Wireless Sensor Network, 2011, 3, 357-361
doi:10.4236/wsn.2011.311041 Published Online November 2011 (http://www.SciRP.org/journal/wsn)
Copyright © 2011 SciRes. WSN
Secure Path Cycle Selection Method Using Fuzzy Logic
System for Improving Energy Efficiency in Statistical
En-Route Filtering Based WSNs*
Su Man Nam, Chung Il Sun, Tae Ho Cho
School of Information and Communication Engineering, Sungkyunkwan University, Suwon,
Republic of Korea
E-mail: smnam@ece.skku.ac.kr, cisun@ece.skku.ac.kr, taecho@ece.skku.ac.kr
Received September 23, 2011; revised October 21, 2011; accepted October 31, 2011
Sensor nodes are easily compromised to malicious attackers due to an open environment. A false injected
attack which takes place on application layer is elected by the compromised node. If the false report arrives
in a base station, a false alarm is occurred, and the energy of the nodes is consumed. To detect the false re-
port, statistical en-route filtering method is proposed. In this paper, we proposed the secure path cycle selec-
tion method using fuzzy rule-based system to consume effective energy. The method makes balanced energy
consumption of each node. Moreover, the lifetime of the whole network will be increased. The base station
determines the path cycle using the fuzzy rule-based system. The performance of the proposed method is
demonstrated using simulation studies with the three methods.
Keywords: Wireless Sensor Network, Secure Path Cycle Selection, Statistical En-route Filtering, Path Selection
Method, Fuzzy System
1. Introduction
Wireless Sensor Networks (WSNs) have rapidly become
widely used based on development of wireless commu-
nication with low-cost, low-power, multifunction sensors
which enable a wide variety of new applications [1,2]. A
WSN is composed of a sensor field which includes a
large number of sensor nodes and a base station. A sen-
sor node is made up of sensing, computing, and wireless
communication modules. If an event occurs in a sensor
field, the sensor nodes sense the event, and create a report.
A base station provides information to users through the
Internet or a communications infrastructure. Because the
sensor network can communicate without the infrastruc-
ture, it is well suited for random distribution in an open
environment [3]. WSNs are vulnerable to malicious at-
tackers using various attack patters. The sensor nodes are
therefore at high risk of being captured and compromised
False report injection attacks [6] occurring in the ap-
plication layer are initiated by a compromised node, such
as node A, in Figure 1. For example, if a malicious at-
tacker injects false reports through node A into a network,
a base station may transmit the false report via nodes B,
C and D. The base station which owns the false report
issues a false alarm to notify users. Moreover, nodes B,
C and D, which are located on the same path as node A,
expend unnecessary energy. Because of these injected
false reports, the lifetime of the whole network is lost.
Ye et al. [6] proposed the statistical en-route filtering
scheme (SEF) to filter out false reports during the forward-
ing process. The scheme is intended to drop false reports as
soon as possible before they reach a base station. The path
selection method (PSM) [7] was proposed to improve the
detection power of the SEF using a control message. In
PSM, each node determines a secure path using the parti-
tion ID information for its own node in the control message.
To increase network lifetime and to distribute communica-
tion traffic more effectively, the path renewal method (PR-
M) [8] was proposed. The method checks the remaining
energy in the network and ensures balanced energy con-
sumption in each node.
In this paper, a fuzzy logic-based secure path cycle
This research was supported by Basic Science Research Program
through the National Research Foundation of Korea(NRF) funded by the
Ministry of Education, Science and Technology(No. 2011-0004955).
Copyright © 2011 SciRes. WSN
Sensor Field
False Alarm!!
False Report
Figure 1. False report injection attack.
selection method is proposed to ensure balanced energy
consumption in the PRM. The paper is organized as fol-
lows: Section 2 outlines the related research and the mo-
tivation of this work. Section 3 presents the proposed
method using a fuzzy-rule based system [13], and Section
4 presents simulation results. Finally, conclusions and di-
rections for future research are covered in Section 5.
2. Related Work and Motivation
This section reviews the three existing methods (SEF,
PSM and PRM) and then explains the motivation for this
2.1. Statistical En-Route Filtering (SEF)
SEF [6] statistically identifies false report injection at-
tacks using keys before filtering them out. In SEF, a
base station manages a global key, including keys for
divided multiple partitions, and every node, before it is
deployed receives a small number of keys, from a ran-
domly selected partition in the global key pool. Figure
2 represents an example of the global key pool.
When real events occur, a center-of-stimulus (CoS)
node, which is one of the detecting nodes, decides to gen-
erate a report.Surrounding nodes, upon sensing the same
event, create message authentication codes (MACs) and
send them to the CoS node, which generates a sensing
report using the collected MACs. The report is transmit-
ted toward the base station via multiple hops; the base
station can prove that the report is legitimate using its
keys [6]. When the base station receives the report, the
keys of all the MACs in the report are verified against
the keys in the global key pool. Figure 2 shows exam-
ples of the report generation and en-route filtering in the
SEF scheme.
2.2. Path Selection Method (PSM)
In SEF, the power to detect false reports is influenced by
the choice of routing paths. In the worst case, if the parti-
tion IDs in the report differs from the partition IDs in
forwarding nodes, the report cannot be verified as real or
false while passing nodes toward the base station. In
PSM [7], a control message establishes routing paths in
the initial phase to improve detection power for false re-
ports. Each node receiving a control message can choose
its desired the security level and the transmission dis-
tance using an evaluation function. The control message
consists of the partition IDs of the visited nodes and the
hop count.
2.3. Path Renewal Method (PRM)
In PRM [8], the balanced energy consumption is main-
tained in each node to increase network lifetimebecause
nodes have secure paths, which are set by PSM, has
many communications. Each super-node checks its re-
maining energy after establishing the routing paths. If its
remaining energy is less than defined threshold value,
one of the super-nodes child nodes takes over commu-
nication traffic to and from the child nodes. Then, a child
node which has lowest communication traffic among
the super-nodes child nodes selects a new super-node.
Through path renews, PRM makes it possible to distrib-
ute the communication traffic evenly and to increase the
lifetime of the network.
2.4. Motivation
PRM maintains the detection ability of SEF, and con-
sumes a balanced amount of energy from every node. To
determine effectiveness of path renewal, it is important to
consider information about the whole network state be-
cause this network state changes dynamically.
In this research, the secure path cycle selection method
is determined using the hop count, the number of normal
reports, and the number of false reports to select the se-
cure path using a fuzzy rule-based system [9-13]. The
next section presents a detailed description of the fuzzy
rule-based system.
Copyright © 2011 SciRes. *******
Figure 2. Generating a report and filtering a false report.
Figure 3. Overview of the fuzzy secure path cycle selection
3. Fuzzy-Based Secure Path Cycle
3.1. Assumptions
A sensor network is assumed to be composed of a large
number of small sensor nodes and a base station. It is
assumed that the routing paths are established by the
PSM in the initial phase, and the network uses a sin-
gle-path routing protocol. To preform the secure path
cycle selection effectively, the base station knows certain
information about each node, including hop count, the
number of normal reports, and the number of false re-
3.2. Overview
The proposed method is based on SEF to improve energy
efficiency after the nodes have been deployed. It is im-
portant to maintain the energy conservation in many se-
cure protocols. In the proposed method, the base station
determines the secure path cycle selection for the whole
network using the average hop count, the average num-
ber of normal reports transmitted, and the average num-
ber of false reports. Fuzzy logic is then used to represent
the fitness of each secure path cycle selection based on
its different values of these three variables.
3.3. Input Factors
This section discusses the factors that are used for fuzzy
AHC (Average Hop Count): When a report is trans-
mitted by a CoS node, the report travels via multiple
hops toward a base station. If a WSN has high hop
counts much energy will be consumed in each node.
Therefore, the lifetime of the sensor network is influ-
enced by high hop counts in each node.
ANNR (Average Number of Normal Reports): This
value indicates how many normal reports, on average,
arrive at the base station from CoS nodes. If each node
transmits many reports, the lifetime of the sensor net-
work will rapidly decrease. Therefore, a number of
normal reports is assumed over the network lifetime.
ANFR (Average Number of False Reports): This
value represents the condition of network security. If
the base station receives many false reports from
compromised nodes, the sensor network needs to
change its secure paths to improve its detection power
for false reports. Accordingly, this value is an indica-
tor of network security.
Copyright © 2011 SciRes. WSN
3.4. Fuzzy Membership Functions and Rules
Figures 5(a), (b) and (c) show the membership functions
of the fuzzy logic input parameters. The labels of the
fuzzy variables are as follows:
AHC = {S (Small), M (Medium), L (Large)}
ANNR = {L (Low), M (Medium), L (Large)}
ANFR = {F (Few), M (Many)}
The fuzzy logic output parameters are represented by a
label, S or C. The label values represent a cycle selection
(CS) as follows:
CS = {S (Stay), C (Change)}
The rule base of the fuzzy system consists of 18(=3 ×
3 × 2) rules. Some of these rules are shown in Table 1. If
AHC is High, ANNR is Medium, and AQFR is Many,
then it is recommended to change the secure path cycle to
conserve energy and to maintain detection power (Rule 11).
This case indicates that there are problems in the network,
and that therefore the secure path cycle must quickly be
altered. If AHC is Low, ANNR is Large, and AQFR is
Low, then the cycle selection does not change because
the network is normal (Rule 12). The network will wait
in this condition until required to move from 0 (Stay) to
1 (Change). Therefore, it is important to select the secure
path cycle appropriately.
4. Simulation Results
To show the effectiveness of the proposed method, it is
compared with the SEF, PSM, and PRM approaches
through simulation studies. The simulation environment
consists of a sensor network with 200 nodes in the simu-
lation environment. Each node consumes 16.56 μJ to
transmit a report, 12.5 μJ to receive a report, and 15 μJ to
generate a MAC [6]. There is a global key pool of 100
keys, the number of partitions is 10 and each node owns
four keys.
Figure5 shows the energy consumption for each
Figure 4. Energy consumption for each method.
Table 1. Fuzzy if-then rules.
Input Output
04 Medium Small Low Stay
11 High Medium Many Change
12 Low Large Low Stay
16 Medium Large Low Change
(a) AHC
(b) ANNR
(c) ANFR
Figure 5. Fuzzy Input Membership Functions.
method for 300 iterations of the network. This figure
indicates that SEF consumed higher than the other
methods because reports on SEF pass via many hops.
PRM is better than PSM because routing paths is
changed through energy check of each node. The energy
consumption of the proposed method is low because
changes in the secure path using the fuzzy logic system
improved the condition of the network. It is expected that
with a larger number of iterations, the gap between the
Copyright © 2011 SciRes. *******
Figure 6. Average numbers of hops traveled by false re-
ports for each method
proposed network and the other networks would increase.
Therefore, it can be said that the proposed method con-
sumes less energy than SEF, PSM, or PRM.
Figure 6 shows the average simulated performance for
each method in filtering out false reports. Because PSM,
PRM, and PM apply secure paths, SEF requires a higher
number of nodes for transmission than the other three
methods. The method proposed here offers almost the
same detection power as the PSM and PRM. The pro-
posed method can therefore maintain a security level
similar to that of PSM or PRM.
5. Conclusions and Future Work
This paper has proposed a fuzzy-based secure path cycle
selection method to conserve energy and maintain detec-
tion power in networks. It has been demonstrated that the
proposed method improves the balance in energy con-
sumption among nodes to increase network lifetime,
considering the average hop count, the average number
of normal reports transmitted, and the average number of
false reports. The simulation results show that the pro-
posed method is able to provide higher energy efficiency
and appropriate security level in the network. In the fu-
ture, the authors propose to apply optimization to the
fuzzy logic system in the proposed method to increase
the lifetime of the whole network.
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