
needs more energy, so the main problem is maintaining the balance between energy efficiency and the number of active nodes. There is several activation methods used in the literature Tree Based
�
[4],[9], [16], [17],
Target Detection
(i) naIve activation (NA), (ii) (RA), (iii) selective activation (SA), (iv)
namely
randomized activation
duty-cycled activation (DA). Target
Activation Based Target
H
Detection in WSN
Detection
In naIve activation, all the nodes in network are active all
Cluster Based
t--f-+-+
the time; basically, nodes are in tracking mode until they are
Target Detection
, ,
out of energy (i.e., become dead) [9],[17]. In [16], the authors propose a new method to save energy. In terms security, naIve activation is the most secure target tracking method as all the
Hybrid Based
nodes are in tracking mode all the time and any misbehaving
Target
�
sensor node can be detected by the others. However, this
Detection
,
method's energy efficiency is very low, hence it is rarely used.
,
In random activation strategy, every node is active with
Fig. 1. Traditional classification of target tracking approaches
some possibility. The nodes that will be used in tracking are determined
II.
CLASSIFICA nON OF TARGET TRACKING ALGORITHMS
Most of the target tracking methods used in the literature can fmd itself a place in the classification presented in Fig. 1. Similar classifications can be found in other survey papers
by
randomly
[9].
This
type
of activation
is
vulnerable, if one of the sensors is compromised and makes itself target tracking node by playing with its random method. In this case, the compromised node can send false target information to the base station.
[2],[3], [4]. In this paper, we use the classification given in
Selective activation based on prediction (SA) is activation
Fig.l by considering the security issues in target tracking task.
based algorithm that predicts next possible position of target
Our new classification is given in Fig. 2.
and by that prediction choose which group of nodes should be in tracking mode. In this strategy, chosen nodes are the ones that are closest to the next predicted position of the target. Nodes which are not in tracking mode are in communication mode
Tree Based Target Detection
[9],[16], [17]. Similar to random activation strategy,
compromised nodes can tamper with the prediction algorithm �
to falsify the target tracking process.
� �
I-
Target
Activation Based Target Detection
+--
Cluster Based
Detection in
Target
WSN
Detection
Duty-cycled activation (DA) is a kind of activation based
�
algorithm that turns on and off whole sensor network in
� ." w
.li
certain periods of time. It can be used jointly with any other
�
activation strategy. Security treat in this activation based
i Hybrid Based
�
algorithm is to prevent the distribution of the on/off command
Z
in WSN. In this case,
Target Detection
WSN can stay in off condition
(completely or partially) and intruders may take advantage of the off WSN regions.
Fig. 2. Classification of target tracking considering security.
In section, we analyze several important target tracking
B.
Tree based target tracking
algorithms in the literature by following the classification in
Tree-based methods organize the network into a tree
Fig. 2. As for the security analysis, we specifically look for if
topology so that data can be collected efficiently. In these
the target tracking algorithm supports
(i)
data confidentiality
algorithms, nodes that detect the target communicate with each
data integrity. Violation of
other and select one node which becomes the root node. The
one of these security properties in a target tracking protocol
root node is always the node closest to the target, and its job is
(ii)
source authentication and
(iii)
can result in catastrophic events. For example, target detection algorithm can be fooled by inserting false data into network [14], changing the collected data or sending false data from unauthorized sensor nodes
[15]. Therefore, in any target
detection algorithm above security requirements must be addressed.
of the target changes. When target moves, some nodes become part of tree, and some get deleted from the tree. There are several important papers that employ tree based target tracking: Scalable Tracking Using Networked Sensors (STUN) [18], Dynamic
Convoy
Tree-based
Collaboration
(DCTC)
[19],
Optimized Communication & Organization (OCO) [20],[21]
A. Activation based target tracking In activation based target tracking algorithms,
to collect information from all the nodes via a distributed spanning tree. The root node frequently changes as the position
energy
savings come at the expense of a reduction in the quality of tracking.Basically, the more active sensors are in the network, the quality of tracking is better. But in this case the network
and Deviation Avoidance Tree (DAT) [22]. STUN target tracking algorithm calculates the cost between two links using Euclidean distance. Sink node gets data
through intermediate nodes which also keep record of the
In static clustering, the number of nodes, head of cluster,
detected object. In this algorithm, data are collected and sent by
area of network coverage is static. It looks like simple
leaf nodes, but it can be very ineffective and costly if its leaf
architecture, since everything is decided in advance, however
and intermediate nodes are not chosen correctly. From the
in real-life implementation it has several negative sides. One
security aspect, STUN is very vulnerable, since in case of a
down side of static clustering is that if the cluster head is out of
compromised node attack it is enough to falsify the selection of
power, all other nodes in its cluster are useless, since nodes in cluster communicate only with the cluster head. Usually, the
leaf and intermediate nodes. DCTC protocol works on principle of convoy tree. When target enters into the monitored area, sensor nodes around the
nodes
of
two
different
clusters
do
not
communicate
or
exchange information between clusters.
target are activated. Activated sensor nodes collaboratively
In [23] authors propose three algorithms for tracking a
form a tree. The root node which collects information from the
mobile target in wireless sensor network utilizing cluster-based
tree refines this information to obtain more complete and
architecture. These algorithms are namely: (i) adaptive head,
accurate
information
about
the
target
by
employing
a
(ii) static head, and (iii) selective static head. Goal of paper was
classification algorithm. The rest of the nodes around the
to achieve a promising tracking accuracy and energy efficiency
moving target make convoy tree dynamically. That is the tree
by choosing the candidate sensor nodes nearby the target to
is reconfigured as the target moves. The negative side of this
participate in the tracking process while preserving the others
algorithm is its energy consumption for reconfiguration of the
in sleep state. They showed that the adaptive head is the most efficient algorithm in terms of energy consumption while static
convoy tree when the target is moving. OCO is another tree based method that is used for single target tracking. It provides self-organizing routing capabilities with low computation overhead. It consists of the following steps:
(i)
(ii)
processing,
(iii)
tracking error is concerned especially when the target moves rapidly.
tracking and
In [24] authors propose improving former strategy designed
maintenance [4],[20]. In the first phase, base station
for heterogeneous WSNs, where sensors are organized in a
collects the position of all sensor nodes in the network. In the
static clustering architecture. Goal of paper was to improve
second phase, border nodes of network are detected and
energy and the longevity constrains of sensor nodes. The
(iv)
position collecting,
and selective static heads algorithms are preferred as far as the
defined. In the tracking stage, all objects that come from the
proactive and the reactive cluster management are proposed to
outside of the network area are detected. To be more energy
efficiently deal with these constraints.
efficient, only border nodes are kept on and they notify the base station when a target detected. When the border nodes lost the target, they send message to all of their neighbors. This way, target is tracked all the way while it is in network area, and the energy cost is less compared to other target tracking algorithms.
Dynamic clustering present several advantages compared to static clustering. Cluster head is a sensor node with good energy and computation capacity and large communication range. In dynamic clustering, nodes can be a part more than one cluster, so there is no useless nodes like with static clustering. This type of clustering can be very energy efficient
DA T target tracking algorithm is a result of overcoming
when WSN is large and when it is necessary to cover a big
issues in earlier version of similar target tracking algorithms.
area. Although dynamic clustering based target tracking sounds
The problem in earlier solutions is the large amount of energy
like
spending necessary for target location updating when network
consumption, it has some drawbacks such as loosely defmed
scale is large. DAT protocol is a two-stage approach. The first
missing target recovery procedure.
stage reduces the update cost, and the second stage reduces query costs. Updates are done when object moves from one location to another, and queries are broadcasted to whole network.
DAT
reduces
cost
of
uploading
and
querying
expenses by treating each node as singleton sub tree, so eventually all nodes would become one tree. C.
In order to detect and track targets, these methods take head and cluster members (sensor nodes). The node who first detects the target becomes the cluster head. Cluster based target tracking algorithms can be divided into the following two
(i)
Static
Clustering
[23],[24]
[27] target tracking algorithm is one of the
objects that move with constant speed by dynamically forming a
cluster
and
selecting
the
and
cluster
head.
Although,
the
algorithm assumes the communication range of sensor nodes is wider than their sensing range, it can only deal with the targets
advantage of clusters in which each cluster consists of a cluster
Clustering [25], [4],[26], [27], [28].
DELTA
due to its low energy
dynamic clustering based target tracking methods. It tracks the
that move at constant speed.
Cluster based target tracking
types:
a winner in target tracking
(ii)
Dynamic
Table 1. Comparison of target tracking algorithms
Performance Criteria
Security Services
Target tracking algorithm
Tracking
Communication
Fault tolerance
accuracy
Confidentiality
Authentication
Integrity
x
x
x
overload
STUN [18[
x
.,/
DCTC [19[
x
.,/
.,/
x
x
x
.,/
.,/
x
x
x
.,/
x
x
x
.,/
x
x
x
.,/
x
x
x
.,/
.,/
x
x
x
.,/
.,/
x
x
x
.,/
.,/
x
x
x
.,/
OCO [20] DAT [21]
x
DELTA [27]
x
RARE [28]
x
DPT [30]
x
x
.,/ x
.,/
DCAT [25] HPS [31]
x
x
compromlSlng
the
current
cluster
head
or
prevent
the
communication between the cluster head and its sensor nodes. D.
Hybrid based target tracking Hybrid methods are the tracking algorithms that fulfill the
requirements of more than one type of target tracking [4],[29] Hybrid
Clustering
(Distributed
""'
Clustering
0
D O//\b
consists
Predictive for
of
some
Tracking)
Acoustic
Tracking)
methods like
[30],
DCAT
[25],
and
DPT
(Dynamic
Hierarchical
prediction strategy (HPS) [31]. DPT protocol employs two different mechanisms for target tracking. A clustering based approach makes DPT protocol scalable, ensures
whereas a prediction based tracking mechanism a
distributed
and
energy
efficient
solution. By
employing these two mechanisms, DPT is able to be resilient against node or prediction failures. DCA T is decentralized dynamic clustering algorithm for acoustic target tracking. In this method, Voronoi Diagrams are used compose the clusters and only one cluster head becomes active when the acoustic signal strength detected by cluster head exceeds a pre-determined threshold. After this signal,
Fig. 3. Scheme for cluster based wireless sensor network [35]
cluster head sends a broadcast packet and forms cluster. Sensor nodes join
Dynamic
clustering
based
target
tracking
method
RARE
protocol reduces the quantity of nodes involved in tracking, and
therefore
it
is
able
to
diminish
the
overall
energy
consumption. In order to achieve this low energy consumption, RARE uses two sub algorithms as follows. RARE-Node algorithm reduces the number of nodes participating in the target tracking process. RARE-Area algorithm ensures that only the nodes that satisfy the certain data quality can send data to cluster head.
the
cluster
depending
on
calculated
distance
between them. HPS protocol also forms clusters using Voronoi division and a target's next location is predicted via Least Square Method.
The
protocol's
drawbacks
are
communication
overhead which is not studied in the paper. III.
COMPARISON OF ALGORITHMS FOR TARGET TRACKING FOCUSING ON SECURITY
Many researches investigate and discus the classification of
In both static and dynamic cluster based target tracking
target tracking algorithms for WSNs [2], [3],[4] however very
cluster heads play an important role. Therefore, in terms
few number of papers discus about these problems from the
security, intruders can falsify the target tracking process by just
security point of view [11], [12],[13]. As seen from Section II,
if at least one node is compromised, false measurement could
explain the important protocols in each category and present
lead to wrong decision-making in the network [32],[33]. In
which security properties they provide. Our study shows that
case of any kind of malicious or hostile attack, target tracking
current research in target tracking mostly focuses on the
algorithm would not be able to report the correct position of
accuracy or energy efficiency and there is limited amount of
target. In the literature,
some of the conventional target
work that considers security. Based on the findings of our
tracking algorithms incorporates Bayesian filtering, particle
study, we present a table that summarizes the state of the art in
filters or EKF filters to ensure secure target tracking in WSNs
the target tracking area. At the end of the paper, we also
[12].
provide open research issues and future research directions.
However,
overlooks
the
many basic
of
the
security
target
tracking
requirements.
algorithms
In
order
to
Due to page limitation, we are able present only a subset of the
summarize this finding, in Table I, we present the comparison
work we that have been surveyed, we plan to prepare the
of the most important target tracking algorithms for WSNs.
extended journal version of this paper covering all target
This table shows if the target tracking protocol supports or
tracking protocols in detail.
considers
the
following
criteria:
communication
tolerance,
tracking
accuracy,
overload,
fault
authentication and integrity. IV.
REFERENCES
confidentiality,
OPEN RESEARCH PROBLEMS AND FUTURE RESEARCH
[I]
Salatas, V. "Object Tracking Using Wireless Sensor Networks", Master Thesis,Naval Postgraduate School, pp. 11-25,September 2005.
[2]
Bhatti,S. and Jie Xu. "Survey of target tracking protocols using wireless sensor network", Proceedings of Fifth International Conference on Wireless and Mobile Communications (ICWMC), pp. 110-115, August 2009.
[3]
M. Fayyaz, "Classification of object tracking techniques in wireless sensor networks",Wireless Sensor Network, Vol. 3,No. 4, pp. 121-124, 2011.
[4]
lLi and Y. Zhou, "Target Tracking in Wireless Sensor Networks", ISBN 978-953-307-321-7,Published: December 14,2010
[5]
M.Nandhini and V.R.Sarma Dhulipala (2012). "Energy-Efficient target tracking algorithms in wireless sensor networks: an overview", !JCST Vol. 3,Isuue 1,pp. 66-71,Jan. - March 2012.
[6]
S. Samarah, M. AI-Hajri and A. Boukerche, "A Predictive Energy Efficient Technique to Support Object-Tracking Sensor Networks", IEEE Transaction on vehicular technology: Vol. 60, No. 2, pp. 656-663 February 2011
[7]
S. Kaur Sarna and M. Zaveri, "ERTA: energy efficient real time target tracking approach for wireless sensor networks",Proceedings of the 4th International Conference on Sensor Technologies and Applications (SENSORCOMM),pp. 220-225 ,July 2010.
[8]
Y. Wang and D. Wang, "Energy-Efficient node selection for target tracking in wireless sensor networks", International Journal of Distributed Sensor Networks,6 pages,Volume 2013.
[9]
S. Pattern,S. Poduri, and B. Krishnamachari, "Energy-Quality Tradeoffs for Target Tracking in Wireless Sensor Networks",LNCS 2634,pp. 3246,2003.
DIRECTIONS
There are many open research issues in target tracking. Majority of these problems fall into moving target(s) related scenarios. For example, the large number of targets or change in target's speed or direction may falsify the algorithms result [34]. So, future research direction for this problem is to propose target tracking algorithms that are not affected by the number of targets and are resilient against the changes in targets' direction/pace. Balancing
between
energy
consumption
and
tracking
accuracy is another problem that protocol designers need to tackle with. Depending on the application, consumption
or
tracking
accuracy
may
either energy
have
a
higher
importance. It is the protocol designer's job to give appropriate weight to them. However, in order to expand the network lifetime applications usually need to maximize both energy utilization and tracking accuracy. Therefore, there is a need for adaptive
protocols
that
dynamically
balances
the
energy
consumption and track accuracy based on the application requirements. Security is the most important issue for mission critical WSNs. Hence, security and target tracking represent a rich field of research problems. Most of target tracking algorithms are not designed with security in mind and these algorithms are vulnerable to several kind of attacks. After the design of target tracking algorithm has been completed, it is not trivial to implement a security mechanism for the algorithm. The best way is to address the security problem during the design of target tracking algorithm. Hence, secure tracking algorithms that are designed from the scratch are needed. In addition, using cryptographic functions increases the delay in communication, using traditional security algorithms in target tracking negatively affects the respond time of the sensor nodes. As target tracking algorithms are delay sensitive, security functions that work fast on small data (such as coordinate or direction information) are needed for target tracking. V.
CONCLUSION
In this paper, we explore the categories of target tracking methods in the literature by focusing on the security. We
[10] F. Deldar and M. H. Yaghmaee, "Designing an Energy Efficient Prediction-based Algorithm for Target Tracking in Wireless Sensor Networks", Wireless Communications and Signal Processing (WCSP), pp 1-6,2011. [II] M. Mansouri and L. Khoukhi, "Secure Quantized Target Tracking in Wireless Sensor Networks", IEEE 978-1-4577-9538-2/11, pp. 713-718, 2011. [12] A. P. Fard and M. Nabaee, "Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter", arXiv preprint arXiv:1204.3141, Apr 14,2012 [13] M. Mansouri, L. Khoukhi, H. Nounou, and M. Nounou, "Secure and Robust Clustering M. Mansouri, L. Khoukhi, H. Nounou, and M. Nounou, "Secure and Robust Clustering for Quantized Target Tracking in Wireless Sensor Networks, JOURNAL OF COMMUNICATIONS AND NETWORKS,VOL. 15,NO. 2,APRIL 2013 [14] S. Ozdemir, and H. Cam, "Integration of False Data Detection with Data Aggregation and Confidential Transmission in Wireless Sensor Networks", IEEE/ACM Transactions on Networking, vo1.l8, no.3, pp. 736-749,Jun. 2010. [15] M. Meghdadi, S. Ozdemir, I. GuIer, "A Survey of Wormhole-Based Attacks and Their Countermeasures in Wireless Sensor Networks", [ETE Technical Review,vol. 28,no. 2,pp. 89-102,2011.
[16] W. Zhou, W. Shi, X. Wang and K. Wang, "Adaptive sensor activation algorithm for target tracking in wireless sensor networks", International Journal of Distributed Sensor Networks,10 pages,Volume 2012. [17] J. Chen, K. Cao, K. Li and Y. Sun, "Distributed sensor activation algorithm for target tracking with binary sensor networks", Cluster Computing,Volume 14, Issue I ,pp 55-64,2011. [18] H. T. Kung and D. Vlah, "Efficient Location Tracking Using Sensor Networks", Proceedings of 2003 IEEE Wireless Communications and Networking Conference [19] W. Zhang and G. Cao, "DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks",Published in the IEEE Transactions on Wireless Communications,Sept. 2004 [20]
S.P.M. Tran, "OCO: Optimized Communication & Organization for Target Tracking in Wireless Sensor Networks", Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006. IEEE International Conference,5-7 June 2006
[21] S. P. M. Tran and T. A Yang, "Evaluations of Target Tracking in Wireless Sensor Networks", SIGCSE'06, March 1-5, 2006, Houston, Texas,USA. [22] C.Y. Lin, W.C. Peng, and Y.c. Tseng, "Efficient In-Network Moving Object Tracking in Wireless Sensor Networks", Mobile Computing, IEEE Transactions on (Volume:5, Issue:8),Aug. 2006 [23] Khalid ADarabkh, Shereen S. Ismail, M. AI-Shurman, Iyad F. Jafar, E. Alkhader,Mamoun F.AI-Mistarihi,"Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks",Journal of Network and Computer Applications, Vol. 35,Iss. 6,pp. 2068-2080, November 2012. [24] J. Teng, H. Snoussi and C. Richard, Prediction-based cluster management for target tracking in wireless sensor networks", Wireless Communications and Mobile Computing, Vol. 12,Iss. 9,pp. 797812, June 2012. [25] W.P. Chen, "Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks", Published by the IEEE CS, CASS, ComSoc,IES, & SPS,2004.
[26] K. Huang, H. Wang, W. Wang, Y. Wang, A Dynamic tracking " mechanism for mobile target in wireless sensor networks", Proceedings of 2012 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), New Taipei, pp. 822-826, November 2012. [27] M.Walchli, P. Skoczylas, M. Meer, and T. Braun, "Distributed event localization and tracking with wireless sensors",WWIC 2007 [28] E. Changsha,W. Guojun,G. Minyi,D. Mianxiong, "RARE: An Energy Efficient Target Tracking Protocol for Wireless Sensor Networks", Parallel Processing Workshops,2007. ICPPW 2007 [29] Z. Wang, W. Lou, Z. Wang, 1. Ma and H. Chen, "A Hybrid Cluster Based target tracking protocol for wireless sensor networks", International Journal of Distributed Sensor Networks, 16 pages, Volume 2013. [30] H. Yang and B. Sikdar, "A Protocol for Tracking Mobile Targets using Sensor NetworksD, Sensor Network Protocols and Applications, 2003. Proceedings of the First IEEE,May 2003 [31] Z. Wang, H. Li, X. Shen, X. Sun, "Tracking and Predicting Moving Targets in Hierarchical Sensor NetworksD, Networking, Sensing and Control,2008. ICNSC 2008 [32] J. Sen, A Survey on Wireless Sensor Network Security", International " Journal of Communication Networks and Information Security (IJCNIS), Vol.l, No. 2,August 2009,pp. 55-78 [33] J. P. Walters, Z. L. W. Shi, and V. Chaudhary, "Wireless Sensor Network Security: A Survey", Security in Distributed, Grid, and Pervasive Computing,Auerbach Publications,CRC Press 2006. [34] S. Oh,"A scalable Multi-Target Tracking Algorithm for Wireless Sensor Networks", International Journal of Distributed Sensor Networks Volume 2012 (2012),Article lD 938521. [35] M. A. Matin, "Wireless Sensor Networks - Tehnology and Protocols", InTech,2012,p. 3-49
