Energy Efficient Enhancement of TDEEC Wireless Sensors Network Protocol Based on Passive RFID Implementation

ISSN (Online) : 2278-1021
ISSN (Print) : 2319-5940
International Journal of Advanced Research in Computer and Communication Engineering
Vol. 3, Issue 5, May 2014
Copyright to IJARCCE www.ijarcce.com 6647
Energy Efficient Enhancement of TDEEC
Wireless Sensors Network Protocol Based on
Passive RFID Implementation
Elahmadi Cheikh1
, Chakkor Saad2
, Baghouri Mostafa3
, Hajraoui Abderrahmane4
Department of Physics, Team: Communication and Detection Systems, University of Abdelmalek Essaâdi, Faculty of
Sciences, Tetouan, Morocco1,2,3
Abstract: Radio frequency identification (RFID) and wireless sensor networks are two important wireless technologies
which have a wide variety of applications in current and in future systems. By integration of these technologies, it is
feasible to improve the operating functionalities. In the heterogeneous network, the need to apply the balancing of
energy consumption across all nodes is very important to prevent the death of those nodes and thereafter increase the
lifetime of the network .The most part of the network energy is consumed in the localization and in the communication
stages, when nodes are sending HELLO packet, this energy can be recovered by implementing a passive RFID circuit
in each node. This approach extends the network lifetime and increase the number of packet messages sent to the base
station. Computer simulation in MATLAB with different scenarios comparison shows that the proposed method
presents an efficient solution to enhance the energy network performance.
Keywords: Wireless sensor network, TDEEC, Passive RFID, Energy, routing, clustering.
I. INTRODUCTION
Wireless sensor networks are an emerging technology that
has a wide range of potential applications including
environment monitoring, smart spaces, medical systems
and robotic exploration... Such a network normally
consists of a large number of distributed nodes that
organize themselves into a multi-hop wireless network [1].
However, the sensor nodes are usually powered by
batteries and thus have very limited lifetime if no power
management is performed. The sensor node contains four
basic building blocks of components, those are sensing
unit, processing unit, radio unit, and power unit [2]. These
sensors are able to communicate with each other to
collaboratively detect objects, collect information and
transmit messages. However, as sensors are usually small
in size, they have many physical limitations such as
battery, computational power and memory. The important
part of energy is consumed in the communication circuit
which must be minimized. Because of those limitations,
energy-efficient techniques are main research challenges
in wireless sensor networks. A number of techniques have
been proposed to solve these problems. The major
challenge is the energy consumption, In order to support
data aggregation through efficient network organization;
nodes can be partitioned into a number of small groups
called clusters. Each cluster has a cluster head, and a
number of member nodes [2].In clustering WSNs, the
imbalanced power consumption among nodes is the main
factor modifying the WSN lifetime. Wireless sensor nodes
can be divided into various types based on the various
capabilities in sensing, energy and communication. The
heterogeneity is not unusual in the WSNs [3]. DEEC
(Design of a distributed energy-efficient clustering
algorithm) [4], is used in heterogeneous wireless sensor
networks. This protocol is based on the election of cluster
head by the balance of the probabilities of the remaining
energy for each node, it use the average energy of the
network as the reference energy, the cluster-heads are
elected by a probability based on the ratio between the
residual energy of each node and the average energy of the
network. Threshold Distributed Energy Efficient
Clustering protocol [18] propose an energy efficient
cluster head scheme, for heterogeneous wireless sensor
networks, by modifying the threshold value of a node
based on which it decides to be a cluster head or note.
Locating the heterogeneous nodes in WSN and clarifying
cluster members by HELLO packet are unfavorable ways
to affect network energy consumption and stability of
WSN. RFID (radio frequency identification) [20] is a
means of storing and retrieving data through
electromagnetic transmission using a radio frequency
(RF)-compatible integrated circuit An RFID system
usually consists of two main components: tags and
readers. A tag has a unique identification number (ID) and
memory that stores additional data such as manufacturer
name, product type, and environmental factors including
temperature, humidity, and so on. The reader can read
and/or write data to tags through wireless transmissions.
By integrating RFID technology with WSN [21], we can
route RFID data from readers to base stations by using
existing WSN clustering protocols. The integration of
RFID and sensor networks can increase their utilities to
other scientific and engineering fields by exploiting the
advantages of both technologies. There are several ways of
integrating RFID with WSN [20,21]. In an RFID enhanced
WSN, it is acceptable that we consider the energy of all

ISSN (Print) : 2319-5940
nodes are not equal and these networks are heterogeneous
[5]. In our proposed method, for increasing the lifetime
period and enhance the network performance , we present
a cluster-based protocol for heterogeneous RFID
enhanced based on exploiting the advantages offered by
the RFID technology to recover the energy lost during the
localization HELLO packet transmission. The remains of
this paper are organized as follows. In section 2 we
defined Integration of RFID and Wireless Sensor
Networks. In Section 3, we described the related work. In
Section 4, we defined the heterogeneous model and
performance measures for WSN. In section 5 we show the
performance of our approach by simulations and compare
it with DEEC. Section 6 contains our concluding remarks.
II. INTEGRATION OF RFID AND WIRELESS SENSOR
NETWORKS
Radio frequency identification and wireless sensor
networks are two important wireless technologies that
have a wide variety of applications in current and future
systems. RFID facilitates detection and identification of
objects that are not easily detectable or distinguishable by
using conventional sensor technologies. However, it does
not provide information about the condition of the objects
it detects. WSN, on the other hand, not only provides
information about the condition of the objects and
environment but also enables multi-hop wireless
communications. Hence, the integration of these
technologies expands their overall functionality and
capacity [6]. By joining RFID tags to WSN nodes, it is
possible to remotely „wake up‟ the processor and other
parts on demand. The RFID prods technique can use either
a passive or an active RFID tag [7]. In integrating RFID
readers with a WSN node scenario, integrated RFID
readers-sensor nodes are assumed [8]. Zhang et al. [5]
present possible architectures for integrating RFID and
WSN, and provide a detailed list of real world
applications.
To ensure the heterogeneity of our network by considering
three types of integrations : integrating tags with sensors,
integrating tags with wireless sensor nodes, integrating
readers with wireless sensor nodes. Hence, we can
distinguish among the following node types:
 nodes integrated with a passive RFID Tag(too
weak).
 nodes integrated with semi passive RFID
Tag(weak nodes).
 nodes integrated with active RFID Tag(strong
nodes).
 RFID reader enhanced WSN node for
interrogation and reading tags data (very strong node).
Because an RFID radio uses much less energy than an
RF sensor radio for simplicity, we do not consider RFID
tags and readers specifications in our network, and specific
to the needs of our model we considered a WSN with
energy heterogeneity.
III.RELATED WORK
There exist two types of distributed clustering techniques
used to reduce energy consumption: the homogeneous and
heterogeneous clustering algorithms. It is very difficult to
design heterogeneous clustering schemes due to their
complexity on the contrary of homogeneous protocols.
Currently, WSN are more possibly heterogeneous
networks than homogeneous ones. Actually, clustered
routing protocol has gained increasing attention from
researchers because of its potential of extending WSN
lifetime. Heizelman [10]designed and implemented the
first distributed and clustered routing protocol with low
energy consumption [9]. LEACH [11], performs well, but
its performance become badly in the heterogeneous
network as shown by [9], [14]. PEGASIS [12] it is an
improved version of LEACH as nodes will be organized to
form a chain, which can be computed by each node or by
the base station. However, excessive delay is introduced
for distant nodes, especially for large networks. SEP
performs poorly in multi-level heterogeneous networks
and when heterogeneity is a result of operation of the
sensor network [9], [4]. In HEED [13] a stochastic
algorithm used to define the cluster-heads based on
probability election of each node which is correlative to
the residual energy. Q. Li, Z. Qingxin and W. Mingwen
are proposed Distributed Energy Efficient Clustering
Protocol (DEEC) [4]. This clustering protocol is based on
multi level and two level energy heterogeneous schemes.
The cluster heads are selected using the probability
utilizing the ratio between residual energy of each node
and the average energy of the network. The epochs of
being cluster-heads for nodes are different according to
their initial and residual energy. A particular algorithm is
used to estimate the network lifetime, thus avoiding the
need of assistance by routing protocol [4]. Parul Saini,
Ajay K Sharma have proposed an energy efficient cluster
head scheme, for heterogeneous wireless sensor networks,
by modifying the threshold value of a node based on
which it decides to be a cluster head or not, called TDEEC
(Threshold Distributed Energy Efficient Clustering)
protocol [18]. Since the network nodes are deployed
randomly in a monitoring zone, the aim problem of
clustering algorithms that it aren‟t takes into account the
localization energy consumption to calculate the total
network energy consumption. When multiple cluster heads
are randomly selected within a large number of nodes into
expanded area, a wide additional loss of energy occurs
because the localization energy is approximately
proportional to the number of HELLO packet message
size and the distance between these nodes. In the face of
this scenario, it is necessary to recover the lost energy
ratio by using the RFID technology.
IV.HETEROGENEOUS MODEL AND PERFORMANCE
MEASURES FOR WSN
A. Performance measures
We describe the indicators that apply here to assess the
performance of protocols that are defined in Smaragdakis
et al.[15] and used in our paper. Network stability: first
dead , all-dead ,number of packets messages in each round
finally number of alive nodes received per round.
B. Radio Energy Dissipation Model

ISSN (Print) : 2319-5940
Radio Energy Model used is based on [10, 19]. Energy
model for the radio hardware energy dissipation where the
transmitter dissipates energy to run the radio electronics
and the power amplifier, and the receiver dissipates energy
to run the radio electronics is shown in Figure 1 [10,19].
Figure 1: Radio Energy Dissipation Model
In this model, both the free space (d2
power loss) and
the multipath fading (d4
power loss) channel models
were used, depending on the distance between the
transmitter and receiver [10,19].Power control can
be used to invert this loss by appropriately setting
the power amplifier if the distance is less than a threshold
do, the free space model is used otherwise, the multipath
model is used. Thus, to transmit an L-bit message a
distance, the radio expands.
The electronics energy, Eelec , depends on factors such as
the digital coding, modulation, filtering, and spreading of
the signal defined at [9] ,whereas the amplifier energy,
Efs.d2
or Eamp.d4
, depends on the distance to the receiver
and the acceptable bit-error rate.
C. Contribution and Proposed Method:
In our proposition, we note that energy dissipated for
localization can be covered by integration of RFID passive
tags model datasheet microID® 125 kHz [21].so as we
know the hello packet size is L=512 bytes the localization
energy becomes:
2
(2*8*512) (2*8*512) 0
(512, )
4
(2*8*512) (2*8*512) 0
E d d delec fs
E dTX
E d d dampelec


 

 



(2)
We based our work on [16] using the energy model
consumption sending and receiving one byte of data from
node i to node j over a distance d meters, and we consider
that the energy consumption costs:
by calculating the distance d we place the node destination
j on the circle where the node i represent his centre. The
angle of arrival serves for locating the node j on the
segment S and reducing the probability of positioning the
node on all area of the circle. Considering the segment S
proximately similar to line, and using the Euclidian
distance d between node I and j at the instant t we denote
[17]:
The techniques (angle of directional antenna and the
energy of transmission), can sufficiently locate node with
reduced probability.
D. Network model
In our model, we assume that there are N sensor nodes,
which are evenly scattered within a MM square region
and organized into clusters hierarchy for aggregate data by
cluster heads to base station which is located at the center
of this region. Nodes have low mobility or stationary as
assumed at [4], [10].In fact we will use the same
conception as mentioned at [4],noted by m fraction of
stronger nodes with a times more energy than the others
which have an initial energyE0. In two and multi-level
heterogeneous networks, the clustering algorithm should
consider the discrepancy of initial energy, Etotal is
expressed by:
   10 0
1 1
N N
E E a E N ai itotal i i
    
  (7)
E. TDEEC Cluster-head selection algorithm
In TDEEC protocol, we choose different parameters based
on the residual energy of Ei(r) node s at round r. If nodes
have different amounts of energy, pi of the nodes with
more energy should be larger than popt Let Ē(r) denotes the
2
0
( , )
4
0
LE L d d delec fs
E L dTX
LE L d d dampelec


 

 



(1)
2 2
( ) ( )ij i j i jd x x y y   
(5)
2 2
2. ( ).sin( )
2
2. ( ) ( ) .sin( )
2
ij
i j i j
S d t
S x x y y



    (6)
Figure 2: Representation of the localization method
2
1 2
1
s
ij ij
r
ji
e c c d
e c
  

 (3)
1
2
s
ij
ij
e c
d
c

 (4)

ISSN (Print) : 2319-5940
average energy at round r of the network, which can be
obtained by:
 
1
(1 )
r
E r EtotalN R
 
(8)
where R denotes the total rounds of the network lifetime,
R can be calculated as:
The total energy dissipated in the network during a round
Eround is equal to:
Where k is the number of clusters, EDA is the data
aggregation cost expended in the cluster-heads. When the
networks are heterogeneous, the reference value of each
node should be different according to the initial energy. In
the model of multi-level heterogeneous networks, the
weighted probability shown as:
 
   
   
1
1
p N a E ropt i i
p s if s Gi iN
N a E ri
i

 


(12)
Threshold for cluster head selection is calculated based on
ratio of residual energy and average energy of that round
in respect to the optimum number of cluster heads by:
 
 
( )
*
1
( ) 1 ( )( mod )
( )
0
p s E ri i
k if s Gopt i
T s p s r E ri
p si

 





(13
)
where p(si), 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(si) rounds. Using this threshold, each node will be a
cluster head, just once at some point within 1/ p(si) rounds.
V. SIMULATION RESULTS
The proposed approach has been implemented in
MATLAB and the performance has been evaluated by
simulation, the lifetime of the network is measured in
terms of rounds when the first sensor node dies. The base
station is assumed in the center of the sensing region. All
the parameters values including the first order radio model
characteristic are mentioned in the table1 below. To
compare the performance of the proposed approach with
TDEEC protocol, the effect caused by signal collision and
interference in the wireless channel is ignored, a multi-
level heterogeneous network is considered.
Table
1 : Simulation Parameters
The effect of varying α on the lifetime of the network and
on the number of packet messages received in the base
station is studied in different scenarios as shown in
table2,where m and m0 are the fraction of the advanced
and super nodes, which own a and b times more energy
than the normal ones [9]:
Thus, each node in the sensor network is randomly
assigned different energy levels between a closed set
[E0,E0(1+amax)]. In the simulation results figures 3and 5
the lifetime evolution of the network for each scenario,
whereas figures 4and 6 shows the number of packet
messages received in the base station per round for each
scenario. The tables 3and 4 provides statistics on the
number of dead nodes per rounds as well as the percentage
increase in the lifetime of the network for the proposed
approach compared to TDEEC protocol. It is very clear
that the proposed approach gives a lifetime network
greater than TDEEC protocol whether for the first dead
node rounds or for all dead nodes rounds due to their
remaining energy. In TDEEC protocol all nodes die early
on the contrary of the proposed approach in which all
nodes die tardily for all studied scenarios. On the other
hand, the energy efficiency of the proposed approach
improves significantly the number of packets message
Etotal
R
Eround
 (9)
4 2
(2 )E L NE NE k d N d
round elec DA mp toBS fs toCH
      (10)
, 0.765
22
M M
d dtoBS toCH
k
 
(11)
Parameter Value
Network area 100 m100 m
Number of nodes 100
E0 0.5 J
Eelec 1.3nJ/bit
εfs 10 pJ/bits/m2
εemp 0.0013 pJ/bit/m4
ETx=ERx 50 nJ/bit
EDA 50 nJ/bit/message
0
fs
mp
d


 70 m
Packet Size 4096 bits
Popt 0.05
Parameters m m0 α b
Scenario1 0.6 0.4 2 3
Scenario2 0.6 0.4 4 3
Table 2 : Simulation scenarios
*2
2
M N fs
kopt
d amptoBS

 
 (14)

ISSN (Print) : 2319-5940
received which increases with a remarkable manner by
increasing α.
Figure 3: Number of alive nodes over time (Scenario1)
Figure 6: Number of packet messages received per round
(Scenario2)
TDEEC Proposed INCREASE
First dead 1114 1274 14,36 %
All-dead 6152 6174 0.3%
Table 3: Number of dead nodes per rounds (Scenario1)
TDEEC Proposed INCREASE
First dead 1020 1213 18,92%
All-dead 9645 10432 8,15%
Table 4: Number of dead nodes per rounds (Scenario2)
Figure 7: Evolving of lifetime nodes percentage according to different
scenarios
Referred to figure 7, the lifetime percentage decreases
with increasing α for the first dead node time while this
percentage fluctuate between different values and do not
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
Scenario1
(α=2)
Scenario2
(α=4)
Pourcentage
scenarios
First dead
All-dead
Figure 4: Number of packet messages received per round
(Scenario1)
Figure 5: Number of alive nodes over time (Scenario2)

ISSN (Print) : 2319-5940
keep a monotony for the all dead nodes time when α
increase, this is justified by the network instability in this
time period
α=2 α=2.5 α=3 α=4 α=5 α=6
Packet
number
*105
(TDEEC)
2.005 2.488 2.882 4.054 4.725 4.674
Packet
Number *
105
(Proposed)
2.048 2.562 2.966 4.406 5.149 5.464
Packet
number
increasing
4300 7400 8400 35200 42400 79000
Table 5: numerical results for different values of α
It is clear in figures 8 and 9, that the proposed approach
preserves the improvement of the network lifetime
whether for the first dead node rounds or for all dead
nodes rounds compared to TDEEC protocol despite the
increase of multi-level heterogeneity value which takes its
value from 2 to6. Therefore, the number of packets
message received at round=10000 increases also.
Figure 8: Evolving of lifetime nodes percentage according to
different scenarios
Figure 9: packet message evolution
VI. CONCLUSION
By integrating RFID technology with WSN, it can lead to
a heterogeneous network. In such network the major goal
of routing clustering protocol is to improve energy
consumption and increasing lifetime. In our article we
suggest and introduce a cluster-based protocol for
heterogeneous RFID enhanced WSNs that can recover the
Energy consumed during the localization. The simulation
results show that our solution significantly improves the
stability period, and consumes energy in a more efficient
way in the WSNs in comparison with existing clustering
protocol TDEEC at the same time that can increase the
packet message number received during the simulation
time.
REFERENCES
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0
100000
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a=2 a=2.5 a=3 a=4 a=5 a=6
packet number TDEEC
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2 2.5 3 4 5 6
Packetnumberincreasing
variation of α

ISSN (Print) : 2319-5940
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last accessed March 2014)
BIOGRAPHIES
Elahmadi Cheikh was born in
Boujdour Morocco. He‟s a member in the
Physics department, Team
Communication and detection Systems,
Faculty of sciences, University of
Abdelmalek Essaâdi, Tetouan Morocco,
and his research area is: improving
performance of sensor networks. He obtained the Master's
degree in Networks and Systems from the Faculty of
Sciences and Techniques of Settat, Morocco in 2009. His
current research interests are in the areas of embedded
systems , wireless sensor networks, energy efficiency,
body sensor networks, and RFID technology.
Chakkor Saad was born in Tangier
Morocco. He‟s a member in the Physics
department, Team Communication and
detection Systems, Faculty of sciences,
University of Abdelmalek Essaâdi,
Tetouan Morocco, and his research area
is: intelligent sensors and theirs
applications. He obtained the Master's degree in Electrical
and Computer Engineering from the Faculty of Sciences
and Techniques of Tangier, Morocco in 2002. He
graduated enabling teaching computer science for
secondary qualifying school in 2003. In 2006, he
graduated from DESA in Automatics and information
processing at the same faculty. He works as teacher of
computer science in the high school.
Baghouri Mostafa is an PhD student in
the Laboratory of Systems Modeling and
Analysis, Team: Communication
Systems, Faculty of sciences, University
of Abdelmalek Essaâdi, Tetouan
Morocco, his research area is:
Optimization of energy in the wireless
sensors networks. He obtained a Master's degree in
Electrical and Computer Engineering from the Faculty of
Science and Technology of Tangier in Morocco in 2002.
He graduated enabling teaching computer science for
secondary qualifying school in 2004. In 2006, he
graduated from DESA in Automatics and information
processing at the same faculty. He work teacher of
computer science in the high school.

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