1. 2009 First International Conference on Networks & Communications
Neural Network Based Energy Efficient Clustering and Routing in Wireless Sensor
Networks
1
Neeraj Kumar, 2 Manoj Kumar, 3R.B. Patel
1,2
School of computer Science and Engineering, SMVD University, Katra (J&K), India
3
Department of Computer Science and Engineering, MM University, Mullana (Ambala), Haryana
E-mail: nehra04@yahoo.co.in,vermamk@gmail.com, patel_r_b@yahoo.com
*
, R.B. Patel
Shri Mata Vaishno Devi University, Katra(J&K), India
Abstract
power radio transmission is employed, wireless
Energy is a valuable resource in Wireless Sensor communication is far from being perfect [4,5,6].
Networks (WSNs). The status of energy consumption In this paper, we address the issue of energy-efficient
should be continuously monitored after network clustering and routing in WSNs using neural networks
deployment. The information about energy status can be with the objective of maximizing the network lifetime.
used to early notify both sensor nodes and Network First, we propose an efficient neural network based
Deployers about resource depletion in some parts of the clustering algorithm for WSNs. Secondly, we propose
network. It can also be used to perform energy-efficient routing and data transmission algorithm for WSNs. We
routing in WSNs. In this paper, we propose a neural define an efficient metric to be used in taking the
network based clustering and energy efficient routing in selection of next hop in routing. The problem is
WSN with the objective of maximizing the network lifetime. formulated as LP with specified constraints and routing
In the proposed scheme, the problem is formulated as metric
linear programming (LP) with specified constraints. Rest of the paper is organized as follows: Section 2
Cluster head selection is done using adaptive learning in discusses related work, section 3 defines the energy
neural networks followed by routing and data model used along with the defined routing metric and
transmission. The simulation results show that the problem formulation, Section 4 describe the proposed
proposed scheme can be used in wide area of solution, Section 5 provides the simulation and results
applications in WSNs. obtained, and finally Section 6 concludes the article.
Keywords: Sensor networks; Energy Efficient Routing,
Linear Programming, Neural Networks. 2. Related Work
There are number of clustering protocols have been
1. Introduction proposed in literature e.g. LEACH [7], PEGASIS [8],
Wireless Sensor Networks (WSNs) is a class of wireless HEED [9], EEUC [10], and FLOC [11]. The cluster
ad hoc networks in which sensor nodes collect, process, formation overhead of the clustering protocols includes
and communicate data acquired from the physical packet transmission cost of the advertisement, node
environment to an external Base-Station (BS). But these joining and leaving, and scheduling messages from
networks have several challenges such as sensor nodes in sensor nodes. All these protocols do not support adaptive
WSNs are normally battery-powered, and hence energy multi-level clustering, in which the clustering level
has to be carefully used in order to avoid early cannot be changed until the new configuration is not
termination of sensors’ lifetimes [1]. As such, the made. Therefore, the existing protocols are not adaptable
concept of continuous monitoring of network resources to the various node distributions or the various sensing
becomes a very important topic in WSNs. This same area. If the sensing area is changed by dynamic
concept has been already investigated in many other circumstances of the networks, the fixed-level clustering
environments, e.g., power plants [2], and in many protocols may operate inefficiently in terms of energy
distributed systems [3]. consumption.
Many recent experimental studies have shown that, Bandyopadhyay and Coyle [12] proposed the
especially in the field of sensor networks where low randomized clustering algorithm to organize sensors into
978-0-7695-3924-9/09 $26.00 © 2009 IEEE 34
DOI 10.1109/NetCoM.2009.56
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2. clusters in a wireless sensor network. Computation of the 3.2 Energy Model
optimal probability of becoming a cluster head was To ascertain the amount of energy consumed by a radio
presented. Moscibroda and Wattenhofer [13] defined the transceiver, we apply the following energy model. For
maximum cluster-lifetime problem, and they proposed each packet transmitted by a sending node to one or
distributed, randomized algorithms that approximate the more receivers in its neighborhood, the energy is
optimal solution to maximize the lifetime of dominating calculated as according to [7]:
sets on wireless sensor networks. Pemmaraju and e = et + ne r + ( N − n)e h r ………………………(1)
Pirwani []14 considered the k-domatic partition problem,
Where et and e r denote the amount of energy required
and they proposed three deterministic, distributed
algorithms for finding large k-domatic partitions. Tan to send and receive, n the number of nodes which should
and Korpeoglu [15] proposed two new algorithms under receive the packet, and N the total number of neighbors
the name PEDAP, which are near optimal minimum in the transmission range. e h r quantifies the amount of
spanning tree based wireless routing scheme. The energy required to decode only the packet header
performance of the PEDAP was compared with LEACH According to model described in [7], et and e r are
and PEGASIS, and showed a slightly better network defined as
lifetime than PEGASIS. Yi et. al[16] presents a Power
efficient and adaptive clustering protocol PEACH et (d , k ) = (e elect + e amp * d ρ )8k
………………..(2)
e r (k ) = e elect * 8k
3 Models, Routing Metric and Problem Formulation
for a distance d and a k byte message. We have
3.1 Network Model
We consider a network of homogeneous and energy- set e elect = 70nJ / bit , e amp = 120 pJ / bit / m 2 , d = 50m ,
constrained sensor nodes that are randomly deployed in a ρ=4
sensor field. Sensor nodes are initially powered by
batteries with full capacities. Each sensor collects data For a given header size n bytes, e h r would be
which are typically correlated with other sensors in its accordingly calculated.
vicinity, and then the correlated data is sent to the BS via
Cluster Head (CH) for evaluation or decision making 3.2 Routing Metric and Problem Formulation
purposes. We assume periodic sensing with the same A proper routing metric has to be chosen that can be used
period for all sensors. To facilitate the operation of the to decide the next hop for data transmission. This metric
network, we apply a novel clustering scheme that results always ensure the best shortest route and incurs the least
in selection of cluster. Inside each fixed cluster, a node is energy to transmit the packet from source to destination.
periodically elected to act as CH through which The cost of a link between two nodes S i and S j is equal
communication to/from cluster takes place. to the energy spent by these nodes to transmit and to
BS receive one data packet, successfully.
Source The metric chosen is Routing cost is calculated as
follows:
⎛ Ei D ⎞
R_C =⎜ ⎟ , ………..(3)
⎜ Et ( S i , S j ) + E r (S i , S j ) ⎟
⎝ ⎠
Where E i D is energy associated with the delivery ratio
of the packet originating from source node S i and
correctly received at destination node, while
E t ( S i , S j ) .is the energy used in transmitting from S i to
S j and E r ( S i , S j ) is the energy used in receiving the
packet. Data routing from every cluster head to the sink
is done over multi-hop paths, which is given by
Sensors
CH BS
minimizing equation (3)
Fig.1: Data Transmission in typical Sensor Networks 4 Proposed Solution
The solution for the energy aware routing problem is
proposed using an LP formulation. The objective of the
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3. LP is to select a number of nodes with higher levels of
residual energy to form an optimal route, while Output Layer
Elected
minimizing the total routing cost. Let us label the base- CH
station as node 0 and label the CH nodes as nodes 1 to n,
where n is the total number of CH sensor nodes. So the
Competition
problem reduces to Layer
Minimize ∑ R_C
1≤i ≤ n
Subject to following constraints
δ ( s, w 2 ) δ ( s, wm )
∑Dij −
1≤ j ≤ n
∑
D ji = bi ………….(4)
1≤ j ≤ n
δ ( s, w1 )
Dij ≥ 0 , 1 ≤ j ≤ n ………….….(5)
E ≤ Pmax imum ……………………(6)
Input Layer
Constraint (4) specifies the amount of data transmitted bi
between two nodes S i and S j , Constraint (5) specifies
amount of data to be transmitted from two nodes
S i and S j , Constraints (6) guarantees a minimum node Input Sensor node for election to be CH
lifetime and limits the maximum power consumption of
any node in the network. Fig.2: Selection of CH
The proposed protocol is divided into two phases namely Neural networks have solved a wide range of
as: setting up phase and energy aware routing and data problems and have good learning capabilities. Their
transmission phase. strengths include adaptation, ease of implementation,
parallelization, speed, and flexibility. A two - layer feed
4.1 Setup Phase forward neural network that implements the idea of
In this part, the initial cluster head selection and cluster competitive learning is depicted in Figure 2 above. The
formation algorithm are introduced , followed by the nodes in the input layer admit input patterns of sensor
energy aware routing. nodes competing for CH and are fully connected to the
output nodes in the competitive layer. Each output node
Cluster Head Election corresponds to a cluster and is associated with
To ensure balanced energy consumption among the weight W j , j = 1,2,...., m , where m is the number of
cluster head nodes throughout the network lifetime, clusters.
many clustering protocols favor uniformly distributed The neurons in the competitive layer then compete
clusters with stable average cluster sizes [7-11]. But we with each other, and only the one with the smallest
propose a new neural network based coverage aware
E i D value becomes activated or fired. Each neuron in the
clustering algorithm. The set of cluster head nodes can
be selected based on the cost metric defined in equation proposed algorithm for CH selection has an adaptive
3. The densely populated parts of the network will be learning. The learning rate μ determines the adaptation
overcrowded with cluster head nodes, while the scarcely of the vector towards the input pattern and is directly
covered areas will be left without any cluster head nodes. related to the convergence. If μ equals zero, there is no
In such a situation, it is likely that the high cost sensors learning. If μ is set to one, it will result in fast learning,
from poorly covered areas will have to perform
and the prototype vector is directly pointed to the input
expensive data transmissions to distant cluster head
pattern. For the other choices of μ , the new position of
nodes, further reducing their lifetime. There are three
layers in the proposed neural network: Input layer, the vector will be on the line between the old vector
Competition layer and Output Layer. value and the input pattern. Generally, the learning rate
could take a constant value or vary over time.
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4. routes, the sink node first generates a Route Discovery
1. Initialize the Vector S = {S1 , S 2 ,...., S m } of message that is broadcasted throughout the network.
sensor nodes competing for Cluster head. Upon receiving the broadcast message, each sensor node
//Processing at Input Layer introduces a delay proportional to its cost before it
2. Choose a winner k from sensor nodes as CH forwards the Route Discovery message to nodes in
range R . In this way a message arrives at each node
whose E i D is minimum as follows
along the desired minimum cost path. The cumulative
k = arg min{E i D } // Competition Layer cost of the routing path from the sink to the node
obtained in this phase is called the energy aware routing
3. Also E i D smallest Euclidean distance to BS i.e. cost of the node described in (3).
Ei D = k ∑| S
i =1, 2 ,...m
i − BS | , where k is proponality 1. Sort the paths p1 , p 2 ,....... p m according to
constant E i D as E i D1 < E i +1 D+11 < .......E m Dm
4.Update the value of weight vector as follows: 2. j = 1 // initialize the counter for available
paths.
w j (new) = w j (old ) + μ ( S i − w j (old )) , where 3. repeat and calculate E ≤ Pmax imum (Constraint 6)
μ is learning rate of the neurons. 0 ≤ μ ≤1 4. repeat
5. Repeat Steps (2-4) iteratively. 5.
6. Neuron with smallest value of E i D is E C ,m = ∑ m i E max L // E C ,m is use to
i =1, 2...m
winner.// Output Layer store the minimal energy consumption per bit with
m paths and is assigned maximum value initially.
Fig.3: Algorithm for Cluster head selection 6. R _ C = 0 // initialize the value of routing cost
4.2 Routing and Data Transmission 7. repeat
An algorithm for routing and data transmission is 8. Solve equation (3) and get the corresponding
proposed in Figure 5. optimal energy distribution with respect to
Let us denote by R the maximum number of routes that constraints defined in equations (4),(5),(6).
exist between each source-destination pair, and l indicate
by a route in R. Also, denote by pow( S i , l ) the power
9. Calculate E C ,m = Ei L∑
i =1, 2...m
consumed by node S i in transmitting to the next node on 10. Calculate the value of R _ C from equation (3)
route l . For the sake of simplicity, we assume that this and R _ C updated = R _ C // Update value of routing
parameter depends only on the distance between the cost
transmitting and the receiving node. Then, we associate
11. Until | R − C updated − R − C |< δ 1 (predefined
with each route l an energy cost routing metric defined
in equation (3) above. The proposed algorithm scan all threshold)
routes in R and determine the least expensive route to 12. Update the values of energy for each data
reach the BS. A source will select the route that has the transmission as E C ,m Updated = E C , m
least energy consumption or the one that maximizes the
network lifetime. Until | E C ,m Updated − E C ,m |< δ 2
13. j = j + 1 // Update the counter of the paths
14. Until m > Destination _ node
Source Destination
15. Compare all paths using R _ C metric and select
the smallest one.
16. Send the data across the multiple paths defined.
Fig. 4: Multipath Routing Fig. 5: Algorithm for Routing and Data Transmission
Route Update
The cluster head nodes send their data over multi-hop Given m available paths, the overall energy consumption
paths to the sink as shown in Figure 4. To obtain these per packet, E, can be written as
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5. E= ∑ E L , where E
i =1, 2...m
i i is the energy consumption for
one bit along path i and L is the packet length in bits. 100
Proposed
80 PEACH
5. Simulation and Results
Number of Alive Nodes
We have considered a stationary WSN of size 400×400 60
with a maximum transmission range of 50 m. The
message length is assumed to be 48 bytes, including an 40
12 byte packet header. The energy used to receive and
transmit data is modeled according to the energy model 20
presented in Section 3. Other sources of energy
consumption like sensing, processing, and idle listening 0
are neglected. MAC-layer behaviors such as contention, 1000 2000 3000 4000 5000
duty cycles, or packet buffering are not addressed. We Number of Rounds
have simulated the proposed scheme on ns-2[17]. Fig. 7: Number of alive nodes in PEACH and Proposed
Figure 6 presents the energy consumption of the Scheme
proposed scheme with well known PEACH clustering
protocol [16] when the maximum transmission range is 100
Proposed
60 m. The results demonstrate that the energy PEACH
consumption of proposed neural network based 90
clustering is smaller than PEACH. Percentage of alive Nodes
80
0.10
70
Proposed
PEACH
0.08 60
Mean Residual Energy
50
0.06
40
0.04 50 100 150 200 250 300
Number of Nodes
0.02 Fig. 8: Percentage of alive nodes in PEACH and
Proposed Scheme after 1500 rounds
0.00
1000 2000 The percentage of nodes alive and the mean of residual
3000 4000 5000
energy versus the number of nodes after 1500 rounds are
Number of Rounds
Fig.6: Mean residual Energy in PEACH and Proposed presented in Figures 8 and 9. Proposed scheme has
Scheme highest percentage of residual energy compared with
PEACH protocol. Also, the variation in the mean of
Figure 7 presents the number of nodes alive when residual energy of proposed scheme is smaller than
using clustering in proposed scheme and PEACH. This PEACH
result directly reflects the network lifetime of the
wireless sensor networks. In the case of networks using
PEACH with the maximum transmission range r = 60 m,
where a node runs out of energy occurs nearly after 4000
rounds, while in propped scheme there is a slight
improvement and node runs out of energy in nearly
4200 rounds.
.
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6. [6]. J. Zhao, R. Govindan, Understanding packet
0.10
delivery performance in dense wireless sensor
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[7]. W.R. Heinzelman, A. Chandrakasan, H.
Balakrishnan, Energyefficient communication
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Hawaii International Conferenceon System
0.02
Sciences (HICSS), 2000.
[8]. S. Lindsey, C. Raghavendra, K.M. Sivalingam,
Data gathering algorithms in sensor networks
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