An Energy Efficient Cross-Layer Cluster based Multipath Routing
Protocol for WSN
1SHIVA KUMAR V., 2RAJASHREE V. BIRADAR, 3V. C PATIL
1Deptartment of CSE, RYMEC, Ballari, Karnataka, INDIA
2Deptartment of CSE, BITM, Ballari, Karnataka, INDIA
3Deptartment of ECE, BITM, Ballari, Karnataka, INDIA
Abstract: - Wireless sensor networks are the most widely used technologies with a wide range of applications
and data collection processes. WSN is the major component for real-time data collection at various places
where human intervention is difficult. With so many features, advantages, and impact WSN have some major
challenges and hurdles, and these challenges the performance of the WSN is reducing and affecting the
application part. This paper discusses the impact of WSN, and why WSN is gaining so much impact in recent
days from a business perspective. A detailed survey and analysis made on the major challenges of WSN are
carried out to identify the major performance factor of WSN. This paper aims to solve the major performance
factor of WSN - power optimization, using novel cluster-based multipath routing. The proposed routing ensures
the energy efficiency of the WSN for the data transfer process. Finally, the proposed method will be compared
over some standard algorithms to analyze the performance in terms of the lifetime of the network and packet
transmission by the sensor.
Key-Words: - Cross-Layer Protocol, MAC Protocol, TDMA, WSN, Routing, Multipath routing, Clustering,
LEACH, EECACL, CL-Model, OSI Layers.
Received: October 25, 2021. Revised: October 22, 2022. Accepted: November 23, 2022. Published: December 9, 2022.
1 Introduction
Wireless Sensor Networks (WSN) is one of the most
prominent technologies in the recent digital and
technology world. WSN contributes to the major
development and implementation of other
technologies like- IoT, Cloud computing, Data
Analytics, AI & ML, and others. In all these
technologies WSN is the major component as it is
used for main data collection automatically where
manual intervention is difficult or not feasible. The
data collected from WSN is the main data source to
store in the cloud and the same data is used for data
analytics, and visualization using AI and ML.
According to the report published by fortune
business insight the global WSN market value was
$38.99 billion in 2018 and is expected to increase
by $148.67 billion by 2026, [1]. Another forecast on
WSN predicted the market value of WSN will reach
$215 billion by 2028, which was $56 billion in
2020, [2]. All this impact is due to the great demand
for automated systems where human intervention is
quite difficult. Some of the automated applications,
[3-5] of WSN are – Health Sector, Automatic
Traffic Control, Military Applications, Surveillance
Application, Industrial IoT, wearable devices, and
many others, [6], [7], [8]. Apart from the
application, WSN is gaining impact because of its
usage – Hardware, Software and Services, Area of
usage, types of sensors used for different
applications, and major contributors as shown in
Fig. 2.
Fig. 1: WSN Market Forecast [2]
Fig. 2: WSN Primary Demand, [2]
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With this market forecast and demand, WSN is
gaining a huge impact in recent days. This impact of
WSN is making a new path for the researchers to
focus on increasing the performance of the WSN. In
this paper, a novel cross-layer-based clustered
approach is proposed to optimize the energy
consumption for data transmission in WSN.
This is paper is organized as follows:
This section 1 Introduction presents the impact and
need of WSN from a market perspective and scope
for the researchers to fill the research gaps. Section-
2 Problem Formulation focuses on the survey of
various works carried out by the researchers in the
field of power optimization of WSN, cross-layer
approach, OSI layer features of WSN, and many
others. Section 3 presented the proposed work as a
novel cluster-based cross-layer approach to
maximize the lifetime of the network. Section 4
gives the results and discussion of the proposed
work over some standard benchmark algorithms and
comparison over identified metrics. Section -5
finally concludes the paper by highlighting the
features of the proposed cross-layer-based approach.
2 Problem Formulation
In this section, a detailed survey is carried out for
identifying the various challenges in the WSN for
routing and energy optimization. The survey carried
out is explained in detail as follows:
2.1 OSI Layer
The first part of the survey starts with the study of
the OSI layer model since the proposed method is
based on the cross-layer approach. The proposed
approach aims to contribute the Data Link Layer,
Network Layer, and Transport Layer of WSN for
effective energy optimization, [9] as shown in Fig.
3.
Fig. 3: OSI Layer
2.2 EECACL
An Energy Efficient Clustering Algorithm using
Cross Layer for WSN has been proposed in
EECACL, [9]. This method focused on optimizing
the energy consumption of the WSN using a
LEACH, [10] based cross-layer approach. A cluster
model has been proposed considering the various
parameters for the implementation of the algorithm.
2.2 EELP
An Enhanced Efficient LEACH-based Protocol
proposed over the cross-layer network. In this paper,
a novel cross-layer approach is used based on the
probability of cluster head selection. This method
focused on the packet ratio, network lifetime, and
analyzing the delay in the network, [11].
2.3 Energy-efficient Fuzzy Logic-Based
Cross-Layer Hierarchical Routing Protocol
A fuzzy logic-based cross-layer approach was
implemented for efficient routing in WSN. Using
fuzzy logic, a novel hierarchical approach was
implemented to improve the network lifetime of the
WSN based on the LEACH protocol, [12].
2.4 Cross-Layer Framework
An OSI layer-based cross-layer framework has been
designed in this reference paper. This paper has
considered and contributed to the Physical, Data
Link, and Network Layer of the OSI model. This
paper is also based on the LEACH clustering
mechanism with a primary focus on – reducing
energy consumption, reducing latency, increasing
the throughput and scalability, and enhancing the
overall network lifetime, [13]. The framework used
is shown in Fig. 4.
Fig. 4: Cross-Layer Framework of WSN [13]
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2.5 Cross-layer Design with Weighted Sum
Approach
A weighted sum approach based on the minimal
hop-count cross layer was proposed in this reference
to enhance the lifetime of the WSN in smart city
applications. Various routing metrics like the
number of nods, battery capacity, minimal total
power metric, and conditional maximum and
minimum battery capacity were considered to
enhance the performance of WSN, [14].
2.6 Multipath Cros Layer
A multipath routing based on the Node-Disjoint
Route Establishment algorithm over the cross-layer
to enhance the performance is developed. This
paper concluded the benefits of having
Multipath routing to improve the performance of the
network. This also impacts the duty cycle process
at the MAC level in WSN, [15].
Table 1 presents the analysis of the existing
work in the field on WSNs and also the feasible
future enhancement to enhance the performance of
the WSNs.
Table 1. Analysis of Various WSNs Approaches
Ref.
OSI Layer
Issues
Addressed
[9]
Data Link
Energy
Optimization
[10]
Network
Layer
Energy
Optimization
using
Clustering
[11]
Data Link
and
Network
Cluster-
based energy
optimization
[12]
Data Link
and
Network
Efficient
routing using
Fuzzy Logic
[13]
Physical,
Data Link,
and
Network
Reducing
Energy
Consumption
and
increasing
the
throughput
[14]
Data Link
and
Network
Energy
Optimization
[15]
Data Link
and
Network
Multipath
route
optimization
Based on the existing work and analysis carried out
section 3 explains the need for a cross-layer-based
approach for the WSNs.
3 Research Requirements
In Section 2 a detailed survey has been carried out
concerning the cross-layer approaches, performance
factors of WSN, and others. Based on the survey
following research requirements were identified.
3.1 WSN Application Requirements
For any kind of WSN application, the algorithm
must be capable of ensuring – Lifetime of WSNs,
[16], [17], Data Availability, and Data Freshness,
[18], [19]. A lifetime of WSN can be achieved by
increasing the lifetime of the sensors in the network,
if the sensor node's energy consumption is reduced
then the overall network lifetime can be increased.
Coming to Data Availability and Data Freshness
depends on the routing path of the data transferred
from sensors to the base station. If the multi-path or
multi-hop routing is enabled in the network means
there will be no data loss and also one can ensure
the freshness of data without getting the data
delayed.
3.2 Benefits of Multi-path
With the integration of multi-path routing in the
WSNs, all sensor nodes in the network have
multiple options to transfer the data collected to the
base station. Without a multi-path, each sensor node
will have only one path to transfer data collected to
the base station. This process ensures data freshness
in the WSNs if there is no multi-path the data may
be transferred to the base station with a delay by
searching for an alternate path.
3.3 Need for Research in WSN
Sections 3.1 and 3.2 address the issues and solutions
required for applications in the WSNs. To enhance
the performance of the WSNs there is a need to
address various challenges and issues, [20]. This
paper aims to contribute to the WSNs domain by
increasing the lifetime of the network using the
cross-layer multi-hop approach as discussed in
section -4.
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4 Proposed Work
4.1 Outcomes of Survey
Based on the survey the proposed algorithm focuses
on improving the Lifetime of the WSNs and also
multi-hop routing based on the cluster to ensure data
availability and freshness. The proposed algorithm
is implemented by considering the following
parameters identified from the survey carried out.
Parameters identified:
• Total number of Nodes Deployed
• Network Area where nodes are deployed
• Node Energy: All sensors node’s Initial energy
• Cluster Head Probability: Number of nodes
becoming cluster head
• Routing: Multi-hop Clustering
Apart from the above parameters, the proposed
algorithm for optimizing the energy consumption in
the WSN.
Additional Parameters:
Minimum Transmission energy (MTE): This
is the energy that every cluster node must
possess to become the cluster head. This method
will overcome reliability issues and increase the
throughput. This is calculated using the threshold
Base Station Distance.
o Threshold Base Station distance (TBS) - A
constant distance chosen by the nodes to transfer
the data to BS from Cluster Head. This is to
ensure that every CH will be able to transfer the
data to BS without any failure. The nodes which
do not have sufficient energy to transfer the data
to this minimum distance will not be considered
to become the CH. These nodes will be
considered normal members and utilized up to
their efficiency rather than making it CH and
dissipating more energy for the unsuccessful
transmission.
MTE = (ETX ∗ EDA) ∗ K + EFS ∗K(TBS ∗ TBS)
Where, TBS = 30m / 100 Sqm network area,
EDA, EFS, and ETX are the Node’s circuit
energy required for Data Aggregation, Antenna
Energy Dissipation, and Energy Required for
Transmission
4.2 Proposed Algorithm
Step-1: All the nodes will be deployed in the given
Network Area
Step- 2:All nodes will be Normal Nodes and
Initially, BS will be selecting some
probability of nodes to become the CH
Step- 3:The CH will advertise its presence to other
member nodes using a TDMA Time slot
Step-4: Normal member nodes will acknowledge
the CH message and Transfer the Data to
the nearest CH or BS using the Euclidean
distance when its TDMA slot
duration.Distance (node1 to node2) =
Sqrt((node2.x − node1.x)2 + (node2.y –
node1.y)2)
Step-5: After receiving the data from members CH
will find the nearest CH or Base Station and
transfer the Data to it thereby causing
Multiple path routing
Step-6: -During the next round new sets of CH will
be elected CH satisfying the condition in
Step-7
Step-7: The nodes which are having MTE will be
added to the CH group, this will ensure the
nodes won’t fail to transfer the data
collected from many members
Step-8: The newly selected CH will take over and
repeats Step 3
4.3 Network Topology
Fig. 5 and Fig. 6 show the Network topology
implemented for the proposed work. The nodes are
indicated in Circles where the circle area decreases
as the round progresses.
• Square Node – Base Station
• Green Nodes – Normal
• Yellow Nodes – CH nodes
• Red Nodes – Dead nodes
Data Transmission is represented using a
Straight Line:
• Green Line – Data Transfer from Normal Node
to CH
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• Blue Dotted Line – Data Transfer from CH to
Base Station
• Black Line _ Data Transfer from CH to CH to
form a multi-hop transmission
Fig. 5: Network Topology (Initial Stage)
Fig. 6: Network Topology (Final Stage)
The proposed algorithm comes with a novel method
of clustering and multi-path routing using various
parameters. Section 4.4 further explains the
advantage of the proposed algorithm.
4.4 Advantage of Proposed Cross Layer
Following are the features of the proposed algorithm
concerning the OSI model:
Physical Layer: The node is considered for
becoming CH only after the analysis of the
Physical Layer i.e. after identifying the battery
capacity or energy available.
Data Link Layer: In the Clustering Process,
TDMA protocol is adopted as the Data can be
transferred only during the allotted time slot
thereby avoiding Collision and Traffic
Congestion at the CH and BS. Thereby dealing
with the Data Link Layer and Part of the
Network Layer
Network Layer: Using Multi-Hop / Multiple
Path Routing protocol routing is done
efficiently in the optimized way to solve the
power consumption issues in unnecessary
routing thereby Network layer issues are also
considered and addressed.
Transport Layer: Since the CH is identified
based on MTE it is ensured that the CH is
RELIABLE in the data transmission Phase
there by Transport layer is also considered in
implementing the proposed protocol.
4.5 Result and Discussion
This section presents the results of the proposed
algorithm which is simulated using MATLAB
considering the various parameters identified from
the survey as standard benchmarks. Apart from
these parameters some additional parameters are
identified concerning the proposed work and used
for the simulation. By considering these additional
parameters and the algorithm proposed has shown a
significant improvement in the performance of the
WSNs
Fig. 7 presents the Lifetime of the various existing
WSN algorithms which are discussed in section 2
[3]. Considering these results of the existing
algorithms, a comparison is made with the proposed
algorithm as shown in Figure-8.
Fig. 7: Lifetime of the Existing Algorithms, [3]
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Fig. 8: Lifetime of the Proposed Algorithm, [3]
Fig. 8 shows the comparison of various Cross-layer
WSN routing algorithms. In this LEACH, CL
MODEL and EECACL (Mentioned as Proposed in
Above Graph). This simulation is carried out for
100 Nodes for 50 rounds. The Network lifetime
graph represents the number of alive nodes for the
given simulation rounds.
At the end of the simulation, one can observe the
number of alive nodes from LEACH is around 20,
CL Model is around 38, and EECACL is around 48.
The simulation setup is carried out by our Proposed
algorithm (CLCBMR) where the number of alive
nodes is 59 which is more than all the other three
protocols used for comparison.
The lifetime of the proposed CLCBMR is 59%,
EECACL is 48%, and CL and LEACH are 38% and
20% respectively. So, thereby we can conclude that
the proposed CLCBMR is 11% more efficient than
EECACL, 21% more efficient than CL Model, and
39% more efficient than LEACH.
Table 1 gives information on the failure nodes at
various rounds compared with the LEACH, CL-
Model, and EECACL existing algorithms. One can
observe the algorithm's efficiency by looking into
the number of failure nodes at the end of various
rounds. At the 0th round number of nodes is alive for
all algorithms at 100% as the round progresses the
nodes will dissipate more energy and become a
failure. At the end of the 50th round, the number of
live nodes for the proposed algorithm is more than
the existing algorithms. Thereby the proposed
algorithm is more efficient than the existing
algorithms.
Table 2. Failure Nodes Comparison
Algorithms
Rounds
0
10
20
30
40
50
LEACH
100
95
68
48
30
20
CL-
MODEL
100
95
78
62
50
38
EECACL
100
95
80
68
58
48
Proposed
100
100
89
75
63
59
Fig. 9 presents the total number of live nodes of
various existing algorithms referred against our
proposed algorithm where the x-axis represents the
number of rounds and the y-axis represents the
number of live nodes.
During each 10th round of over-simulation rounds,
the number of rounds for the proposed algorithms is
more than other existing algorithms. In the 10th
round, the proposed algorithm’s live node is 100%
whereas other algorithms have around 95%.
Similarly, at the 50th round, the proposed algorithm
has around 59% alive nodes whereas LEACH has
20%, CL-Model has 39% and EECACL has 48%
alive nodes. This justifies the efficiency of the
proposed algorithm.
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Fig. 9: Number of Alive Nodes
Fig. 10 represents the number of nodes becoming
the cluster head during each round based on the
probability. Where the X-axis represents the number
of rounds and the y-axis represents the number of
nodes that become CH. Fig. 11 represents the
number of packets transmitted to the Cluster Head
to Base Station and Normal Member to Cluster
Head during various rounds. In this graph, the x-axis
represents the number of rounds and the y-axis
represents the number of packets transmitted
Fig. 10: Number of Nodes of Becoming CH
Fig. 11: Packet Transmission
5 Conclusion
Wireless Sensor Networks are one of the most
prominent technologies with a wide range of
applications in many domains. Along with this
impact, the application requirements and challenges
make it compromise the performance of the WSNs
application. The proposed work has developed an
algorithm that focuses on the performance factor of
WSNs. The proposed algorithm has resulted in an
efficient result compared to some of the benchmark
algorithms. The proposed algorithm has increased
the lifetime of the network by reducing the energy
dissipated at the sensor nodes. The result achieved
shows a significant number of packets transmitted to
the cluster head and base station. This result is
achieved because of the cross-layer implementation
in the WSNs and this also contributed to different
OSI layers. Thus, the proposed cross-layer approach
shows an optimized approach for making a
significant contribution to the WSNs domain.
The main motive of this contribution is to enhance
the performance of WSN using the Cross-Layer
approach. Hence, the proposed work doesn’t
highlight the WSNs limitation for mobile nodes,
heterogeneous nodes, and large-scale WSNs
applications. Further, the proposed algorithm can be
enhanced for various WSNs applications, and
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heterogeneous sensor nodes and also mobile sensor
nodes can be considered. The proposed algorithm
can also be used for various WSNs applications
where the nodes are deployed with high intensity
and the need for fresh data collected from sensors
are where much expected as in Surveillance and
Military applications.
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