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IPASJ International Journal of Computer Science (IIJCS)
Web Site: http://www.ipasj.org/IIJCS/IIJCS.htm
Email: [email protected]
ISSN 2321-5992
A Publisher for Research Motivation ........
Volume 3, Issue 5, May 2015
LEACH-ENL: LEACH Protocol with Enhanced
Network Lifetime in Wireless Sensor Network
Divyani Garg1, Kanak Soni2, Vatika Goswami3, Rajeshwari Porwal4 , K.Anil Kumar5
1
BE (Computer Science), Indore Institute of Science & Technology-II, Indore
Madhya Pradesh, India
2
3
4
5
Associate Professor, Department of CSE, Indore Institute of Science & Technology-II, Indore
ABSTRACT
Research on Wireless Sensor Networks has recently received much attention as they offer an advantage of monitoring various kinds
of environment by sensing physical phenomenon. Wireless sensor networks (WSNs) are networks of large number of tiny, battery
powered sensor nodes having limited on-board storage, processing, and radio capabilities. Nodes sense and send their reports toward
a processing center which is called “sink or base station.” Since the transmission and reception process consumes lots of energy as
compare to data processing, Designing protocols and applications for such networks has to be energy aware in order to prolong the
lifetime of the network. Energy saving is the crucial issue in designing the wireless sensor networks. In this paper, a modified
algorithm for Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is proposed known as “LEACH with Enhanced
Network Lifetime protocol (LEACH-ENL) for clustered WSN” is aimed at prolonging the lifetime of the sensor networks by
balancing the energy consumption of the sensor nodes. The proposed protocol adds feature to LEACH to reduce the consumption of
the network resource in each round. Simulation results using MATLAB shows that the proposed LEACH-ENL protocol is better
than LEACH in balancing node energy consumption, improving the efficiency of data transmission and prolonging the network life.
Keywords: LEACH, Wireless sensor network, Routing protocols, LEACH-ENL
1. INTRODUCTION
Wireless sensor network (WSN) [1] is a self-organized network composed by a large number of micro sensors that are
randomly deployed in monitoring regional through wireless communication. With its wide application in military
reconnaissance, medical aid, logistics management, environmental monitoring, agriculture and other commercial areas,
WSN has become the furthermost technology in the field of communication and computer research [2]. Sensor nodes rely
on battery power supply, their communication capability and energy storage capacity are very limited, so how to utilize the
energy of nodes efficiently, balance the network energy consumption and extend the network lifetime has become a primary
design objective for wireless sensor network.
A sensor network is defined as being composed of large number of nodes with sensing, processing and communication
facilities which are deployed either inside the phenomenon or very close to it. Each of these nodes collects data and route
the information back to a sink [4]. In Current years, Wireless sensor networks becomes the furthermost exciting networking
technologies to offer the sensed collected data to the base station with restricted power ability . Sensor nodes are battery
driven devices with restricted energy resources. Once installed, the minor sensor nodes are usually unapproachable to the
operator, and thus auxiliary of the energy source is not practicable. Stretching network lifespan for these nodes is a vital
issue [5]. Sensor networks may consist of many different types of sensors such as seismic, low sampling rate magnetic,
thermal, visual, infrared, and acoustic and radar. Applications of the WSNs include to monitor a wide variety of ambient
conditions like temperature, humidity, vehicular movement, lightning condition, pressure, soil makeup, noise levels, In
Military for target field imaging, Earth Monitoring, Disaster management. Fire alarm sensors, Sensors planted underground
for precision agriculture, intrusion detection and criminal hunting [4].
In general, routing in WSNs can be divided into flat-based routing (data-centric routing), hierarchical-based routing, and
location-based routing depending on the network structure. In hierarchical-based routing, nodes will play different roles in
Volume 3 Issue 5 May 2015
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ISSN 2321-5992
the network. The main aim of hierarchical routing is to efficiently maintain the energy consumption of sensor nodes by
involving them in multi-hop communication within a particular cluster. Here data aggregation and fusion is performed in
order to decrease the number of transmitted messages to the sink. Here all nodes get a chance to become cluster head for the
cluster period [3]. LEACH is one of the widely used dynamic clustering hierarchical routing protocols for sensors networks
[3]. In the following section, we will describe LEACH protocol and its shortcomings. To avoid the shortcomings of LEACH
protocol here new LEACH-ENL protocol is proposed to reduce average energy consumption of network and enhance the
network lifetime which ensures high availability of sensor nodes and so high reliability of data transmission to sink node
which ultimately makes the entire network reliable.
A. Wireless Sensor Network Structures
A typical sensor network includes sensor nodes, sink nodes (Nodes) (Sink), infrastructure network (Internet or satellite) and
a sensor network node management. The communication distance is short, only with their neighbors to exchange data
within communication range to access the communication outside the range of node, must use multi-hop routing. In order
to guarantee the network most nodes can establish a wireless link with the gateway node distribution nodes must be density.
Sink node with Internet or satellite communication, the whole area of the data is transmitted to the remote monitoring
center for centralized treatment. The user through the management node of sensor network configuration and management,
release detection task and collect monitor data, as shown in figure 1. In the vast majority of nodes in sensor networks have
only a small range of the transmitter, and the Sink node transmission capability, high power, the data can be sent back to
the remote control node [6].
Figure 1: Wireless Sensor Network Structure
B. Wireless Sensor Network Protocol Architecture
Figure 2 shows the architecture of wireless sensor network protocol. This architecture has a hierarchical network
communication protocol, the application layer; transport layer, network layer, data link layer, physical layer, and the
internet protocol layer five protocol corresponding. As well as the protocol architecture also includes three management
platforms, respectively, energy management platform, mobile management platform and mission management platform.
Among them, energy management platform to the control node to the use of energy plays a main role. Certain application
conditions, some nodes may be mobile. Mobile management platform for detection and control of mobile node, the
maintenance of the converging point of the route can make the sensor node to trace its neighbors. Mission management
platform is the role of balancing and scheduling of a particular area of assigned tasks [7].
Figure 2: Wireless Sensor Network Protocol Architecture
The rest of the paper is organized as follows. Section 2 describes the LEACH Protocol. Problem formulation and solution is
presented in section 3. Section 3 includes two ray propagation model, first order radio propagation model and system model.
Section 4 describes generalized algorithm for LEACH-ENL Protocol. Section 5 addresses the Simulation experiments which
includes settings and environment. Section 6 represents the implementation part. Result Analysis of LEACH and LEACHENL Protocol is covered under Section 7. Concluding remarks and future work directions are provided in Section 8.
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2. RELEVENT WORK
A. Low-Energy Adaptive Clustering Hierarchy
(LEACH)
As we all know that all the networks have a certain lifetime during which nodes have limited energy by using that, the
nodes gather, process, and transmit information. This means that all aspects of the node, from the sensor module to the
hardware and protocols, must be designed to be extremely energy-efficient. Decreasing energy usage by a factor of two can
double system lifetime, resulting in a large increase in the overall usefulness of the system. In addition, to reduce energy
dissipation, protocols should be robust to node failures, fault-tolerant and scalable in order to maximize system lifetime [8].
LEACH is the first network protocol that uses hierarchical routing for wireless sensor networks to increase the life time of
network. All the nodes in a network organize themselves into local clusters, with one node acting as the cluster-head. All
non-cluster-head nodes transmit their data to the cluster-head, while the cluster-head node receive data from all the cluster
members, perform signal processing functions on the data (e.g., data aggregation), and transmit data to the remote base
station. Therefore, being a cluster-head node is much more energy-intensive than being a non-cluster-head node. Thus,
when a cluster-head node dies all the nodes that belong to the cluster lose communication ability [10] [11].
LEACH incorporates randomized rotation of the high-energy cluster-head position such that it rotates among the sensors in
order to avoid draining the battery of any one sensor in the network [9]. In this way, the energy load associated with being a
cluster-head is evenly distributed among the nodes. The operation of LEACH is divided into rounds. Each round begins
with a set-up phase when the clusters are organized, followed by a steady-state phase where several frames of data are
transferred from the nodes to the cluster-head and onto the base station [12].
Figure 3: Clustering in LEACH
In LEACH, nodes take autonomous decisions to form clusters by using a distributed algorithm without any centralized
control. Here no long-distance communication with the base station is required and distributed cluster formation can be
done without knowing the exact location of any of the nodes in the network. In addition, no global communication is needed
to set up the clusters. The cluster formation algorithm should be designed such that nodes are cluster-heads approximately
the same number of time, assuming all the nodes start with the same amount of energy [11]. Finally, the cluster-head nodes
should be spread throughout the network, as this will minimize the distance the non-cluster-head nodes need to send their
data. A sensor node chooses a random number, r, between 0 and 1. Let a threshold value be
T (n): T (n) = p/1-p ×(r mod p-1).
If this random number is less than a threshold value, T (n), the node becomes a cluster-head for the current round. The
threshold value is calculated based on the above given equation that incorporates the desired percentage to become a clusterhead, the current round, and the set of nodes that have not been selected as a cluster-head in the last (1/P) rounds, p is
cluster head probability. After the nodes have elected themselves to be cluster-heads, it broadcasts an advertisement message
(ADV). This message is a small message containing the node's ID and a header that distinguishes this message as an
announcement message. Each non-cluster-head node determines to which cluster it belongs by choosing the cluster-head
that requires the minimum communication energy, based on the received signal strength of the advertisement from each
cluster-head. After each node has decided to which cluster it belongs, it must inform the cluster-head node that it will be a
member of the cluster. Each node transmits a join-request message (Join-REQ) back to the chosen cluster-head. The clusterheads in LEACH act as local control centers to co-ordinate the data transmissions in their cluster [11]. The cluster-head
node sets up a TDMA schedule and transmits this schedule to the nodes in the cluster. This ensures that there are no
collisions among data messages and also allows the radio components of each non cluster-head node to be turned off at all
times except during their transmit time, thus minimizing the energy dissipated by the individual [11] [13] [14].
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3.PROBLEM IDENTIFICATION AND WORK DONE
The objective of this section is to propose the improved routing technique that is used to form most appropriate clustering
and selection of cluster heads, which reduces average energy consumption and enhance the network lifetime by balancing
load of network among all active participant sensor nodes. The key ideas of proposed technique are as follows:
1. It is well known [16] that the energy spent in transmission over a wireless medium is directly proportional to dC, where d
is the transmission range and c is a constant between 2 to 4. Moreover the increase in transmission range beyond certain
value decreases the lifetime of the sensor network [17].
2. LEACH-ENL considers least distant from the center of cluster as a criterion for a node to be chosen as a cluster head
(CH) during cluster head selection procedure (from second round onwards), whereas LEACH protocol does random
selection of CHs, this again may lead to poor to very poor selection of CHs which will consequently lead to highly
inefficient energy retention by the network.
3. Minimize the total energy spent by all nodes in the round. Minimize the total energy spent in the network such that the
maximum energy spent by a node in a round is minimized.
A. Two Ray Radio Propagation Model
Figure 4: Simplified model of ground reflection causing signal interference at the receiver.
In network simulation, fading due to large-scale path loss, deterministic small-scale fading, or probabilistic attenuation
effects is most commonly calculated as a sum of independent loss processes. Figure 4: Simplified model of ground reflection
causing signal interference at the receiver. Path loss, which we focus on in this paper, is often estimated assuming free
space propagation, taking into account distance d and wavelength only.
However, more realistic treatment of the path loss takes the fact into account that radio propagation will commonly suffer
from at least one notable source of interference, namely ground reflection, as illustrated in Figure 4. A physically more
correct approximation of path loss must therefore be based on the phase difference of interfering rays ' and a reflection
coefficient leading to a Two-Ray Interference model,
Apparently, calculation is more complex than the much more simple calculation of path loss according to the free space
model. The calculation of interference between line-of-sight and reflected rays can be simplified to yield a path loss
according to what is commonly termed the Two-Ray Ground path loss.
This has led many common network simulators to pick up the Two-Ray Ground model as an option for simulating path loss
in radio transmissions, using a cross-over distance dc for switching [20].
B. First Order Radio Propagation Model
Figure 5: Radio energy dissipation model
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We considered the first order radio model for calculation of the energy dissipation for data communication operations like
transmission and reception. This is one of the most widely accepted and used models in literature for sensor network
simulations and theoretical analysis. In this model, the transmitter dissipates energy to run the radio electronics and the
power amplifier, and the receiver dissipates energy to run the radio electronics. The first order radio model can be divided
into free-space model and multi-path fading model according to the distance between the sending node and receiving node.
The protocol assumes that the communication channel is symmetrical; the energy consumption of l bits message between
two nodes with a distance of d can be shown as equations (2) and (3) [19].
ETx(k,d) = ETx-elec(k) + ETx-amp(k,d)
ETx(k,d) = Eelec*k + Єamp*k*d2
(2)
And to receive this message, the radio expands:
ERx(k) = ERx-elec(k)
ERx(k) = Eelec*k
(3)
C. System model
The following assumptions are:
1. Each sensor node has a unique pre-configured id, fixed transmission range and the same amount of initial energy.
2. The sensor network is proactive [15] and each node generates equal amount of data per unit time. Each data unit
(packet) is of the same length.
3. The transceiver exhibits first order radio model characteristics [18], where energy dissipation for the transmitter or
receiver circuitry is constant per bit communicated. In addition, we assume that energy spent in transmitting a bit over
a distance d is proportional to d2 in [6], the transmission energy was assumed to be proportional to d4.The main
conclusions of our work remain valid for this assumption as well.
4. We consider equal periods of time called rounds. At the beginning of each round, routing information is recalculated to
account for changes in the topology.
5. Nodes are left unattended after deployment. So, battery re-charge is not possible.
6. All the nodes are homogeneous and no mobility of sensor node.
4.ALGORITHM
//n is the set of all nodes in a network, r is number of rounds, p is probability to be a cluster head
Step 1: In network all n number of nodes is randomly deployed.
Step 2: Initially equal amount of energy supplied to all normal nodes.
Step 3: 0.1 percentage of nodes are chosen randomly taken as advanced nodes and posses œ (alpha) higher energy then the
normal nodes.
Step 4: Cluster heads are elected using formula(p/(1-p*mod(r,round(1/p))))
Step 5: Each non-head node selects the nearest Cluster Heads.
Step 6: The nodes transmit data to their respective heads.
Step 7: Each head transmits the aggregated data to the BS via the shortest path.
5.SIMULATION EXPERIMENTS
This section evaluates the performance of LEACH-ENL by using MATLAB simulation Tool.
A. Experiment setting:
The comparison results of LEACH-ENL protocol over LEACH protocol by using MATLAB simulation.
1. All the nodes are deployed in the area randomly.
2. The base station is located in the center position of the experimental region.
3. All the nodes have the same initial energy.
4. The base station knows the ID and location of each node.
5. The communication link between the nodes is symmetrical, and has the same effective communication distance.
We assume that there are deployed variable sensor nodes randomly in a two-dimensional area of 100m × 100m square.
Each normal node is in the same initial state, and having the same initial energy. The base station is located in (50, 50).
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B. Simulation Environment
The various parameters used in the simulation are shown in below:
SIMULATION
PARAMETERS
Simulator
Area of simulation(Max)
PARAMETER
VALUE
MATLAB
1500 \ 1500 m2
Position of Node S
Number of Nodes
Physical/MAC
protocol
Node mobility model
(50,50)
20,40,60,80,100,200
802.15.4
layer
Schedulers/Queues
Battery model
Transmission power
Packet Size
Transport Layer
Initial Energy
Efs, the average amount of
energy
consumed
for
aggregating the data
Emp, energy consumed per
bit in the transmitter
amplifier
Full battery capacity
Traffic type
Source ID
Destination ID
Start/End time
Random waypoint
mobility model
DiffServ/FIFO
Linear Model
3 dBm
4000 bit
UDP
0.5 Joules
1pJ/bit
1.3fJ/bit
1200 mA h (sensor nodes)
12,000 mA h (Sink node)
CBR
Variable
1 (Sink node)
Variable
In the simulation, we compared the performance of our proposed LEACH-ENL algorithm and with LEACH protocol in
under the continuous delivery model. Our performance metrics are total residual energy per round in the network and total
network lifetime, means number of alive nodes per round. Network lifetime is the number of round from the start of
operation until the death of the last alive node. The network connectivity which depends on the time of the first node failure
is a meaningful measurement in the sense that a single node failure can make the network partitioned and further services
are interrupted. When a sensor node is depleted of energy, it will die and be disconnected from the network which may
impact the performance of the application significantly. The energy consumption due to communication will be calculated
using the first order energy model. We assume that each sensor node generates one data packet per time unit to be
transmitted to the BS.
6.IMPLEMENTATION
A. Random Deployment of sensor nodes
In the first stage all the sensor nodes is randomly deployed in a given area of 100m*100m. Base station in placed in the
center.
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Figure 6: Randomly deployment of sensor nodes in a given area
B. Dead Nodes
Figure 7: When all nodes are dead
C. Recognizing trained network and Evaluation of the optimal cluster head in each cluster
The trained network is recognized and implemented on a given network for optimal and fast cluster head election. Sensor
nodes in each cluster then transmit sensed data to their cluster head and further Cluster head from each cluster transmits its
own data and data from other sensor nodes to Base Station.
7.RESULT ANALYSIS
When the nodes start with the same initial energy and the total number of nodes in a network is 100, the number of dead
nodes per round for both LEACH and LEACH-ENL is shown in Figure 6.
Below figures shows the mean number of nodes still alive at each round. The X-axis shows the no. of sensor nodes while the
left Y-axis shows the Network Lifetime (in sec). If the number of nodes is 100, the ratio of cluster heads to the other nodes
is 5% and every node’ energy level is equal to 0.5 J.
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Figure 8: Number of dead nodes per unit time for both LEACH and LEACH-ENL for 20 Nodes
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Figure 12: Number of dead nodes per unit time for both LEACH and LEACH-ENL
Figure 13: Number of dead nodes per round for both LEACH and LEACH-ENL
The nodes are started to die in LEACH from the initial few rounds whereas in LEACH-ENL, 1st node died later. Average
energy consumption of LEACH-ENL is less than that of LEACH protocol. It shows that the total network lifetime of our
algorithm is longer than that of LEACH.
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During most of the network lifetime, LEACH-ENL runs with much more living nodes than LEACH. From our simulations,
LEACH-ENL protocol has the following advantages:
 The stability period of the LEACH-ENL is prolonged than LEACH.
 By uniform clustering and distance data structure load is balanced among all the active participant nodes of the network
so energy dissipation is also balanced so lifetime is increased as compared to LEACH protocol.
To sum up, in our simulation we obtained a prolonged stability period and a reduction in the instability region in the
network lifetime
8.CONCLUSION
WSNs are increasingly being used for health care, transportation, manufacturing, and much more. Routing in sensor
networks is an emerging area of research. In this paper we present an improved version of LEACH protocol, LEACH-ENL,
to extend the lifetime of a sensor network by using first order radio propagation model and balancing the load of entire
network among all active nodes. The total network lifetime of our algorithm is longer than that of LEACH. During most of
the network lifetime, LEACH-ENL runs with much more living nodes than LEACH. The simulation results show that the
proposed algorithm can maintain a balanced energy consumption distribution among nodes in a sensor network and thus
prolong the network lifetime.
REFERENCES
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selection [C]// Proc of the 4th IEEE Conf. on Mobile and Wireless Communications
[3] Jamal N. Al-Karaki, The Hashmite University Ahmed E. Kamal, Lowa state University, “Routing techniques in WSN:
A survey”, IEEE Wireless communication, 2004
[4] Parul Bakaraniya , Sheetal Mehta , “Features of WSN and Various Routing Techniques for WSN : A Survey”,
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[5] Naveen Kumar, Mrs. Jasbir Kaur, “Improved LEACH (I-LEACH) Protocol for Wireless Sensor Networks” , IEEE 2011
[6] K.Wong, “Physical layer considerations for wireless sensor networks networking”, Proceedings of International
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