Fault tolerant routing scheme based on

International Journal of Computer Networks & Communications (IJCNC) Vol.7, No.3, May 2015
DOI : 10.5121/ijcnc.2015.7308 105
FAULT-TOLERANT ROUTING SCHEME BASED ON
LEACH FOR WIRELESS SENSOR NETWORKS
Chifaa TABET HELLEL1
, Mohamed LEHSAINI1
, and Hervé GUYENNET2
1
STIC Laboratory, Faculty of Technology, Tlemcen University, Algeria
2
FEMTO-ST/DISC UFR ST, University of Franche-Comte, France
ABSTRACT
Most routing protocols designed for wireless sensor networks used the unit disk graph model (UGD) to
represent the physical layer. This model does not take into account fluctuations of the radio signal.
Therefore, these protocols must be improved to be adapted to a non-ideal environment. In this paper, we
used the lognormal shadowing (LNS) model to represent a non-ideal environment. In this model, the
probability of successful reception is calculated according to the link quality. We evaluated LEACH’s
performance with LNS model to illustrate the effects of radio signal. Unfortunately, our findings showed
that the fluctuations of signal radio have a significant impact on protocol performance. Thereby, we
proposed a Fault-Tolerant LEACH-based Routing scheme (FTLR scheme) to improve the performance of
LEACH in a non-ideal environment. Simulation results proved that our contribution provides good
performance over the ideal model in terms packet loss rate and energy consumption.
KEYWORDS
FTLR scheme, LEACH, Lognormal Shadowing Model, Model Unit Disk Graph Model, WSN.
1.INTRODUCTION
Wireless sensor network (WSN) is a set of devices called “sensor nodes” distributed over an area
to monitor the surrounded environment. Sensor nodes have capabilities of computing, sending
and receiving sensed data. Recently, WSNs have attained an appreciable attention that many
researchers have devoted a lot of studies to improve its interests in many domains like
environmental monitoring, industrial control, transportation, and healthcare, and in these
applications, the reliability of the network is required for collecting data without loss from nodes.
Therefore, prolonging the network lifetime is an important and challenging issue, which is also
the focus of designing the routing protocols for WSNs [1].
Routing process is a fundamental operation in WSNs. It consists in transmitting a message from a
source node to a remote base station according to the main routing schemes: hierarchical,
location-based, data-centric and QoS-aware [2]. Furthermore, most routing protocols derived
from these schemes rely on a physical layer based on an ideal model represented by the Unit Disk
Graph model (UDG) [3]. However, this model although commonly used cannot be considered as
a realistic model since it assumes that the messages are always received without any error if the
distance between the transmitter and the receiver is less than or equal to the transmission range
[4]. This assumption does not take into account the random fluctuations of the radio signal, which
may have a significant impact on the transmissions. Therefore, it is interesting to study the
behaviour of these routing protocols in a realistic environment to illustrate the impact of radio

106
fluctuations on the performance of these protocols. Among all these solutions, we have chosen to
focus on LEACH protocol [5] for several reasons: it provides good results using an ideal physical
layer and it is the most popular routing protocol designed for WSNs.
In this paper, we used the LogNormal Shadowing model (LNS) [6] for a non-ideal environment
and analyze the performance of LEACH protocol with this model. The used model takes into
account radio signal fluctuations, and therefore could be more realistic than the commonly used
static UDG model. LNS model computes the probability of successful reception between
communicating nodes according to the distance separating them. Then, we accordingly propose a
Fault-Tolerant LEACH-based Routing scheme (FTLR) to adapt the original version of LEACH to
a non-ideal environment. In FTLR scheme, we assume that if the probability of reception without
error is lower than a certain threshold, the message will be dropped. In this case to avoid this
anomaly, we proposed a multi-hop routing scheme for intra-cluster communications so that the
member node could use a relay node to reach its cluster-head.
The remainder of this paper is organized as follows: Section 2 provides some necessary
preliminaries for describing the model used for the realistic physical layer. In section 3, we
review related work. Section 4 evaluates LEACH with the lognormal shadowing model and
proposes an improved version of LEACH for realistic environments. In Section 5, we present
simulation results and compare them with the original version of LEACH over an ideal
environment. Finally, Section 6 concludes the paper with a summary and future work related to
this topic.
2.BACKGROUND
Before presenting our contribution, we will give some definitions and notations that facilitate the
understanding of what follows.
2.1.Notations and assumptions
WSN can be represented as a graph G=(V,E) with a set of vertices (V) consisting of the nodes of
the network and a set of edges (E ⊆ V2
) consisting of the links between the nodes. An edge e =
(u,v) belongs to E if and only if the node u is physically able to transmit messages to v and vice
versa. Each node (u∈V) is assigned by an unique value to be used as an identifier Id(u). The set
of neighbors of a node u is represented by N1(u) and the size of this set is known as the degree of
u, denoted by δ(u) as presented in equation (1).
ܰଵሺ‫ݑ‬ሻ = ሼ‫ݒ‬ ∈ ܸ: ሺ‫ݒ‬ ≠ ‫ݑ‬ሻ ∧ ሺ‫,ݑ‬ ‫ݒ‬ሻ ∈ ‫ܧ‬ሽ (1)
ߜሺ‫ݑ‬ሻ = |ܰଵሺ‫ݑ‬ሻ|
We consider the following assumptions:
- Each node has an omni-directional antenna thereby a single transmission of a node can be
received by all nodes within its vicinity.
- The nodes are almost static in a reasonable period of time.
- A node is considered as neighbor of another node if the probability of receiving messages
from each other is greater than a defined threshold p0.
- A message can be received without any error, if the distance separating the communicating
nodes is less than or equal to p0.

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2.1. Radio model
We primarily present the unit disk graph model. Let us assume a graph G = (V, E), where all
nodes have the same transmission range denoted by Rc. The UDG model defines the set E of the
edges as follows:
‫ܧ‬ = ሼሺ‫,ݑ‬ ‫ݒ‬ሻ ∈ ܸଶ
: ሺ‫ݑ‬ ≠ ‫ݒ‬ሻ ∧ ݀݅‫ݐݏ‬ሺ‫,ݑ‬ ‫ݒ‬ሻ ≤ ܴ௖ሽ (2)
where dist(u,v) is the Euclidean distance between u and v. This model although commonly used
cannot be considered as a realistic model since it assumes that the messages are always received
without errors if the distance between the communicating nodes is lower than or equal to the
transmission radius Rc [4]. This assumption does not take into account the random fluctuations of
the radio signal, which could have a significant impact on the quality of transmission.
Thereby, it is interesting to evaluate the performance of these routing protocols in a realistic
environment to illustrate their robustness in this kind of environments. For that, we have involved
the link quality factor in determining the probability of successful reception between nodes in
order to know if the message is received or it is corrupted. Since this probability implied several
factors such as signal strength, the distance separating the communicating nodes, and the presence
of obstacles, etc…, it may be difficult to obtain an accurate evaluation of these factors which are
themselves prone to errors. Therefore, we assume that signal strength gradually decreases
according to the distance; thereby the probability of reception without errors can be calculated
according to the distance separating two nodes. Thus, we proposed using the LNS model
described in [6,7] to evaluate this probability between nodes as presented in equation (3).
‫ܨ‬ሺ‫ݔ‬ሻ =
‫ە‬
ۖ
‫۔‬
ۖ
‫ۓ‬ 1 −
ቀ
ೣ
ೃ೎
ቁ
మഀ
ଶ
, ݂݅ 0 < ‫ݔ‬ ≤ ܴ௖
ቀ
మೃ೎షೣ
ೃೣ
ቁ
మഀ
ଶ
, ݂݅ ܴ௖ < ‫ݔ‬ ≤ 2ܴ௖
0 ‫ݐ݋‬ℎ݁‫݁ݏ݅ݓݎ‬
(3)
where α represents the attenuation factor which depends on the environment and x is the
considered distance separating the transmitter node from the receiver node. In equation (3), we
assume that the probability of successful reception is 0.5 when the distance between the
communicating nodes is equal to Rc. Figure 1 illustrates the evolution of the probability of
reception without errors depending on the distance between the communicating nodes with Rc=10
and α=2.

Figure 1: Probability of successful reception in UDG and LNS models
3. RELATED WORK
Routing in wireless sensor networks has previously been studied in several papers such as
[9]. Moreover, other protocols use
sending data such as that described in
works have been performed with an ideal simulation environment.
As mentioned above, in this paper we have proposed usi
performance of LEACH protocol in a realistic scenario. The considered model takes into account
the variation of the radio signal strength caused by obstructions and irregularities in the
surroundings of the transmitting and
realistic than the UDG model.
In this section, we review some related works which have been carried out to alleviate routing in
WSNs with non-ideal environment. In [11], the authors have developed an
tolerant algorithm called DFCR (Distributed Fault
DFCR, the base station (BS) broadcasts a “HELLO” message, and depending on the RSSI
(Received Signal Strength Indication)
from the base station. Then, each CH broadcasts a hop
reach the base station. Moreover, during cluster formation process, each sensor node selects its
CH based on the cost function involving the residual energy of the CH, the distance between the
node and CH, and the distance from CH to BS. If the sensor node has not a CH within it
communication range due to random deployment or sudden failure of the corresponding CH, it
broadcasts a “HELP” message to join the CH via a helping sensor node with multi
communication. Furthermore, CH selects the next hop to reach BS according to the distance
between the base station and the number of hops calculated during the setup phase.
In [12], the authors proposed two algorithms to find the optimal routing path despite even in the
case of loss of links. The proposed algorithms aim to reduce energy consumption and ensure
reliable data transfer to the base station. Sending data from a source
done using a multi-hop communication scheme in a generic model in which the probability of
: Probability of successful reception in UDG and LNS models
Routing in wireless sensor networks has previously been studied in several papers such as
protocols use a multi-path routing scheme to avoid failed nodes
described in [10]. However, most of the protocols presented in these
works have been performed with an ideal simulation environment.
As mentioned above, in this paper we have proposed using the LNS model to evaluate the
surroundings of the transmitting and receiving antennas [4]. Therefore, this model is more
ideal environment. In [11], the authors have developed an energy-efficient fault
tolerant algorithm called DFCR (Distributed Fault-tolerant Clustering and Routing) for WSN. In
Received Signal Strength Indication) of the message received, each CH calculates the distance
from the base station. Then, each CH broadcasts a hop-packet indicating the number of hops to
ction involving the residual energy of the CH, the distance between the
asts a “HELP” message to join the CH via a helping sensor node with multi
between the base station and the number of hops calculated during the setup phase.
12], the authors proposed two algorithms to find the optimal routing path despite even in the
reliable data transfer to the base station. Sending data from a source node to the base station is
hop communication scheme in a generic model in which the probability of
108
Routing in wireless sensor networks has previously been studied in several papers such as [1] [8]
failed nodes when
[10]. However, most of the protocols presented in these
ng the LNS model to evaluate the
receiving antennas [4]. Therefore, this model is more
efficient fault-
tolerant Clustering and Routing) for WSN. In
eceived, each CH calculates the distance
packet indicating the number of hops to
ction involving the residual energy of the CH, the distance between the
asts a “HELP” message to join the CH via a helping sensor node with multi-hop
12], the authors proposed two algorithms to find the optimal routing path despite even in the
node to the base station is
hop communication scheme in a generic model in which the probability of

109
reception of data without errors is calculated according to the distance separating communicating
nodes. The first algorithm establishes a path to the base station with the minimum energy
consumption while guaranteeing reliable routing data. The second algorithm aims to balance
energy consumption among the nodes and minimize energy consumption when a node fails.
In [13], the authors proposed a novel hierarchical routing protocol which addresses fault-tolerance
through a multi-path topology and energy preservation through a cluster-based routing protocol.
In this algorithm three kinds of nodes are used: cluster member, cluster-head, overlapping area-
head (OAH). The principle of this algorithm is almost similar to that of LEACH protocol and the
only difference is that a cluster member may belong to more than one cluster. Moreover, a CH
may have several OAHs associated with it and a cluster should have a common OAH with its
neighboring clusters. In data transmission phase, the CH aggregates data received from its
members and sends it to each OAH within its cluster, and each OAH sends received data to its
associated CH. This process of transfer data continues until reaching the remote base station.
In [14], an improved version of LEACH is proposed, in which each cluster member is within
transmission range of two neighboring cluster-heads. Therefore, if a CH fails the member node
joins the other CH. This anomaly is detected by the base station such as if no response is received
from a CH, the latter is considered as faulty CH.
In [15], another algorithm has been proposed to detect the failure of CH in a short time after the
beginning of each round. It is assumed that the CH transmits a “HELLO” message to all its
members, and if no transmission is received, the CH is considered as failed node. The election of
the new CH is based on the position of nodes in the cluster. In [16], the authors improved the
previous version. They added another phase to the original LEACH phases called detection phase
in which the failure is detected and all members should be advised about it. Therefore, to recover
this failure, a faulty recovery phase is launched by the base station that selects the new CH among
the cluster members based on their residual energy.
In [8], an enhanced version of LEACH is proposed in which each member cluster is covered by
two cluster-heads: the main CH and its vice that takes its role when the main CH fails. The
selection of CH is based on three criteria: minimum distance, maximum residual energy and
minimum energy. Each non-CH chooses its CH based on RSSI such as, greater RSSI means
shorter distance and thereby energy consumed for transmission is not enough.
In [17], the authors proposed an enhanced version of LEACH wherein each cluster contains a CH
which is responsible for gathering data and sending it to the remote base station. These tasks
could quickly deplete its energy; thereby a vice-CH will be involved to take its role to tolerate the
failure of CH.
In [18], the authors proposed two fault-tolerant versions of LEACH. In the first version called
FT1-LEACH, there is two cluster-heads in each cluster: ; primary CH (CHp) and secondary CH
(CHs). The cluster members send their collected data to CHp and CHs. Moreover, when CHp is
alive, it is considered as responsible for aggregating data and transmitting it to the base station,
and if CHp fails, its vice (CHs) would send collected data to the base station. In the second version
called FT2-LEACH, the authors used the checkpoint technique in which the base station should
store all availability information about cluster-heads and their members. If at any time, the base
station does not receive any supervisory message from CH, it considers it as failed CH, and elects
a new CH among their members.

4.CONTRIBUTION
As mentioned above, several studies have been proposed to make LEACH fault
but these studies do not take into account the link quality during data delivery.
of these contributions involve more than one cluster
such as in [10,17,18] but these contributions could not guarantee this goal with a realistic
environment. In this context, we evaluated LEACH
such environments. Then, we pr
quality in the selection of relay nodes in o
consequently make LEACH adaptable to a realistic environment.
Before presenting our contribution, we evalua
with the LNS model to point out its weaknesses in terms of the number of
of lost packets and energy consumption.
4.1. Evaluation of LEACH with L
In this section, we evaluated the performance of the original version of LEACH in
environment in terms of the number of
TOSSIM simulator [19] to evaluate
results to illustrate the weaknesses of the original version of LEACH in a non
We used the LNS model to represent a non
quality to compute the probability of successful recep
probability (p=0.6, 0.7, 0.8) for various network sizes: 20, 40, 60, 80 and 100 nodes.
Figure
As mentioned above, several studies have been proposed to make LEACH fault-tolerant protocol
but these studies do not take into account the link quality during data delivery. Furthermore
of these contributions involve more than one cluster-head in a cluster to ensure reliable delivery
t these contributions could not guarantee this goal with a realistic
environment. In this context, we evaluated LEACH with LNS model to illustrate its limitations in
such environments. Then, we proposed an improved version of LEACH that involves the link
quality in the selection of relay nodes in order to guarantee reliable data
consequently make LEACH adaptable to a realistic environment.
Before presenting our contribution, we evaluated at first the performance of LEACH protocol
with the LNS model to point out its weaknesses in terms of the number of lost packets,
energy consumption.
Evaluation of LEACH with LNS model
In this section, we evaluated the performance of the original version of LEACH in
in terms of the number of lost packets and the ratio of lost packets. We used
] to evaluate the performance of LEACH and we analyzed the
to illustrate the weaknesses of the original version of LEACH in a non-ideal environment.
We used the LNS model to represent a non-ideal environment. This model implies the link
quality to compute the probability of successful reception. Furthermore, we have varied this
probability (p=0.6, 0.7, 0.8) for various network sizes: 20, 40, 60, 80 and 100 nodes.
Figure 2: Number of lost packets vs. Network size
110
tolerant protocol
Furthermore, most
cluster to ensure reliable delivery
t these contributions could not guarantee this goal with a realistic
LNS model to illustrate its limitations in
oposed an improved version of LEACH that involves the link
rder to guarantee reliable data delivery and
ted at first the performance of LEACH protocol
packets, the ratio
In this section, we evaluated the performance of the original version of LEACH in a non-ideal
packets. We used
ed the obtained
ideal environment.
ideal environment. This model implies the link
, we have varied this

Figure
Figure 2 shows the variation of the number of packets lost according to the network size for
various values of probability of successful
increases when this probability increases. These obtained results
LEACH degrades in a realistic environment thereby the original version of this protocol is not
adaptable for non-ideal environment
Figure 3 illustrates the ratio of packets lost according to the network size with various val
probability. We notice that the ratio of packets corrupted increases when the probability of
successful reception increases. For example, in a network that contains 100 nodes with the
probability of successful reception is 0.8, the ratio of lost pac
packets thereby it is necessary to take into account link quality.
4.2. Proposed routing scheme (FTLR scheme)
According to the performance of
necessary to improve LEACH, so that it would be adapted to a realistic environment. Hence, we
have proposed a multi-hop routing scheme called FTLR
scheme to overcome packet loss
CH.
Our contribution takes into account the link quality to
member node would transmit data to its corresponding CH, it computes the probability of
successful reception. If this probability is high
will be received without errors;
ensure reliable delivery and in the same time minimize
routing scheme consumes less energy than
Figure 3: Ratio of lost packets vs. Network size
successful reception. We observe that the number of lost packets
increases when this probability increases. These obtained results mean that the performance of
degrades in a realistic environment thereby the original version of this protocol is not
ideal environment.
Figure 3 illustrates the ratio of packets lost according to the network size with various val
probability of successful reception is 0.8, the ratio of lost packets becomes half of all transmitted
packets thereby it is necessary to take into account link quality.
Proposed routing scheme (FTLR scheme)
the performance of LEACH in a non-ideal environment, it is shown that is
, so that it would be adapted to a realistic environment. Hence, we
hop routing scheme called FTLR scheme instead of a single
e packet loss when a member node could not communicate correctly with its
Our contribution takes into account the link quality to avoid unreliable links, hence
f this probability is higher than a predefined threshold Thresh
errors; otherwise a multi-hop routing scheme will be incorporated to
and in the same time minimize energy consumption since a
e consumes less energy than single-hop routing scheme [20].
111
reception. We observe that the number of lost packets
the performance of
degrades in a realistic environment thereby the original version of this protocol is not
Figure 3 illustrates the ratio of packets lost according to the network size with various values of
kets becomes half of all transmitted
, it is shown that is
, so that it would be adapted to a realistic environment. Hence, we
instead of a single-hop routing
when a member node could not communicate correctly with its
links, hence when a
Thresh1, the packet
scheme will be incorporated to
since a multi-hop

112
We assumed that if a cluster member has a reliable communication link with its respective CH, it
is considered as a perfect node, and a list of this kind of nodes is created. Moreover, when the
probability of reception without errors is less than the predefined threshold, the cluster member
selects the optimal next-hop node among its neighbors to reach its CH. This selection is
performed according to the following algorithm:
Algorithm: Next-hop selection
- Mi: Identifier of a cluster member i
- CHi: Identifier of a clusterhead i
- ቀ௫೘
௬೘
ቁ: coordinates of Mi
- ቀ௫೎
௬೎
ቁ: coordinates of CHi
Begin
- Mi computes the Euclidean distance to its CHi
݀ = ඥሺ‫ݔ‬௠ − ‫ݔ‬௖ሻଶ + ሺ‫ݕ‬௠ − ‫ݕ‬௖ሻଶమ
- Mi computes the probability of successful reception
-
ܲ‫ݎ‬ሺ݀ሻ = 1 −
ቀ
݀
ܴ௖
ቁ
ଶఈ
2
if ሺܲ‫ݎ‬ሺ݀ሻ < ܶℎ‫ݏ݁ݎ‬ℎሻ then
- Mi chooses among its neighbors one that guarantees reliable data delivery with its CH
-
Repeat
- Choose v from ܰଵሺ‫ܯ‬௜ሻ
- Computing of distances : d1 and d2
- d1: distance from Mi to v
- d2: distance from v to CHi
- Computing of the probabilities: Pr1 and Pr2
-
ܲ‫ݎ‬ଵሺ݀ଵሻ = 1 −
ቀ
݀ଵ
ܴ௖
ቁ
ଶఈ
2
ܲ‫ݎ‬ଶሺ݀ଶሻ = 1 −
ቀ
݀ଶ
ܴ௖
ቁ
ଶఈ
2
Until ൫ሺܲ‫ݎ‬ଵሺ݀ଵሻ ≥ ܶℎ‫ݏ݁ݎ‬ℎሻ ܽ݊݀ ሺܲ‫ݎ‬ଶሺ݀ଶሻ ≥ ܶℎ‫ݏ݁ݎ‬ℎሻ൯
endif
End

5.SIMULATION RESULTS
In our experiments, we conducted extensive simulations with the LNS
performance of the proposed contribution
energy consumption. For that, we used a network topology in which nodes are randomly
distributed between (x = 0, y = 0) and (x = 500, y =
distinct thresholds Thresh1=0.6 and
1 summarizes the simulation parameters used for these evaluations.
Parameter
Deployment area
Simulation time
Number of nodes
Packet size
Initial node energy
Threshold
5.1. Rate of lost packets
We evaluated the performance of FTLR and the original version of
LNS model.
Figure 4: Comparison between LEACH and FTLR in terms of number of lost packets
In our experiments, we conducted extensive simulations with the LNS model to evaluate the
performance of the proposed contribution (FTLR scheme) in terms of the ratio of lost
distributed between (x = 0, y = 0) and (x = 500, y = 500). We performed simulations using two
and Thresh2=0.7 for probability of reception without errors. Table
1 summarizes the simulation parameters used for these evaluations.
Table 1 Simulation parameters
Parameter Value
Deployment area 100m x 100m
Simulation time 500 sec
Number of nodes 20, 40, 60, 80, 100
Packet size 29 bytes
Initial node energy 2 Joules
0.6, 0.7
We evaluated the performance of FTLR and the original version of LEACH with the
: Comparison between LEACH and FTLR in terms of number of lost packets
113
model to evaluate the
lost packets and
500). We performed simulations using two
for probability of reception without errors. Table
LEACH with the
: Comparison between LEACH and FTLR in terms of number of lost packets

Figure 5: Comparison between LEACH and FTLR in terms of ratio of
Figure 4 shows that the number of lost
that of LEACH. That means that the reliability is achieved with FTLR for various probability
values Thresh1=0.6 and Thresh2=0.7
Figure 5 proves the efficiency of our contribution such as the ratio of
negligible, it attains the reliability required. Therefore, our contribution would provide a sufficient
trade-off between guaranteeing the transmission reliability and achieving fault
communication links. Hence, based o
LEACH and it can be applied in realistic environment
5.2. Energy consumption
Since energy consumption is one of the
consumption and compared it between FTLR
that if the probability of reception without errors is less than a predefined threshold the cluster
member generates a random number
higher than 0.5, it will perform a retransmission of the packet; otherwise the packet will be
dropped. In this context, the energy consumption is calculated a
which considers that the energy consumed for transmitting and receivin
sensor model is respectively 4.602 µJ and 2.34 µJ
: Comparison between LEACH and FTLR in terms of ratio of lost packets
ows that the number of lost packets is almost negligible in FTLR scheme compared to
means that the reliability is achieved with FTLR for various probability
=0.7.
Figure 5 proves the efficiency of our contribution such as the ratio of lost packets is also almost
off between guaranteeing the transmission reliability and achieving fault
communication links. Hence, based on these results, FTLR can outperform the original version of
and it can be applied in realistic environment.
Since energy consumption is one of the major concerns in WSNs, we evaluated the energy
it between FTLR and the original version of LEACH. We assumed
member generates a random number whose value is between 0 and 1, and if this number is
than 0.5, it will perform a retransmission of the packet; otherwise the packet will be
. In this context, the energy consumption is calculated according to the energy model [21
which considers that the energy consumed for transmitting and receiving of one bit using MICA2
espectively 4.602 µJ and 2.34 µJ.
114
packets
scheme compared to
means that the reliability is achieved with FTLR for various probability
packets is also almost
off between guaranteeing the transmission reliability and achieving fault-tolerance of
can outperform the original version of
evaluated the energy
and the original version of LEACH. We assumed
, and if this number is
than 0.5, it will perform a retransmission of the packet; otherwise the packet will be
ccording to the energy model [21]
g of one bit using MICA2

Figure 6: Energy Consumption in LEACH and FTLR with p=0.6
Figure 7: Energy Consumption in LEACH and FTLR with p=0.7
Figures 6 and 7 shown respectively that energy consumption in
since the multi-hop communication scheme is efficient in minimizing energy consumption, by
against the retransmission is costly in terms of
: Energy Consumption in LEACH and FTLR with p=0.6
: Energy Consumption in LEACH and FTLR with p=0.7
Figures 6 and 7 shown respectively that energy consumption in FTLR is lower than in LEACH,
hop communication scheme is efficient in minimizing energy consumption, by
costly in terms of energy consumption when packets are dropped.
115
than in LEACH,
hop communication scheme is efficient in minimizing energy consumption, by
ackets are dropped.

116
6. CONCLUSION
In this paper, we used the LNS model to evaluate the performance of the original version of
LEACH protocol to illustrate its robustness in non-ideal environments. LNS model takes into
account the fluctuations of radio signal, and could therefore be considered as a realistic model
compared with the UDG model. The results showed the weaknesses of LEACH protocol in a non-
ideal simulation environment such as the LNS model. Thereby, we have proposed a fault-tolerant
LEACH-based routing protocol for non-ideal environments. Simulation results illustrate that our
proposed contribution compared with the original version of LEACH provides much better
performance in terms of the ratio of lost packets and energy consumption.
Furthermore, since most existing protocols rely on a physical layer based on the UDG model,
evaluating these protocols in a realistic layer could be interesting. Our further work includes the
analysis of other protocols in a realistic environment.
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