IMPLEMENTATION OF ENHANCED

DISTRIBUTED ENERGY EFFICIENT

CLUSTERING PROTOCOL IN WIRELESS
SENSOR NETWORKS
Under the esteemed guidance of
Dr. D. SRINIVASA RAO
Professor in ECE Department

Submitted By
SAI JOSHITHA ANNAREDDY (17011A0423)
KANNA SANTHI(17011A0428)
VISHAL PANDITA(17011A0444)
BAIRI NAVYA SRI(18015A0403)
 AIM
• The main aim of the project is to implement Enhanced Distributed Energy efficient
Clustering Protocol in Wireless Sensor Networks
• The performance of the EDEEC is compared with other existing clustering protocols such
as DEEC and DDEEC using MATLAB simulation software.

• The performance is compared with various parameters like:

1.Network lifetime(No of alive nodes)
2.Throughput
3.No of Data packets delivered from cluster head to Base station per round
4.Energy Consumption/Total remaining energy per round
5.No of Dead nodes per round
6.No of Cluster Heads formed per round
 Objectives

• To Study about different wireless sensor protocols like

DEEC,DDEEC,EDEEC.

• To implement Enhanced Distributed Energy Efficient Clustering Protocol

using MATLAB.

• To Study about Basics of Matlab required for these protocols and to Simulate
the protocols using Matlab.

• To Compare the performance between DEEC,DDEEC and EDEEC using

MATLAB simulation results.
 Introduction

• Wireless Sensor Networks (WSNs) are envisaged to become the fabric of our
environment and society. It has applications in the management of events, battlefield
surveillance, recognition security, drug identification, and automatic security.

• Wireless sensor network contains hundreds of thousands of sensor nodes. These

nodes are deployed randomly in the area of interest and have a low battery lifetime.
Their lifetime expires when their energy is finished.

• So, energy is a scarce source for wireless sensor networks. We must manage the
right use of energy for increasing sensor lifetime.

• In wireless sensor networks, all sensed data must be sent to a base station called a
sink. One can retrieve required information from the network by injecting queries
and gathering results from the sink.
• Sensing Region: Remote sensing is the process of detecting and monitoring the
physical characteristics of an area by measuring it's reflected and emitted radiation
at a distance (typically from satellite or aircraft).
• Sensor Node: A sensor node is a node in a sensor network that is capable of
performing some processing, gathering sensory information, and communicating
with other connected nodes in the network.
• The main components of a sensor node are a microcontroller, transceiver,
external memory, power source, and one or more sensors.

• Base Station: It act as a gateway between sensor nodes and the end-user as it
typically forward data from the WSN onto a server.
 Clustering Mechanism
The clustering mechanism is the best and most efficient one to resolve the issue
with the requirement of energy in WSN. In clustering, the network is divided into
smaller clusters and each cluster includes a cluster head (CH) and its members. It
is very much useful for reducing energy dissipation and enhancing the lifetime of
the network. There are two types of WSNs that is Homogenous and heterogenous
WSNs.
 DEEC
(Distributed energy-efficient Clustering)
• DEEC is designed to deal with nodes of heterogeneous WSNs.

• Two levels of heterogeneous nodes

a. Normal nodes
b. Advanced Nodes

• The cluster head is elected by a probability based on the ratio between the
amount residual energy present at each node and the average energy of the
network.

• E(r) denotes the average energy of the network during round r which can
be given as:
Probability for CH selection in DEEC is given as
Popt=optimal power consumed by the nodes

In heterogeneous networks
The value of popt is different according to the initial energy of the node. In
two-level heterogeneous network the value of popt is given by:

Then use the above padv and pnrm instead of popt for two level
heterogeneous network as:

…….Equation(a)
 DDEEC
(Developed Distributed Energy Efficient Clustering)
DDEEC implements the same strategy as DEEC in terms of estimating the
average energy of networks and the cluster head selection algorithm which is
based on initial energy where:

The average energy of rth round is set at...

where R denote the total rounds of the network lifetime

In DEEC,we continue to punish more just advanced nodes based on the
average energy calculated before , so if they spent more energy then they
will die quickly To avoid this unbalanced case, our protocol DDEEC
introduces some changes to equation. These changes are based on using a
threshold residual energy value ThREV , which is equal to:

Therefore, the cluster head election will be balanced and more equitable. So,
the equation (a ) which represents the nodes average probability pi to be a
cluster head will change as follow:
 EDEEC
(Enhanced Distributed Energy Efficient
Clustering) Protocol
• E-DEEC implements the same strategy for estimating the energy in the
network as proposed in DEEC.
• During each round, the node decides whether to become a CH or not based
on the threshold calculated by the suggested percentage of CH and the
number of times the node has been a CH so far.

• If the number is less than threshold T(s), the node becomes a CH for the
current round. A threshold is calculated as:

where p, r, and G represent, respectively, the desired percentage of cluster-

heads, the current round number, and the set of nodes that have not been
cluster-heads in the last 1/p rounds.
In the three-level heterogeneous networks, there are three types of nodes
normal nodes, advanced nodes, and super nodes, based on their initial energy

Hence the reference value of p is different for these types of nodes. The
probabilities of normal, advanced, and super nodes are:
• The threshold for cluster head selection is calculated for normal, advanced,
super nodes

where
𝐺′is the set of normal nodes that have not become cluster heads within the last
1/pi rounds of the epoch where si is a normal node,

𝐺′′is the set of advanced nodes that have not become cluster heads within the
last 1/pi rounds of the epoch where si is an advanced node,

G”’ is the set of super nodes that have not become cluster heads within the last
1/pi rounds of the epoch where si is super node.
Flow chart of DEEC, DDEEC, and EDEEC
 Comparison of Protocols
Type Energy Network CH Probabilit Threshold
Levels Lifetime Mobility y of CH Energy
selection
DEEC Two Good Variable Residual Not present
energy and
Average
energy

DDEEC Two Better Variable Initial Present

energy and
Average
energy

EDEEC Three Best Variable Residual Present

Energy and
Average
energy
 Simulation Parameters
Parameter Value
Network size 500m*500m

Number of Nodes 500,1000

Probability of cluster heads p0 0.1

Initial Energy E0 0.5

Fraction of Energy Advancement of 1

Advance nodes(a)
Packet size 4000 bits

Rounds(r max) 5000,10000

Transmitting Energy cost 50nJ/round
Receiving energy cost 50nJ/round
Other parameters • m=0.5
• m0=0.4
• b=3
 Definitions of Parameters
Network lifetime(No of alive nodes):
The lifetime of a sensor network is defined as the time during which Network
is operational
Throughput:
Throughput is the number of packets successfully transmitted to the
destination /base station per second
Energy consumption:
Total amount of Energy consumed per round
Number of packets to BS:
Total number of packets sent from Cluster Head to base station
 Simulation Graphs

(i) represents the graph of dead nodes for no. of dead nodes per round. Rounds upto 5000.
(ii) represents the graph for alive nodes i.e., no. of alive nodes per round.Rounds upto 5000.
(iii) represents the graph for no. of packets sent to the base station per round. Rounds upto
5000.
(iv) represents the graph for the number of cluster heads formed per round. Rounds upto 5000.
(v) represents the graph for total residual energy per round. Rounds upto 5000.
(vi) represents the graph for throughput per no. of rounds
 (i)represents the comparison of DEEC DDEEC and EDEEC protocols on dead
nodes in 5000 rounds for 500 nodes
(ii). represent the no. of dead and alive nodes after 5000 rounds for 500 nodes.
(iii). represent the total no. of packets sent to the base station after 5000 rounds
 (i)represents the comparison of DEEC DDEEC and EDEEC protocols on dead nodes in
10000 rounds for 500 nodes
(ii) represent the no. of dead and alive nodes after 10000 rounds for 500 nodes.
(iii) represent the total no. of packets sent to the base station after 10000 rounds
Fig (i) represents the graph of dead nodes for no. of dead nodes per round and rounds up to 10000.
Fig(ii) represents the graph for alive nodes i.e., no. of alive nodes per round and rounds up to 10000.
Fig(iii) represents the graph for no. of packets sent to the base station per round and rounds up to 10000.
Fig(iv) represents the graph for the number of cluster heads formed per round and rounds up to 10000.
Fig(v) represents the graph for total residual energy per round and rounds up to 10000.
Fig(vi) represents the graph for throughput per no. of rounds(packets/sec)
Fig (i) represents the graph of dead nodes for no. of dead nodes per round and rounds up to 5000.
Fig(ii) represents the graph for alive nodes i.e., no. of alive nodes per round and rounds up to 5000.
Fig(iii) represents the graph for no. of packets sent to the base station per round and rounds up to 5000.
Fig(iv) represents the graph for the number of cluster heads formed per round and rounds up to 5000.
Fig(v) represents the graph for total residual energy per round and rounds up to 5000.
Fig(vi) represents the graph for throughput per no. of rounds.
 (a) Represents the comparison of DEEC DDEEC and EDEEC protocols on dead nodes in
10000 rounds for 1000 nodes
(b) Represents the no. of dead and alive nodes after 10000 rounds for 1000 nodes.
(c) Represents the total no. of packets sent to the base station after 10000 rounds for 1000
nodes.
Fig(i) represents the graph of dead nodes for no. of dead nodes per round and rounds up to 10000.
Fig(ii) represents the graph for alive nodes i.e., no. of alive nodes per round and rounds up to 10000.
Fig(iii) represents the graph for no. of packets sent to the base station per round and rounds up to 10000.
Fig(iv) represents the graph for the number of cluster heads formed per round and rounds up to 10000.
Fig(v) represents the graph for total residual energy per round and rounds up to 10000.
Fig(vi) represents the graph for throughput per no. of rounds (packet/sec)
 Conclusions
• In this project, EDEEC (Enhanced Distributed Energy Efficient Clustering)
protocol improves the stability and energy-efficient property of the
heterogeneous wireless sensor network.
• In terms of the number of packets transmitted to the BS, Enhanced DEEC
gives a substantial improvement of 70.85 % when compared to DDEEC and
69.6% when compared to DEEC.
• Whereas in terms of first node dead Enhanced DEEC shows an improvement
of 97.2% when compared to DEEC and 91.5% when compared to DDEEC.
• When the last dead node is considered, there is an improvement of 99.2%
when compared with DEEC and 98.1% when compared to DDEEC.
• Simulation results show that EDEEC performs better as compared to DEEC
and DDEEC in a heterogeneous environment for wireless sensor networks.
 Protocol/ DEEC DDEEC EDEEC
Parameters protocol protocol protocol
1st 211th round 219th round 380th round
Dead Nodes
All 3131th round 3147th round 9914th round

Network lifetime 3130 rounds 3146 rounds 9913 rounds

(Nodes are alive up
to…)

Packets sent to base

station after 10000 45920 packets 51840 packets 67890 packets
rounds

Energy Consumption
per round 79.63J 75.87J 49.54J
(Joules)

Throughput 21packets/sec 73packets/sec 78packets/sec

 Limitations of EDEEC
• In EDEEC ,Sensor nodes are uniformly deployed and fixed.

• EDEEC protocol contains 3 types of nodes, Probability for CHs selection

for super and advanced nodes is higher than that of the normal nodes.
EDEEC continues to punish super and advanced nodes even when these
have the same energy level as the normal nodes.

• Nodes which are not in use are still active and energy is wasted
unnecessarily.
 Future Scope

• Nodes are fixed and protocol can be extended to mobility of nodes.

• Only Data transfer is considered, Protocol can be extended to Audio, Video

transfer .

• Heterogeneity can be extended to multilevel.

• It is a single way communication i.e., data is sent from nodes to base

station. Can be extended to two way communication.
 References and Bibliography
1. K. Kaur and E. S. Sharma, "Enhanced Distributed Energy Efficient Clustering Protocol," 2020
International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India,
2020, pp. 1-5, DOI: 10.1109/ICCCI48352.2020.9104091.
2. R. Kumar, “Evaluating the Performance of DEEC Variants,” vol. 97, no. 7, pp. 9–16, 2014.
3. P. Saini and A. K. Sharma, “E-DEEC - Enhanced distributed energy-efficient clustering scheme for
heterogeneous WSN,” 2010 1st Int. Conf. Parallel, Distrib. Grid Comput. PDGC - 2010, pp. 205–
210, 2010.
4. B. Elbhiri, S. Rachid, S. El Fkihi, and D. Aboutajdine, “Developed Distributed Energy-Efficient
Clustering (DDEEC) for heterogeneous wireless sensor networks,” 2010 5th Int. Symp. I/V
Commun. Mob. Networks, ISIVC 2010, pp. 1–4, 2010.
5. Jamal N. Al-Karaki, Ahmed E. Kamal,” Routing Techniques In Wireless Sensor Networks: A
Survey”, IEEE Wireless Communications, Volume: 11, Issue: 6, 26- 28, December 2004.
6. W.Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient communication protocol
for wireless sensor networks," in the Proceeding of the Hawaii International Conference System
Sciences, Hawaii, January 2000.
7. S. Lindsey and C. S. Raghavendra, "PEGASIS: Power Efficient Gathering in Sensor Information
Systems," in the Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2002.
8. O. Younis, S. Fahmy, "HEED: A hybrid, energy efficient, distributed clustering approach for adhoc
sensor networks", IEEE Transactions on Mobile Computing vol 3, no 4, pp 660-669, 2004.
9. G. Smaragdakis, I. Matta, A. Bestavros, “SEP: A Stable Election Protocol for clustered
heterogeneous wireless sensor networks”, in: Second International Workshop on Sensor
Thank you

Submitted By
SAI JOSHITHA ANNAREDDY (17011A0423)
KANNA SANTHI(17011A0428)
VISHAL PANDITA(17011A0444)
BAIRI NAVYA SRI(18015A0403)