The document presents a new energy-efficient clustering-based communication protocol for wireless sensor networks (WSNs), which modifies the existing LEACH algorithm to improve energy consumption. The proposed technique enhances data transmission by electing cluster heads and using data aggregation, allowing nodes to forward data to the nearest cluster head instead of directly to the sink. Simulation results demonstrate significant improvements in energy efficiency and network lifetime compared to the traditional LEACH protocol.

1. A New Energy Efficient Clustering based
Communication Protocol for Wireless Sensor
Networks
Aman Singhal1
and K.K. Shukla2
1
Department of Computer Science & Engineering IIT (BHU), Varanasi, India
Email: aman.singhal.cse09@iibhu.ac.in
2
Department of Computer Science & Engineering IIT (BHU), Varanasi, India
Abstract— Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
Index Terms— Wireless sensor networks (WSN), clustering algorithm, data aggregation,
energy efficient communication.
I. INTRODUCTION
Wireless Sensor Networks (WSN) have drawn a lot of attention in recent times due to their wide range of
applications in various fields. WSN can be deployed in places where human presence is not feasible such as
military and environment monitoring [1]. WSN are also useful in field monitoring to observe any abnormal
growth behaviour in different types of plants.
In WSN, number of nodes may vary from a few hundred to several thousands. Nodes send data collectively
to a base station in some predefined manner. Each node may have several parts including, radio transceiver,
microcontroller and a battery [5]. Usually, the battery has limited power supply, and reception and
transmission of data spends a considerable amount of energy. Also, reinstallation of batteries in so many
nodes is not feasible. So this process of data reception and transmission should be as energy efficient as
possible so as to maximize the lifetime of WSN. Also sensors in WSN have lot of data and for user to make
use of that data, aggregation techniques [3, 4] are needed to be applied. So, in order to conserve energy,
nodes can form clusters and instead of sending data directly to sink, each node can send their data to the
cluster head. Then cluster head can apply data aggregation techniques to remove redundancy and then can
DOI: 02.ITC.2014.5.69
© Association of Computer Electronics and Electrical Engineers, 2014
Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC

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send the data to the sink. This is the idea used in LEACH [2] protocol.
There are various energy efficient communication protocols available for WSN. We make use of improved
LEACH to select cluster head as the first step of our algorithm. Then we apply data aggregation technique at
each cluster head and then each cluster head forwards its data to the nearest cluster head in forward direction
or to the sink.
In the rest of the paper first the preliminaries are described, then the proposed scheme is illustrated and in the
last section the simulation results of proposed scheme are discussed.
II. PRELEMINARIES
A. Wireless Sensor Network Model
The sensor network model considered in this project has following characteristics:
 The sensor field consists of n sensor nodes scattered randomly in a fixed sized field. Once deployed,
positions of these sensors are fixed in the field.
 Sink is at distance ds far away from the sensor field whose position is also fixed.
 Each of these sensors is provided an initial fixed energy.
 Each sensor performs one of the two tasks: sensing the data or being the cluster head.
Data sensed by the sensor is forwarded to the cluster head of the cluster to which it belongs, which in turn
aggregates the data and forwards that data to either sink or a cluster head which is closer to it than sink.
B. Energy Model [2]
While forwarding and receiving the data there is some amount of energy spent at each node. This energy
depends upon the distance travelled by the data and number of bits in the data. Model (Fig. 1) for energy
expansion is given by:
To transmit a k bit message at distance d, energy expansion at transmitter is:
ETx(k, d) = ETx-elec(k) + ETx-amp(k, d) (1)
ETx(k, d) = Eelec*k + Efs*k*d2
(2)
To receive this message, energy expanded at receiver is:
ERx(k) = ERx-elec(k) (3)
ERx(k) = Eelec*k (4)
If distance d>dth, the energy dissipated is given by:
ETx(k, d) = Eelec*k + Eamp*k*d4
(5)
where, dth is given by:
dth = √( Efs / Eamp ) (6)
C. Data Aggregation Model [3]
For data aggregation, simple model is used. This model simply reduces the redundant data. In this model, if
there are x nodes in a cluster and each of the node sends a packet of same length to cluster head, then at
cluster head there are x packets each of same length. After data aggregation, cluster head produces ζ(x)
packets of same length. So the number of packets in the output is a function of number of packets in the input
given by:
ζ(x) = m*x + c (7)
where,
c: overhead of aggregation
m: the compression ratio.
We can say that 0 ≤ m ≤ 1 because after aggregation, number packets in the output will be lesser than the
number of packets in the input.
If m = 0 and c > 0 then it corresponds to the case when any number of packets can be compressed into a
single packet of fixed length [3]. We are considering this scenario into our simulations.

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Energy expansion for data aggregation is given by:
EDAx(k) = EDA*k (8)
where, EDA represents energy for data aggregation.
Figure 1: Energy model used [2]
III. PROPOSED SCHEME
The proposed scheme can be subdivided into following phases:
A. Selection of Cluster-Head
After the deployment of nodes in the sensor field, cluster heads are chosen according to improved LEACH
(QBCDCP)[4]. In this scheme, every node i selects a random number between 0 and 1. If this number is
found below a threshold T(i), the node is decided as a cluster-head for current round. In this case, the
threshold T(i) is defined as:
T(i) =
*
_
_
+ ( ∗ ) 1 −
_
_
if iϵG
(9)
0 otherwise
where,
P : the desired percentage of cluster heads
G : the set of nodes that have not been cluster heads in last 1/P rounds
rs : the number of successive rounds in that a node has not been cluster head. It is reset to 0 after a node
becomes cluster head in a round and is increased for each round in which it doesn’t become cluster head.
r : current round.
Ei_current : current energy of node i
Ei_max : initial energy of node i
Each node, that has elected itself as a cluster-head for the current round, broadcasts an advertisement
message to the rest of the nodes.
B. Cluster Set-up Phase
Each node calculates its distance from each of the cluster-head. It will belong to the cluster for which
distance from that cluster head and node is minimum. 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.
C. Data transmission Phase
Once the clusters are created and the schedule is fixed, data transmission can begin. If we assume that each
node always has data to send, then they send it during their allocated transmission time to the cluster head.

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Data aggregation is applied at each cluster head. But a different strategy is applied before sending the data
collected to the sink.
If the distance of cluster head is greater than threshold distance dth, then following strategy is used:
 Distances between this cluster head and all other cluster heads, whose distance from the sink is lower than this
cluster head’s distance from the sink, are calculated.
 Minimum of these distances is found.
 All of the data at this cluster head is forwarded to the cluster head whose distance is found minimum in the last
step.
Otherwise, the data collected is sent from the cluster head to the sink directly.
In the algorithm, following symbols are used
Array rs[], where rs[i] represents number of rounds node i has not been cluster head.
Array distance[], where distance[ch] represents distance between cluster head ch and sink or another cluster
head to which ch forwards its data.
Array E[], where E[i] represents remaining energy of node i.
Number of nodes is n.
Algorithm- improved wireless communication protocol
1. for each round r, from 1 to rmax do
2. for each node i do
3. calculate T(i)
4. select a random number between 0 and 1.
5. if T(i) is greater than this number then
6. select this node as a cluster-head and set rs[i] = 0;
7. else
8. rs[i] ← rs[i] + 1;
9. end
10. end
11. for each cluster-head ch do
12. distance[ch] = distance(ch, sink)
13. if distance(ch, sink) > dth then
14. mindist ← ∞
15. for each ch1 other than ch do
16. if distance(ch1, sink) < distance(ch, sink) then
17. mindist ← min(distance(ch1, ch), mindist)
18. end
19. end
20. if mindist ≠ ∞ then
21. assign forward_cluster[ch] as ch1 with mindist
22. distance[ch] ← distance(ch1, ch)
23. end
24. end
25. if distance[ch] > dth then
26. E[ch] ← E[ch]–{(Eelec+EDA)*k + Eamp*distance[ch]4
}
27. else
28. E[ch] ← E[ch]–{(Eelec+EDA)*k + Efs*distance[ch]2
}
29. end
30. end
31. for each node i do
32. calculate distance(i, ch) for all ch
33. find min_dis as minimum of these distances and ch1 as cluster-head for which this min_dis
comes
34. if min_dis > dth then
35. E[i] ← E[i]–(Eelec*k + Eamp*min_dis4
)
36. else

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37. E[i] ← E[i]–(Eelec*k + Efs*min_dis2
)
38. end
39. E[ch1] ← E[ch1] – (Eelec+EDA)*k
40. end
41. end
Functions used:
distance(p1, p2): returns the Euclidean distance between p1 and p2.
min(a,b): returns the minimum of a and b.
In the above algorithm,
Lines 3-9 represent cluster-head selection phase. Complexity of this phase O(n).
Lines 13-24 represent, for cluster head ch, selection of nearest cluster head, if sink is located far than dth.
Lines 32-33 represent, for node i, selection of cluster head nearest to i.
D. Illustration
Figure 2: Algorithm illustration
Proposed technique can be best illustrated with the help of Fig. 2.
If we suppose that all shaded nodes are cluster-heads and distances between A and Sink, C and Sink, and D
and Sink all are greater than dth. These distances are shown by shaded edges in the figure. Now each of these
cluster-heads, A, C and D, will find a cluster-head which is nearest to them and has distance to sink lower
than their own distance to Sink.
For A, that cluster-head comes out to be B. Now, instead of sending directly to Sink, A will send all data
collected at A to B.
Similarly, distance between C and sink is also greater than dth. So, C will find a cluster-head whose distance
from Sink is lower than this distance. For C, that cluster-head comes out to be D. Now, instead of sending
directly to Sink, C will send all data collected at C to D.
Now, D is also having its distance from sink greater than dth. This time D will find the forwarding cluster-
head as E. Now, instead of sending directly to Sink, D will send all data collected at D to E.
These paths are shown by dark arrows in the network diagram.
As it is demonstrated in the figure, distance that had to be covered from a cluster-head to sink has been
reduced significantly. This is the main reason for the improvements observed in the proposed algorithm.
IV. SIMULATION RESULTS
Implementation of the proposed technique was simulated using MATLAB. Values taken for different
parameters were:
 n (no of nodes) = 100
 Size of field = 100x100
 Location of sink = (150, 50) and (200, 50)
 Initial energy = 1 J
 P = 0.1 or (10%)

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 Eelec = 50 nJ/bit
 Efs = 10 pJ/bit/m2
 EDA = 5 nJ/bit/message
 Eamp = 0.0013 pJ/bit/m4
 dth = √( Efs / Eamp )
 k = 4000
 c (overhead of aggregation) = 1
 m (compression ratio) = 0
These parameters are similar to the ones used in [6]. Using these parameters values experiments were
performed and following results were found (Fig. 3 & 4).
On comparison of the results of proposed scheme with that of LEACH, we can easily say that proposed
scheme is showing almost 160% improvement in first case while 90% in second case. For these simulations
we have taken compression ratio as 0.
V. CONCLUSION AND FUTURE WORK
In this paper we described an energy efficient communication protocol for wireless sensor networks. First we
introduced the need of energy efficient communication protocol. Then we discussed the model used. Then
the proposed scheme was described and finally results obtained from simulation of proposed scheme with the
help of MATLAB were shown.
As part of future scope, modifications in the selection of cluster head can be done. Also, security aspects of
WSN can be included while selecting cluster head.
Figure 3: Bar plot of results when sink is at (150, 50)
Figure 4: Bar plot of results when sink is at (200, 50)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
First Node
Dead
25th Node
Dead
50th Node
Dead
75th Node
Dead
100th Node
Dead
Lifetime(innumberofrounds)
LEACH
Proposed
0
500
1000
1500
2000
2500
3000
First Node
Dead
25th Node
Dead
50th Node
Dead
75th Node
Dead
100h Node
Dead
Lifetime(innumberofrounds)
LEACH
Improved

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REFERENCES
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[3] Vivek Mhatre, Catherine Rosenberg, “Design guidelines for wireless sensor networks: communication, clustering
and aggregation”, Elsevier’s J on Ad Hoc Networks, 2 (2004) pp. 45–63.
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[5] Wireless Sensor Network: http://en.wikipedia.org/wiki/Wireless_sensor_network
[6] Ezzati Abdellah et al, “Advanced Low Energy Adaptive Clustering Hierarchy”, (IJCSE) International Journal on
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