Abstract— An efficient selection method of cluster heads in
wireless sensor networks using an intelligent computing algorithm is
evaluated with a large data set in this paper. The CHS-CNN (Cluster
Head Selection using Centroid Neural Network) finds clusters heads
optimally by using Centroid Neural Network (CNN). Th CHS-CNN
can take the advantages of both CNN and LEACH for minimizing
battery (energy) consumption in sensor nodes. The CHS-CNN
compares with another intelligent computing method, Self-Organizing
Map (SOM). Initial results show that the CHS-CNN can be effective in
terms of total energy consumption of the network and cluster head
selection speed.
Keywords— sensor networks, cluster head, intelligent computing,
centroid neural network, LEACH, SOM.
I. INTRODUCTION
IRESSS sensor network(WSN) is a physical system with
a few to hundreds or thousands of sensor nodes. Since
each node in the network connects with one or several other
nodes, WSN can have various topologies[1]-[4]. Each sensor
node in WSN collects various information temperature, sound,
pressure, and remaining battery life data. These collected
information is then transmitted to a base station over a limited
data bandwidth. With existing thousands of nodes, this kind of
work consumes lots of energy and can overload bandwidth in
communicating many nodes at the same time. Therefore, it is
required to design an effective routing protocol for wireless
sensor networks. Various routing protocols have been
proposed for wireless networks [5]-[7]. One of the main
features in these protocols is minimizing energy consumption
at each sensor. Among proposed routing protocols for WSN,
LEACH(Low Energy Adaptive Clustering Hierarchy)[1] can
successfully minimize the energy consumption required for
communication and LEACH then achieves improving the life
time of a wireless sensor network by selecting the cluster
heads stochastically. Although LEACH shows meaningful
advantageous features, there is a room for improvement in
system life time or system performance capacity because
cluster selection process in LEACH is far from being optimal.
This issue can be addressed by using computational
intelligence methods like Self-Organizing map (SOM) or
Centroid Neural Network(CNN) [8]-[11].
The rest of this paper is organized as follows: Section II
Eun-Ae Park, Kheon-Hee Lee, and Dong-Chul Park are with the
Department of Electronics, Myongji University, YongIn, Rep. of KOREA (phone: +82-31-330-6756, fax: +82-31-3306977, email id; [email protected])
Soo-Young Min is with Software Device Research Center at Korea
Electronics Technology Institute, SongNam, Rep. of KOREA (e-mail: [email protected] )
summarizes LEACH and CNN. A cluster head selection
method based on CNN is summarized in Section III. Section IV
presents experiments and results on several data sets for the
evaluation of the CNN-based method. Section V concludes this
paper.
II. RELATED WORKS
A. LEACH (Low-energy adaptive clustering hierarchy)
LEACH assumes that sensor nodes have enough power for
transmitting data to the base station (BS). LEACH also assumes
that each node always has data to send. LEACH divides nodes
into groups and each group has one node, called cluster head
(CH). CH collects data from its neighboring node members and
then sends the collected data to the BS. For selecting CHs,
LEACH uses a stochastic process and it ensures that all nodes
are selected with equal probability. Once all CHs are chosen,
they broadcast an advertisement message to other non-CH
nodes and let them know which nodes are the CH. More details
on LEACH can be found in [1].
B. . CNN (Centroid Neural Network)
The CNN algorithm [9] is an unsupervised competitive
learning algorithm. It finds the center of clusters optimally at
each presentation of the data vector. The CNN first introduces
definitions of the winner neuron and the loser neuron. When a
data x is given to the network at the epoch (k), the winner
neuron at the epoch (k) is the neuron with the minimum
distance to x. The loser neuron at the epoch (k) to x is the
neuron that was the winner of x at the epoch (k-1) but is not the
winner of x at the epoch (k). The CNN updates its weights only
when the status of the output neuron for the presenting data has
changed when compared to the status from the previous epoch.
The objective function for CNN is the summation of all the
distances between each data and the center of the data group for
the data.
The CNN has several advantages over conventional
algorithms such as SOM or k-means algorithm when used for
clustering and unsupervised competitive learning. The CNN
requires neither a predetermined schedule for learning gain nor
the total number of iterations for clustering. It always
converges to sub-optimal solutions while conventional
algorithms such as SOM may give unstable results depending
on the initial learning gains and the total number of iterations.
More detailed description on the CNN can be found in [9]-[11].
Application of Intelligent Computing
to Wireless Sensor Networks
Eun-Ae Park , Kheon-Hee Lee, Dong-Chul Park, and Soo-Young Min
W
3rd International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2014) Feb. 11-12, 2014 Singapore
26
III. CHS-CNN (CLUSTER HEAD SELECTION USING CENTROID
NEURAL NETWORK)
Although LEACH has some advantages, there is room for
improvement because of the grouping is not optimal and rather
random. In LEACH, it ignores physical locations of the node
when selecting group and cluster heads. Thus, CHs in LEACH
may consume more energy from cluster members than those at
the center positions. Fig. 1 shows examples of the problem. To
overcome this problem, CNN is applied in the process of
cluster selection. At the first round, nodes at cluster centers are
chosen as CHs and CNN consumes less system energy than
LEACH from the first round. The CHS-CNN ensures that the
energy is optimized locally in each group and thus decreases
the energy consuming in the whole networks rather than
randomly changing the clusters as in LEACH[8].
IV. EXPERIMENTS AND RESULTS
For experiments, the same environment setup as in LEACH
is used. We randomly generate 1,000 sensor nodes that locate at
(x,y) in a certain node range 0≤x≤200 and 0≤y≤200 and BS
locate at (100,350). Each data message is 500 bytes over 1 Mb/s
of bandwidth. The header for each packet is 25 bytes. The cost
of communication energy when transmit a message with size of
l bits through d distance is evaluated as follows:
(1)
and for a received message:
(2)
Where is a threshold distance. and are the
amplifier energy respect to the free space model or the
multipath model. is the electronics energy. In our
experiments, these parameters are set as: = 50 ,
= 0.0013 , =10 and =86.202.
These parameters are also used in [1].
Fig. 1 shows the location of CHs and grouping information
for LEACH, EBCS (Energy Based Clustering using Self
organizing map), and the CHS-CNN on one instance for the
data set. In Fig. 1, selected CHs are in red dots. Light blue dots
represent the node that have been selected as CHs in previous
rounds. Blue dots are the nodes that never been selected as CHs
in previous rounds and are the candidates for future CHs. The
total distances from the chosen CHs to the nodes in their groups
in these instances are 11,423.8, 11,598.4, and 8,862.7 for
LEACH , EBCS, and CHS-CNN, respectively. This clearly
shows the difference of communication energies required for
different protocols. Table I summarizes average numbers of
rounds for different case: first death, half death and last death.
These criteria are very important in evaluating different routing
protocols. Table I shows that the CHS-CNN can be favorably
compared with to other algorithms in terms of node deaths.
Similar results are reported in [8] for a smaller scale problems.
(a)
(b)
(c)
Fig. 1: Examples of cluster head locations: (a) LEACH (b) EBCS,
and (c) CHS-CNN (red dots: current CHs, light blue dots: past CHs,
blue dots: never been CHs)
In order to evaluate speed for selecting cluster heads in
EBCS and CHS-CNN, the cpu times are calculated under the
following computing environment:
- CPU: Intel(R) Core(TM) i5-2400, 3.1㎓ CPU,
- RAM size: 2 GB, OS: Window 7 Enterprise, 64bit.
Table II shows the clustering time required for different
protocols. Note that LEACH does not require clustering time
because it selects CHs randomly. On average, EBCS requires
5,399.1 mS while CHS-CNN finds clusters in 1,620.0 mS. This
clearly shows that CHS-CNN reduces the clustering time about
70% when compared to EBCS. When number of nodes is
practical size, this effect is more severe and this savings in
selection time makes the CNN-based protocol very valuable.
3rd International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2014) Feb. 11-12, 2014 Singapore
27
TABLE I
AVERAGE NUMBERS OF DEATH ROUNDS FOR DIFFERENT CASES
Protocol First death Half death Last death
LEACH
EBCS
CHS-CNN
101.1
111.7
139.0
282.2
575.5
601.8
139.0
601.8
1,018.5
TABLE II
AVERAGE CPU TIME REQUIRED FOR CLUSTERING
Data Random Grid Gaussian
EBCS
CHS-CNN
4,993.6
1,488.6
5,412.8
1,583.2
5,790.9
1,788.3
V. CONCLUSIONS
A wireless sensor network protocol based on centroid neural
network is evaluated in this paper. The CHS-CNN protocol
finds clusters of sensor nodes optimally by adopting Centroid
Neural Network and follows the selection method for cluster
heads from LEACH algorithm. Experiments are performed on
example platform used in LEACH algorithm in order to
evaluate performances of the CNN-based algorithm. Several
different sensor network maps with 2,000 nodes are designed
for experiments and evaluated the number of nodes alive after a
period. The results show that the CHS-CNN shows very
compatible performance with EBCS and very improved
performance over LEACH. The computing speeds for the
selection of cluster heads for each round are evaluated for
CHS-CNN and EBCS. The results show that the CHS-CNN can
save the clustering time required for the selection of cluster
heads about 70%. From experiments and results, we can
conclude that the CHS-CNN can be more effective than
conventional LEACH in terms of total energy consumption of
the network and system life time while the CHS-CNN can find
its cluster heads 3 times faster than EBCS.
ACKNOWLEDGMENT
This work was supported by the IT R&D program of The
MKE/KEIT (10040191, The development of Automotive
Synchronous Ethernet combined IVN/OVN and Safety control
system for 1Gbps class).
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