A NOVEL RBCMIC METHOD FOR HIERARCHICAL NETWORK AND K-MEANS TRUST MECHANISM FOR NON-HIERARCHICAL NETW

Abstract

Abstract: Wireless Sensor Networks (WSNs) are widely used in various applications today, with nodes distributed across areas where a cluster head is chosen to enable communication between clusters. Conventional methods for selecting cluster heads rely on random probability, residual energy, or mobility factors, but these approaches often select ineffective nodes. This study introduces an optimized method for cluster formation using an unsupervised k-means algorithm, where cluster heads are elected based on parameters such as distance to the control center, residual energy, and mobility, ensuring a reliable choice. While existing approaches like Depth-Based Ratio (DBR) and DEAD prioritize high-energy paths, they require multiple path discoveries, leading to energy depletion. The RBCMIC algorithm improves path selection by choosing cooperative nodes, yet it faces delays. The proposed modified RBCMIC method streamlines this by identifying a single path and a forward node based on residual energy, distance, and channel quality, enhancing efficiency and network lifespan.

Specification

Abstract
Wireless Sensor Networks (WSNs) are widely used in various applications today,
with nodes distributed across areas where a cluster head is chosen to enable
communication between clusters. Conventional methods for selecting cluster heads rely
on random probability, residual energy, or mobility factors, but these approaches often
select ineffective nodes. This study introduces an optimized method for cluster formation
using an unsupervised k-means algorithm, where cluster heads are elected based on
parameters such as distance to the control center, residual energy, and mobility, ensuring
a reliable choice. While existing approaches like Depth-Based Ratio (DBR) and DEAD
prioritize high-energy paths, they require multiple path discoveries, leading to energy
depletion. The RBCMlC algorithm improves path selection by choosing cooperative
nodes, yet it faces delays. The proposed modified RBCMlC method streamlines this by
identifying a single path and a forward node based on residual energy, distance, and
channel quality, enhancing efficiency and network lifespan.
Prior Art Statement
1. Cluster Head Selection Based on Residual Energy and Distance:
LEACH Protocol (Low Energy Adaptive Clustering Hierarchy): LEACH is a well-known
protocol that u_ses probabilistic methods to choose cluster heads based on residual
energy to extend the network lifetime. However, it does not consider other factors like
mobility, which can limit its effectiveness in dynamic environments.
TEEN Protocol (Threshold Sensitive Energy Efficient Sensor Network): This protocol
selects cluster heads based on a combination of residual energy and communication
distance, primarily for applications requiring periodic data transmission, but it lacks
dynamic adaptation based on node mobility.
2. Path Selection Using Multi-Path Routing and Cooperative Nodes:
Depth-Based Routing (DBR): DBR is widely used in underwater sensor networks and
identifies the best path by calculating depth-based ratios across multiple paths. Though
it is effective for finding high-reliability paths, the method is energy-intensive due to
constant depth calculations.
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DEAD Algorithm (Dynamic Energy-Aware Data aggregation): DEAD selects cluster
heads based on residual energy but overcomes DBR's hop deficiency by using secondary
cluster headSito distribute communication load. However, its energy efficiency decreases ..
in larger networks due to the need for continuous multi-path exploration.
3. Single Path Optimization to Reduce Delay and Complexity:
RBCMJC (Residual-Based Cooperative Multipath for Information Collection): RBCMIC
improves path selection by using multiple cooperative nodes within the destination
cluster. However, RBCMlC has drawbacks such as increased delay in finding the optimal
path and high communication complexity due to its multi-path nature. This inspired the
proposed modification, which limits path selection to a single route, improving delay and
reducing overhead.
AODV (Ad hoc On-Demand Distance Vector Routing): AODV is commonly used for
dynamic, single-path route discovery and improves data delivery by limiting path
establishment to a single optimal route. While AODV offers reduced delay, it does not
account .for residual energy, which can lead to faster energy depletion in nodes on the
selected path.
4. Energy-Efficient and Delay-Aware Routing Protocols:
PEGASlS (Power-Efficient GAthering in Sensor Information Systems): PEGASIS
minimizes energy usage by arranging nodes in a chain structure for sequential data
transmission, which reduces the number of transmissions per node. While it extends
network life, its single-chain structure introduces delays, particularly in large networks
with high mobility.
EEUC (Energy-Efficient Unequal Clustering): EEUC addresses energy efficiency by
forming clusters of unequal sizes, reducing energy consumption for nodes closer to the
base station. However, it suffers from delays in large-scale implementations due to
uneven data flow across clusters.
These protocols h·ave contributed significantly to WSN advancements but still face
challenges in balancing energy efficiency, delay, and communication overhead. The
proposed modified RBCMlC approach aims to address these gaps by combining singlepath
optimization with dynamic cluster head selection based on multiple factors, offering
improved efficiency and reduced latency in WSNs.
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Background:
Wireless Sensor Networks (WSNs) are networks of spatially distributed sensor
nodes that monitor and transmit data about environmental conditions such as
temperature, humidity, movement, and pressure. These networks play a crucial role in
applications rqnging from environmental monitoring, healthcare, and military
surveillance to smart city infrastructures and agricultural management. Typically, WSNs
consist of low-power nodes with limited computational resources, memory, and battery
life, making energy efficiency a paramount concern. Efficient data transmission strategies
are essential for prolonging network lifetime and ensuring reliable communication.
One widely adopted approach in WSNs is the formation of clusters, where nodes
are grouped into clusters with a designated cluster head responsible for aggregating and
transmitting data to the base station. The selection of cluster heads is critical for efficient
communication and energy conservation. However, traditional methods of cluster head
selection, such as random or probabilistic approaches, often fail to account for nodes'
energy levels, mobility, or the distance to the control center, leading to suboptimal cluster
hea'd choices. As a result, the network may suffer from frequent re-clustering, high energy
consumption, and reduced lifetime.
To optimize routing in WSNs, several path selection protocols have been
developed. Methods like Depth-Based Ratio (I;JBR) calcl,llate paths based on metrics like
depth ratios, while the Dynamic Energy-Aware Data Aggregation (DEAD) algorithm
attempts to improve upon DBR by utilizing secondary cluster heads for better load
distribution. However, these multi-path approaches can introduce overhead, increased
delays, and high communication costs. Another advancement, the Residual-Based
Cooperative Multipath for Information Collection (RBCMIC) algorithm, selects multiple
cooperative nodes within clusters to enhance the robustness of data transmission .
Although RBCMIC reduces some complexity, it still faces latency issues due to the time
taken for cooperative path selection.
The goal of recent advancements is to develop routing algorithms that address the
inherent trade-offs in WSNs-balancing energy efficiency, data latency, and network
longevity. The proposed modified RBCMIC approach improves upon existing methods by
electing a single optimal path based on 'factors like residual energy, proximity to the
control center, and channel quality, thus reducing energy expenditure and delay. This
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background emphasizes the ongoing need for improved WSN protocols that can adapt to
dynamic conditions while maintaining network performance and reliability.
Short Description of Diagram:
Fh:ure: Methodology Flow
Details Description of Diagram:
Nodes Placement in an Area
Node Placement Algorithm (101) is responsible for formation of network by randomizing the
placement of the nodes within the limits. Node Placement algorithm places the nodes in the
network and also generates a matrix known as Node Deployment Matrix which is of order
N * 3. Where N is the number of nodes in the network. The first column will be Node lD,
second column is the x position for the node and Third Column is they position for the node.
Cluster Formation Algorithm
Cluster Formation algorithm (102) divides the entire are into multiple zones. Each Zone has
set of nodes in its zone. This is the algorithm which is responsible for deploying the nodes.
The entire area is divided into zones with each zone bounded with the limits with some xmin
and xmax.
DEPTHS Cluster Head Algorithm
The algorithm (103) takes a set of nodes belonging to the cluster and computes the
random probability. The node which has highest precedence will act as cluster head.
DEPTHS Path formation and Data Delivery
The set of nodes (104) in the initiator cluster are first chosen leaving the cluster head
and initiator. From each of those nodes the path formation is initiated. The cluster head of
the initiating cluster is chosen from the initiator after that the communication will happen to
the base station. The base station scans each of the cluster until control center node is
reached.
DEADs Cluster.Head Algorithm
The algorithm (108) takes a set of nodes belonging to the cluster and computes the residual
energy for all the nodes in the network and then picks the node which is having the highest
residual energy as the cluster head.
DEADs path formation
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The set of nodes (I 07) in the initiator cluster are first chosen leaving the primary cluster head
as well as secondary cluster head and initiator. From each of those nodes the path formation
is initiated. The cluster head of the initiating cluster is chosen from the initiator after that the
communication will happen to the secondary cluster head and to the base station.
RBCMIC Cluster Head
The algorithm (106) takes a set of nodes belonging to the cluster and computes the residual
energy for all the nodes, distance with respect to the base station for the specific cluster along
with mobility ratio in the cluster.
RBCMIC Path Formation
The RBCMIC method (105) will fmd multiple paths by making use of co-operative nodes of
the destination cluster. First from the initiator node the link is established with respect to the
initiator cluster head after that the REQ packet is send to all cluster heads of remaining
clusters one the cluster will send a ACK which has the destination control center. After the
destination cluster head the link is established to each co-operative nodes to the destination
control center.
Modified RBCMIC Cluster Formation
The Modified RBCMIC (I 09) will make use of unsupervised machine learning based
k means algorithm which is used to fonn dynamic clusters based on number of nodes, number
of clusters. During the initialization phase the random centers are selected. Atler that the
distance from each cluster center to nodes arc computed and then node whose distance is
minimum with respect to cluster center is assigned a class label of that specific cluster.
Modified RBCMIC Cluster Head Election
The algorithm (110 and Ill) takes a set of nodes belonging tothe cluster and computes the
residual energy for all the nodes, distance with respect to the base station for the specific
cluster along with mobility ratio in the cluster. Hence the total selection factor is the sum of
remaining energy, reciprocal of distance, reciprocal of mobility. After computation of
selection factors for all the nodes in the cluster the node which is having the highest selection
factor is chosen as the cluster head.
Bayesian Based Route Discovery
0 The Bayesian method (113) will find 2 hop routes, multiple routes are found out from
~> source node to destination lOT node, the best route will be selected which has the highest tmst.
0 z· - The individual route discovery will pick forward node based on highest value of trust. . - - .. -
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Beta Distribution based Route Discovery
The Beta Distribution based method (114) will find the I hop neighbors. After that the
routes form the initiators till destination JOT node is done. The route which has maximum
Eigen trust will be chosen as the best route. The individual route discovery is performed by
selecting the node of highest trnst based on Eigen clistribution.
Machine Learn Eigen Trust based Algorithm
The machine learning algorithm (115) k means is taken and then nodes are classified
into high trnst, low trnst and medium trnst nodes. The routing is performed based on trust level
and lowest clistance with respect to destination.
Summary:
Wireless Sensor Networks (WSNs) are essential for various applications but face
challenges due to limited energy resources and the need for efficient data transmission.
In WSNs, nodes are organized into clusters with a selected cluster head to manage
communication, yet traditional cluster head selection methods-often based on random
or probabilistic criteria-can lead to ineffective choices, reducing network lifespan.
Advanced approaches like Depth-Based Ratio (DBR) and Dynamic Energy-Aware Data
Aggregation (DEAD) attempt to improve path reliability by calculating energy-efficient
paths and distributing load across secondary cluster heads, though they often increase
complexity and delay. The Residual-Based Cooperative Multipath for Information
Collection (RBCMIC) method further optimizes by selecting cooperative nodes, yet
suffers from delays in identifying the best path.
The proposed modified RBCMIC method seeks to address these issues by electing
a single, optimal path based on residual energy, distance to the control center, and
channel quality, resulting in reduced energy consumption, decreased delay, and extended
network lifespan.
1. Sreekantha B
2. Dr. Sha ila K
Date: 06-11-2024 - '· .
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Claims:
Claim 1: Optimized cluster formation using an unsupervised k-means algorithm
ensures more effective cluster head selection by considering essential parameters
like distance to the control center, residual energy, and mobility, instead of relying
on random probability.
Claim 2: 'Phe modified RBCMlC method enhances path selection efficiency by
identifying a single optimal path and forward node based on residual energy,
distance, and channel quality, reducing communication overhead and extending the
network's lifespan.
Claim 3: The proposed approach decreases energy consumption in network nodes
by limiting unnecessary communication between the base station and network
nodes, addressing key drawbacks of traditional methods such as Depth·Based Ratio
(DBR) and DEAD.
Claim 4: By selecting a single path and reducing the need for multiple cooperative
nodes, the modified RBCMlC method minimizes path selection delays. improving
data packet delivery speed and efficiency.

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