This document discusses clustering techniques to analyze communication overhead in wireless sensor networks. It describes how clustering can help reduce energy consumption and traffic load by limiting redundant data transmission. The document compares the K-means and fuzzy clustering algorithms. The K-means algorithm partitions nodes into K clusters based on distance from cluster centers, while fuzzy clustering allows nodes to belong to multiple clusters. A simulation found that fuzzy clustering results in lower communication overhead than K-means as node velocity increases, helping to increase network lifetime.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Based on Heterogeneity and Electing Probability of Nodes Improvement in LEACHijsrd.com
In heterogeneous sensor networks, certain nodes become cluster heads which aggregate the data of their cluster nodes and transfer it to the sink. An Improved Energy leach protocol for cluster head selection in a hierarchically clustered heterogeneous network to reorganize the network topology efficiently is proposed in this research work. The proposed algorithm will use thresholding to improve the cluster head selection. The presented algorithm considers the sensor nodes in wireless network and randomly distributed in the heterogeneous network. The coordinates of the sink and the dimensions of the sensor field are known in prior.
Wireless sensor network are emerging in various fields like environmental monitoring, mining, surveillance
system, medical monitoring. LEACH protocol is one of the predominantly used clustering routing protocols
in wireless sensor networks. In Leach each node has equal chance to become a cluster head which make
the energy dissipated of each node be moderately balanced. We have pioneered an improved algorithm
named as Novel Leach based on Leach protocol. The proposed algorithm shows the significant
improvement in network lifetime .Comparison of proposed algorithm is done with basic leach in terms of
network life time, cluster head selection, energy consumption, and data transmission to base station. The
simulation results shows that proposed algorithm can reduce network energy consumption and prolong
network life commendably. Simulation of our protocol is done with Matlab.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
International Journal of Advanced Smart Sensor Network Systems (IJASSN)ijcseit
The placement of base stations in wireless sensor networks affect the energy consumption for
communication between sensor node and base station. In this paper we analyzed the performance of the
zone based clustering protocol [2] under varying position of base stations, different zone sizes and the
effect on network life time with multiple base stations. While evaluating the communication overhead of
various cluster sizes, we observed that the optimal cluster size for a given network is complex, depending
on a range of parameters. Simulation results show that communication overhead decreases as we increase
the number of zone in the network. We show that placing multiple base stations in place of single base
station in zone based routing protocol enhance the network life time.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Based on Heterogeneity and Electing Probability of Nodes Improvement in LEACHijsrd.com
In heterogeneous sensor networks, certain nodes become cluster heads which aggregate the data of their cluster nodes and transfer it to the sink. An Improved Energy leach protocol for cluster head selection in a hierarchically clustered heterogeneous network to reorganize the network topology efficiently is proposed in this research work. The proposed algorithm will use thresholding to improve the cluster head selection. The presented algorithm considers the sensor nodes in wireless network and randomly distributed in the heterogeneous network. The coordinates of the sink and the dimensions of the sensor field are known in prior.
Wireless sensor network are emerging in various fields like environmental monitoring, mining, surveillance
system, medical monitoring. LEACH protocol is one of the predominantly used clustering routing protocols
in wireless sensor networks. In Leach each node has equal chance to become a cluster head which make
the energy dissipated of each node be moderately balanced. We have pioneered an improved algorithm
named as Novel Leach based on Leach protocol. The proposed algorithm shows the significant
improvement in network lifetime .Comparison of proposed algorithm is done with basic leach in terms of
network life time, cluster head selection, energy consumption, and data transmission to base station. The
simulation results shows that proposed algorithm can reduce network energy consumption and prolong
network life commendably. Simulation of our protocol is done with Matlab.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
International Journal of Advanced Smart Sensor Network Systems (IJASSN)ijcseit
The placement of base stations in wireless sensor networks affect the energy consumption for
communication between sensor node and base station. In this paper we analyzed the performance of the
zone based clustering protocol [2] under varying position of base stations, different zone sizes and the
effect on network life time with multiple base stations. While evaluating the communication overhead of
various cluster sizes, we observed that the optimal cluster size for a given network is complex, depending
on a range of parameters. Simulation results show that communication overhead decreases as we increase
the number of zone in the network. We show that placing multiple base stations in place of single base
station in zone based routing protocol enhance the network life time.
COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NE...IJCSIT Journal
In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...cscpconf
Wireless sensor network (WSN) is the collection of many micro-sensor nodes, connecting each other by a
wireless medium. WSN exhibits different approaches to provide reliable sensing of the environment,
detecting and reporting events. In this paper, we have proposed an algorithm for hierarchy based protocols
of wireless sensor networks, which consist of two groups of sensor nodes in a single cluster node. Each
cluster consists of a three cluster head. The event driven data sensing mechanism is used in this paper and
this sensed data is transmitted to the master section head. Hence efficient way of data transmission is possible with larger group of nodes. In this approach, using hierarchy based protocols; the lifetime of the sensor network is increased.
Energy Efficient Zone Divided and Energy Balanced Clustering Routing Protocol...csijjournal
Wireless sensor network has proven its significance in almost every field in today’s era. Wireless sensor network consists of large number of sensor nodes distributed randomly in some areas. In WSN the main objective has been increasing the network lifetime. There is zone divisional approach which has shown sound improvement in increasing the network lifetime over the Leach and EEUC protocols. The proposed protocol Energy efficient zone divided and energy balanced clustering routing protocol (EEZECR) has not only much higher network lifetime as compare to ZECR and it also has much better load balancing in the network. In the EEZECR the concept of double cluster head in a cluster is introduced which reduces the load on cluster head and very efficiently does the task of load balancing in the network thoroughly which makes this protocol favorite for many real time applications. Simulations are performed in MATLAB.
Adaptive and Energy Efficient Compressive Sensing Clustering for Wireless Sen...paperpublications3
Abstract: Adaptive and energy efficient compressive sensing clustering for wireless sensor networks. In this paper mainly used the term CS that is compressive sensing. Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper used compressive sensing used for clustering method. The enhanced hybrid compressive sensing method of using CS was proposed to reduce the number of transmissions in sensor networks.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
Erca energy efficient routing and reclusteringaciijournal
The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especially commutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However, this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering-power, better energy-efficiency, and an average gain over 285% in network lifetime.
Wireless sensor networks, clustering, Energy efficient protocols, Particles S...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
Communication synchronization in cluster based wireless sensor network a re...eSAT Journals
Abstract A wireless sensor network is acquiring more popularity in different sectors. A scalable, low latency and energy efficient are desire challenges that should meet by wireless sensor network. Clustering permits sensors to systematically communicate among clusters. Cluster based sensor network satisfies these challenges as it provides flexible, energy saving and QoS. The communication efficiency and network performance degrades if the interaction between inter-cluster and intra-cluster communication are not managed properly. The proposed work uses two approaches to solve this problem. At aiming low packet delay and high throughput first approach uses cycle- based synchronous scheduling. By completely removing necessity of communication synchronization second approach send packets with no synchronization delay. The combined scheme can take benefit of both approaches. Keywords: Wireless sensor network, clustering, communication synchronization, QoS.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NE...IJCSIT Journal
In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...cscpconf
Wireless sensor network (WSN) is the collection of many micro-sensor nodes, connecting each other by a
wireless medium. WSN exhibits different approaches to provide reliable sensing of the environment,
detecting and reporting events. In this paper, we have proposed an algorithm for hierarchy based protocols
of wireless sensor networks, which consist of two groups of sensor nodes in a single cluster node. Each
cluster consists of a three cluster head. The event driven data sensing mechanism is used in this paper and
this sensed data is transmitted to the master section head. Hence efficient way of data transmission is possible with larger group of nodes. In this approach, using hierarchy based protocols; the lifetime of the sensor network is increased.
Energy Efficient Zone Divided and Energy Balanced Clustering Routing Protocol...csijjournal
Wireless sensor network has proven its significance in almost every field in today’s era. Wireless sensor network consists of large number of sensor nodes distributed randomly in some areas. In WSN the main objective has been increasing the network lifetime. There is zone divisional approach which has shown sound improvement in increasing the network lifetime over the Leach and EEUC protocols. The proposed protocol Energy efficient zone divided and energy balanced clustering routing protocol (EEZECR) has not only much higher network lifetime as compare to ZECR and it also has much better load balancing in the network. In the EEZECR the concept of double cluster head in a cluster is introduced which reduces the load on cluster head and very efficiently does the task of load balancing in the network thoroughly which makes this protocol favorite for many real time applications. Simulations are performed in MATLAB.
Adaptive and Energy Efficient Compressive Sensing Clustering for Wireless Sen...paperpublications3
Abstract: Adaptive and energy efficient compressive sensing clustering for wireless sensor networks. In this paper mainly used the term CS that is compressive sensing. Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper used compressive sensing used for clustering method. The enhanced hybrid compressive sensing method of using CS was proposed to reduce the number of transmissions in sensor networks.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
Erca energy efficient routing and reclusteringaciijournal
The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especially commutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However, this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering-power, better energy-efficiency, and an average gain over 285% in network lifetime.
Wireless sensor networks, clustering, Energy efficient protocols, Particles S...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
Communication synchronization in cluster based wireless sensor network a re...eSAT Journals
Abstract A wireless sensor network is acquiring more popularity in different sectors. A scalable, low latency and energy efficient are desire challenges that should meet by wireless sensor network. Clustering permits sensors to systematically communicate among clusters. Cluster based sensor network satisfies these challenges as it provides flexible, energy saving and QoS. The communication efficiency and network performance degrades if the interaction between inter-cluster and intra-cluster communication are not managed properly. The proposed work uses two approaches to solve this problem. At aiming low packet delay and high throughput first approach uses cycle- based synchronous scheduling. By completely removing necessity of communication synchronization second approach send packets with no synchronization delay. The combined scheme can take benefit of both approaches. Keywords: Wireless sensor network, clustering, communication synchronization, QoS.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
Energy Efficient Zone Divided and Energy Balanced Clustering Routing Protocol...csijjournal
Wireless sensor network has proven its significance in almost every field in today’s era. Wireless sensor network consists of large number of sensor nodes distributed randomly in some areas. In WSN the main objective has been increasing the network lifetime. There is zone divisional approach which has shown sound improvement in increasing the network lifetime over the Leach and EEUC protocols. The proposed protocol Energy efficient zone divided and energy balanced clustering routing protocol (EEZECR) has not only much higher network lifetime as compare to ZECR and it also has much better load balancing in the network. In the EEZECR the concept of double cluster head in a cluster is introduced which reduces the load on cluster head and very efficiently does the task of load balancing in the network thoroughly which
makes this protocol favorite for many real time applications. Simulations are performed in MATLAB.
Data gathering in wireless sensor networks using intermediate nodesIJCNCJournal
Energy consumption is an essential concern to Wireless Sensor Networks (WSNs).The major cause of the energy consumption in WSNs is due to the data aggregation. A data aggregation is a process of collecting data from sensor nodes and transmitting these data to the sink node or base station. An effective way to perform such a task is accomplished by using clustering. In clustering, nodes are grouped into clusters where a number of nodes, called cluster heads, are responsible for gathering data from other nodes, aggregate them and transmit them to the Base Station (BS).
In this paper we produce a new algorithm which focused on reducing the transmission bath between sensor nodes and cluster heads. A proper utilization and reserving of the available power resources is achieved with this technique compared to the well-known LEACH_C algorithm.
Efficient Cluster Based Data Collection Using Mobile Data Collector for Wirel...ijceronline
Establishing an efficient data gathering scheme in wireless sensor networks is a challenging task. Lot of researches has been carried out to establish energy efficient data gathering scheme to avoid heavy traffic received by the nodes near the sink. Data gathering scheme is a significant factor in determining the network lifetime. In this paper we propose an efficient data gathering scheme by introducing clustering and mobility into the wireless sensor network. We consider data collection in wireless sensor networks by utilizing mobile data collector and cluster heads. Cluster heads are chosen and clusters are formed to collect data from the sensor nodes. The proposed scheme finds the shortest tour for the mobile data collector to collect data from the cluster heads. The shortest tour saves time and energy in data gathering.
Design of a Reliable Wireless Sensor Network with Optimized Energy Efficiency...paperpublications3
Abstract: Data gathering in wireless sensor network (WSN) is a crucial field of study and it can be optimized various algorithms like clustering, aggregation, and cryptographic technique in order to reliably transfer data between sensor and sink. But these techniques do not provide an optimized data gathering wireless sensor network because of the fact that they do not leverage the advantages of various techniques. Our problem definition is to create a reliable data gathering wireless sensor network which ensures good energy efficiency and lower delay as compared to existing techniques.
Keywords: Aggregation, Clustering, Data Gathering, Cryptography, Data Compression, Run Length Encoding.
Title: Design of a Reliable Wireless Sensor Network with Optimized Energy Efficiency and Delay
Author: Neelam Ashok Meshram
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
INCREASE THE LIFETIME OF WIRELESS SENSOR NETWORKS USING HIERARCHICAL CLUSTERI...ijwmn
Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time
of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge.
Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size,
number of Cluster head per cluster and the selection of cluster head are considered as important factors in
clustering. In this research by studying LEACH algorithm and optimized algorithms of this protocol and by
evaluating the strengths and weaknesses, a new algorithm based on hierarchical clustering to increase the
lifetime of the sensor network is proposed. In this study, with a special mechanism the environment of
network is layered and the optimal number of cluster head in each layer is selected and then recruit for the
formation of clusters in the same layer by controlling the topology of the clusters is done independently.
Then the data is sent through the by cluster heads through the multi- stage to the main station. Simulation
results show that the above mentioned method increases the life time about 70% compared to the LEACH.
INCREASE THE LIFETIME OF WIRELESS SENSOR NETWORKS USING HIERARCHICAL CLUSTERI...ijwmn
Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time
of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge.
Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size,
number of Cluster head per cluster and the selection of cluster head are considered as important factors in
clustering. In this research by studying LEACH algorithm and optimized algorithms of this protocol and by
evaluating the strengths and weaknesses, a new algorithm based on hierarchical clustering to increase the
lifetime of the sensor network is proposed. In this study, with a special mechanism the environment of
network is layered and the optimal number of cluster head in each layer is selected and then recruit for the
formation of clusters in the same layer by controlling the topology of the clusters is done independently.
Then the data is sent through the by cluster heads through the multi- stage to the main station. Simulation
results show that the above mentioned method increases the life time about 70% compared to the LEACH.
INCREASE THE LIFETIME OF WIRELESS SENSOR NETWORKS USING HIERARCHICAL CLUSTERI...ijwmn
Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time
of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge.
Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size,
number of Cluster head per cluster and the selection of cluster head are considered as important factors in
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I04503075078
1. ISSN (e): 2250 – 3005 || Vol, 04 || Issue, 5 || May – 2014 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 75
Clustering Techniques to Analyze Communication Overhead in
Wireless Sensor Network
Prof. Arivanantham Thangavelu1
, Prof. Abha Pathak2
1
(Department of Information Technology, Pad.Dr.DYPIET, Pune-18,
2
(Department of Information Technology, Pad.Dr.DYPIET, Pune-18,
I. INTRODUCTION
Wireless Sensor Networking is a network of wireless sensor nodes deployed in an area. The wireless
sensor network consists of the sensor nodes. The components of wireless sensor networks are sensing unit,
processing unit and communication unit. The sensing unit senses the surroundings and acquires the data. The
processing unit processes the acquired data and removes the redundancy. The communication unit acts as a
transceiver, i.e. it receives the data and also transmits it. Wireless Sensor Networks (WSN) now-a-days is very
popular for its specialty.WSN applications are having wide variety of domain. It include right from military
application to farming application. The surveillance system for enemy or threat, the precision agriculture where
farmer can control the temperature, humidity, etc. are the few examples of WSN applications. Health domain is
having full of challenges with which the WSN can play important role in monitoring and disseminating the data
to base station. Every application of WSN comprises of a set of sensor nodes and the base station called as sink.
It is a sort of distributed system where all the nodes can work together to convey the data up to the sink. The
entire node senses the data, depending on the application and sends it to the sink. The data may be reaching up
to the destination in a single hop or through multi hop. In single hop, the data acquired by each node is
transmitted to the base station directly. In multi hop, the data to be transmitted to the base station is through a
number of nodes, i.e. the nodes transmit their data to the next node which is then transmitted to the next node,
and finally to the base station.
II. CLUSTERING
In order to reduce the energy consumption a clustering and node redundancy approach has been
extensively studied. In Clustering approach, sensor nodes are divided into clusters. Each cluster has a leader
which is called cluster head (CH) aggregate all the data received by members of cluster and sends aggregated
data to Base Station (BS). Clustering allows aggregation of data. It helps in removing the redundant data and
combining the useful data. It limits the data transmission. The cluster system gives an impression of a small and
very stable network. It also improves the network lifetime by reducing the network traffic.
ABSTRACT:
Wireless Sensor network is a tiny sensor device about a cubic size having sensors and small battery,
which enables applications that connect the physical world with pervasive networks. These sensor
devices do not only have the ability to communicate information across the sensor network, but also to
cooperate in performing more complex tasks, like signal processing, data aggregation and compression
in the network rather than out of the network. The major problem with wireless sensor network is their
limited source of energy, the courage constraint and high traffic load. In this paper we introduce various
clustering techniques which are to be used to reduce communication overhead and increase network’s
lifetime. In the present work, the comparative evaluation of communication overhead for the wireless
sensor network based on clustering technique is carried out.
Keywords– Wireless sensor network (WSN), Clustering, K-means algorithm, Fuzzy clustering
algorithm.
2. Clustering Techniques to Analyze Communication Overhead in Wireless Sensor Network
www.ijceronline.com Open Access Journal Page 76
III. CLUSTERING ALGORITHM
Clustering algorithms are designed to reach goals like a specific cluster structure and cluster-head
distribution respectively. Also load-distribution among CHs, energy saving, high connectivity, and fault
tolerance are often emphasized goals. Clustering provides re-source utilization and minimizes energy
consumption in WSNs by reducing the number of sensor nodes that take part in long distance transmission.
Cluster based operation consists of rounds. These involve cluster heads selection, cluster formation, and
transmission of data to the base station. The operations are explained below.
1) Cluster Head Selection
In order for a node to become cluster head in a cluster the following assumptions were made.
All the nodes have the same initial energy.
There are S nodes in the sensor field.
The number of clusters is K.
Based on the above assumptions, the average number of sensor nodes in each cluster is M where
M = S/K
After M rounds, each of the nodes must have been a cluster head (CH) once.
2) Cluster Formation
The next step in the clustering phase is cluster formation after CHs have been elected. Below gives the
description of new cluster formation.
Step 1: The new cluster heads elected above broadcast advertisements (ADV) message to all non-cluster nodes
in the network using Carrier Sense Multiple Access (CSMA) MAC Protocol.
Step 2: Each sensor node determines which clusters it will join, by choosing CH that requires minimum
communication energy.
Step 3: Each non-cluster node uses CSMA to send message back to the CHs informing them about the cluster it
wants to belong.
Step 4: After CHs have received messages from all nodes, Time Division Multiple Access (TDMA) scheduling
table will be created and send it to all nodes. This message contains time allocated to each node to transmit to
the CH within each cluster.
Step 5: Each sensor node uses TDMA allocated to it to transmit data to the CH with a single- hop transmission
and switch off its transceiver whenever the distance be-tween the node and CH is more than one hop to conserve
energy. To avoid a single node transmitting data multiple times in one round, we set a threshold value G. G is
the total time of all nodes in the cluster forwarding their data to the CH in one round.
Step 6: CHs will issue new TDMA slots to all nodes in their clusters when allocated time for G has elapsed, for
each node to know exact time it will transmit data to avoid data collision during transmission that can increase
energy consumption.
Step 7: CH transceiver is always turn-on to receive data from each node in its cluster and prepare them for inter-
clusters transmission. Inter-cluster transmission is of two types: single hop and multi-hop [23,24]. We adopted
multi-hop transmission in order to save more energy during inter-cluster transmission.
3) Transmission of Data
After all data has been received, the CH performs data fusion function by removing redundant data and
com-presses the data into a single packet. This packet is transmitted to the base station via multi hops
transmission. After a certain period which is calculated in advance, the next round starts with the election of
new CHs using our initial algorithm as described in 1 above and formation of new clusters as explained in 2.
3. Clustering Techniques to Analyze Communication Overhead in Wireless Sensor Network
www.ijceronline.com Open Access Journal Page 77
3.1 K-means Clustering
K-means is one of the simplest algorithms that solve the well known clustering problem. The efficient
cluster head selection method using K-means algorithm to maximize the energy efficiency of wireless sensor
network. It is based on the concept of finding the cluster head minimizing the sum of Euclidean distances
between the head and member nodes. The K-Means method is numerical, unsupervised, non-deterministic and
iterative technique. It is used to partition an image into K clusters. The procedure follows a simple and easy way
to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea
is to define k centroids,[5] one for each cluster. These centroids should be placed in a cunning way because of
different location causes different result. So, the better choice is to place them as much as possible far away
from each other. The next step is to take each point belonging to a given data set and associate it to the nearest
centroid. When no point is pending, the first step is completed and an early group age is done. At this point we
need to re-calculate k new centroids as barycenters of the clusters resulting from the previous step. After we
have these k new centroids, a new binding has to be done between the same data set points and the nearest new
centroid. A loop has been generated. As a result of this loop we may notice that the k centroids change their
location step by step until no more changes are done. In other words centroids do not move anymore. K-Means
clustering generates a specific number of disjoint, flat (non-hierarchical) clusters. It is well suited to generating
global clusters. K-means clustering is responsible for reducing communication overhead, energy consumption in
wireless sensor network and increases network’s lifetime The basic K-mean algorithm describes following:
Step1.Choose the number K of clusters either manually, randomly or based on some heuristic.
Step2.Generate K clusters and determines the cluster’s center.
Step3. Assign each pixel in the image to the cluster that minimizes the variance between the pixel and the
cluster center
Step4.Re-compute cluster centers by averaging all of the pixels in the cluster.
Step5.Repeat steps 3 and 4 until some convergence criterion is met.
3.2 Fuzzy Clustering
In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one
cluster. Fuzzy clustering methods, however, allow the objects to belong to several clusters simultaneously, with
different degrees of membership. In many situations, fuzzy clustering is more natural than hard clustering.
Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather
are assigned membership degrees between 0 and 1 indicating their partial membership. Fuzzy clustering is a
process of assigning these membership levels, and then using them to assign data elements to one or more
clusters. One of the most widely used fuzzy clustering algorithms is the Fuzzy C-Means (FCM) Algorithm. The
algorithm of fuzzy c-means clustering is as follows:
Step1. Choose a number of clusters in a given image.
Step2. Assign randomly to each point coefficients for being in a cluster.
Step3. Repeat until convergence criterion is met.
Step4. Compute the center of each cluster.
Step5.For each point, compute its coefficients of being in the cluster [4-5].
IV. SIMULATION RESULT
Simulation was carried out in Matlab. As seen from Figure (1) communication overheads increases
significantly when velocity of sinks nodes increases using K-means clustering algorithm.
Figure (2) shows using Fuzzy clustering algorithm communication overhead increases with velocity on sink
node and drops at the end.
4. Clustering Techniques to Analyze Communication Overhead in Wireless Sensor Network
www.ijceronline.com Open Access Journal Page 78
10 20 30 40 50 60 70 80 90 100
5
10
15
20
25
30
35
40
45
Velocity in m/s
CommunicationOverhead
Communication overhead in WSN with diffrent velocity for BBM
Figure (1)
10 20 30 40 50 60 70 80 90 100
0
5
10
15
20
25
30
35
Velocity in m/s
CommunicationOverhead
Communication overhead in WSN with diffrent velocity for BBM
Figure (2)
V. CONCLUSION
As a result of these experiments, we evaluated the communication overhead in WSN using K-means
clustering algorithm and Fuzzy clustering algorithm. We find that FCA is stable and energy efficient algorithm
because it gives the low communication overhead as compare to K-means algorithm.
REFERENCES
[1] Shiv Prasad Kori and Dr R K baghel “Evaluation of Communication Overheads in Wireless Sensor Networks” International Journal of
Engineering Research (ISSN: 2319- 6890) Volume No.2, Issue No.2, pp: 167-171 01 April 2013.
[2] Zhang/RFID and Sensor Networks AU7777_C012 Page Proof Page 323 2009-6-24 “Clustering in Wireless Sensor Networks”.
[3] Kavita Musale and Sheetal Borde “Analysis of Cluster Based Routing Protocol for Mobile Wireless Sensor Network” International
Journal of Advanced Trends in Computer Science And Engineering, Vol.2, No.1, Pages: 124-129 (2013)
[4] Pallavi Mathur , Soumya Saxena and Meghna Bhardwaj “Node clustering using K Means Clustering in Wireless Sensor Networking
“National Conference in Intelligent Computing & Communication.
[5] A Tutorial on Clustering Algorithms.
[6] S. Adaekalavan and C. Chandrashekar “A Comparative Analysis of Fuzzy C- Means Clustering and Harmonic K Means Clustering
Algorithms” European Journal of Scientific Research ISSN 1450-216X Vol.65 No.1 (2011)
[7] Mrs. Bharati R.Jipkate and Dr. Mrs. V.Gohokar “A Comparative Analysis Of Fuzzy C-Means Clustering and K-Means Clustering
Algorithm” International Journal of Computational Engineering Research / ISSN: 2250- 3005 IJCER | May-June 2012
[8] Mathworks (http://www.mathworks.com)