Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved
Data aggregation in wireless sensor network based on dynamic fuzzy clusteringcsandit
Wireless Sensor Networks (WSN) use a plurality of s
ensor nodes that unceasingly collected and
sent data from a specific area to a base station. C
luster based data aggregation is one of the
popular protocols in WSN. Clustering is an importan
t procedure for extending the network
lifetime in WSNs. Cluster Heads (CH) aggregate data
from relevant cluster nodes and send it to
the base station. A main challenge in WSNs is to se
lect suitable CHs. In another communication
protocol based on a tree construction, energy consu
mption is low because there are short paths
between the sensors. In this paper, we propose Dyna
mic Fuzzy Clustering (DFC) data
aggregation. The proposed method first uses fuzzy d
ecision making approach for the selection
of CHs and then a minimum spanning tree is construc
ted based on CHs. CHs are selected
efficiently and accurately. The combining clusterin
g and tree structure is reclaiming the
advantages of the previous structures. Our method i
s compared to Low Energy Adaptive
Clustering Hierarchy (LEACH), Cluster and Tree Dara
Aggregation (CTDA), Modified Cluster
based and Tree based Data Aggregation (MCTDA) and C
luster based and Tree based Power
Efficient Data Collection and Aggregation (CTPEDCA)
.Our method decreases energy
consumption of each node. In DFC data aggregation,
the node lifetime is increased and the
survival of the WSN is improved.
34 9141 it ns2-tentative route selection approach for edit septianIAESIJEECS
Wireless Sensor Networks (WSNs) assume a crucial part in the field of mechanization and control where detecting of data is the initial step before any automated job could be performed. So as to encourage such perpetual assignments with less vitality utilization proportion, clustering is consolidated everywhere to upgrade the system lifetime. Unequal Cluster-based Routing (UCR) [7] is a standout amongst the most productive answers for draw out the system lifetime and to take care of the hotspot issue that is generally found in equivalent clustering method. In this paper, we propose Tentative Route (TRS) Selection approach for irregular Clustered Wireless Sensor Networks that facilitates in decision an efficient next relay to send the data cumulative by Cluster Heads to the Base Station. Simulation analysis is achieved using the network simulator to demonstrate the effectiveness of the TRS method.
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
ENERGY OPTIMISATION SCHEMES FOR WIRELESS SENSOR NETWORKcscpconf
A sensor network is composed of a large number of sensor nodes, which are densely
deployed either inside the phenomenon or very close to it. Sensor nodes have
sensing, processing and transmitting capability . They however have limited energy
and measures need to be taken to make op- timum usage of their energy and save
them from task of only receiving and transmitting data without processing. Various
techniques for energy utilization optimisation have been proposed Ma jor players are
however clustering and relay node placement. In the research related to relay node
placement, it has been proposed to deploy some relay nodes such that the sensors
can transmit the sensed data to a nearby relay node, which in turn delivers the data
to the base stations. In general, the relay node placement problems aim to meet
certain connectivity and/or survivabil- ity requirements of the network by deploying a
minimum number of relay nodes. The other approach is grouping sensor nodes into
clusters with each cluster having a cluster head (CH). The CH nodes aggregate the
data and transmit them to the base station (BS). These two approaches has been
widely adopted by the research community to satisfy the scala- bility objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments and hence are discussed here along with single hop and multi hop characteristic of sensor node
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
Data aggregation in wireless sensor network based on dynamic fuzzy clusteringcsandit
Wireless Sensor Networks (WSN) use a plurality of s
ensor nodes that unceasingly collected and
sent data from a specific area to a base station. C
luster based data aggregation is one of the
popular protocols in WSN. Clustering is an importan
t procedure for extending the network
lifetime in WSNs. Cluster Heads (CH) aggregate data
from relevant cluster nodes and send it to
the base station. A main challenge in WSNs is to se
lect suitable CHs. In another communication
protocol based on a tree construction, energy consu
mption is low because there are short paths
between the sensors. In this paper, we propose Dyna
mic Fuzzy Clustering (DFC) data
aggregation. The proposed method first uses fuzzy d
ecision making approach for the selection
of CHs and then a minimum spanning tree is construc
ted based on CHs. CHs are selected
efficiently and accurately. The combining clusterin
g and tree structure is reclaiming the
advantages of the previous structures. Our method i
s compared to Low Energy Adaptive
Clustering Hierarchy (LEACH), Cluster and Tree Dara
Aggregation (CTDA), Modified Cluster
based and Tree based Data Aggregation (MCTDA) and C
luster based and Tree based Power
Efficient Data Collection and Aggregation (CTPEDCA)
.Our method decreases energy
consumption of each node. In DFC data aggregation,
the node lifetime is increased and the
survival of the WSN is improved.
34 9141 it ns2-tentative route selection approach for edit septianIAESIJEECS
Wireless Sensor Networks (WSNs) assume a crucial part in the field of mechanization and control where detecting of data is the initial step before any automated job could be performed. So as to encourage such perpetual assignments with less vitality utilization proportion, clustering is consolidated everywhere to upgrade the system lifetime. Unequal Cluster-based Routing (UCR) [7] is a standout amongst the most productive answers for draw out the system lifetime and to take care of the hotspot issue that is generally found in equivalent clustering method. In this paper, we propose Tentative Route (TRS) Selection approach for irregular Clustered Wireless Sensor Networks that facilitates in decision an efficient next relay to send the data cumulative by Cluster Heads to the Base Station. Simulation analysis is achieved using the network simulator to demonstrate the effectiveness of the TRS method.
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
ENERGY OPTIMISATION SCHEMES FOR WIRELESS SENSOR NETWORKcscpconf
A sensor network is composed of a large number of sensor nodes, which are densely
deployed either inside the phenomenon or very close to it. Sensor nodes have
sensing, processing and transmitting capability . They however have limited energy
and measures need to be taken to make op- timum usage of their energy and save
them from task of only receiving and transmitting data without processing. Various
techniques for energy utilization optimisation have been proposed Ma jor players are
however clustering and relay node placement. In the research related to relay node
placement, it has been proposed to deploy some relay nodes such that the sensors
can transmit the sensed data to a nearby relay node, which in turn delivers the data
to the base stations. In general, the relay node placement problems aim to meet
certain connectivity and/or survivabil- ity requirements of the network by deploying a
minimum number of relay nodes. The other approach is grouping sensor nodes into
clusters with each cluster having a cluster head (CH). The CH nodes aggregate the
data and transmit them to the base station (BS). These two approaches has been
widely adopted by the research community to satisfy the scala- bility objective and generally achieve high energy efficiency and prolong network lifetime in large-scale WSN environments and hence are discussed here along with single hop and multi hop characteristic of sensor node
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
In the recent years, wireless sensor network (WSN) have witnessed increased interest in information gathering in applications such as combat field reconnaissance, security surveillance, environmental monitoring, patient health monitoring and so on. Thus, there is a need for scalable and energy-efficient routing, data gathering and aggregation protocols in these WSN environments. Various hierarchical
clustering Protocols have been proposed by authors for WSN to improve system stability, lifetime, and energy efficiency. Clustering involves grouping nodes into disjoint and non-overlapping clusters. In this paper we motivate the need for clustering. Secondly, we present general classification of published clustering schemes. Thirdly, we review some existing clustering algorithms proposed for WSNs; highlighting their objectives, features, and so on. Finally, we develop an Average Energy (AvE) prediction algorithm using exponential decay function y=Ae-ax+B. We then combine this function with the
probabilistic distributed LEACH of algorithm to determine suitable CHs. The combined algorithm was implemented on MATLAB simulator and tested for homogenous network. The result gathered from the simulation shows that the extended algorithm in homogenous network mode is able to achieve 39%
stability, 11% Average energy Dissipation per round and 40% Lifespan better than LEACH-Homo. This paper proposes a new direction in improving energy efficiency of WSN routing protocol, which is desirable in some critical WSN applications. .
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.
HERF: A Hybrid Energy Efficient Routing using a Fuzzy Method in Wireless Sens...ijdpsjournal
Wireless Sensor Network (WSN) is one of the major research areas in computer network field today.Data
dissemination is an important task performed by wireless sensor networks. The routing algorithms of this
network depend on a number of factors such as application areas, usage condition, power, aggregation
parameters. With respect to these factors, different algorithms are recommended. One of the most
important features of routing algorithms is their flexibility and ability to self-organize themselves
according to such parameters. The existence of flexibility in routing protocols can satisfy calls for on
demand and table driven methods. Switching between these two methods would be impossible except by
compatibility between nodes' and switcher. Energy is another significant factor in wireless sensor
networks due to limited battery power and their exchangeability. To arrive at a network with mentioned
features, we have proposed an algorithm for hybrid energy efficient routing in wireless sensor networks
which uses two algorithms, i.e. EF-Tree (Earliest-First Tree) and SID (Source-Initiated Dissemination) to
disseminate data, and employs a fuzzy method to choose cluster head, and to switch between two
methods, i.e. SID and EF-Tree. In this routing, the whole network is clustered and the appropriate clusterhead
is selected according to fuzzy variables. Then, analyzing the changes in fuzzy variables and If fuzzy,
then rule, one routing in EF-Tree or SID is chosen for information transmission. The results of
simulations indicate that HERF has improved energy efficiency.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
In wireless sensor networks (WSN), conserving energy and increasing lifetime of the network are a critical issue that has been addressed by substantial research works. The clustering technique has been proven particularly energy-efficient in WSN. The nodes form groups (clusters) that include one cluster head and member clusters. Cluster heads (CHs) are able to process, filter, gather the data sent by sensors
belonging to their cluster and send it to the base station. Many routing protocols which have been proposed are based on heterogeneity and use the clustering scheme such as SEP and DEEC. In this paper we introduce a new approach called WDDC in which cluster heads are chosen on the basis
of probability of ratio of residual energy and average energy of the network. It also takes into consideration distances between nodes and the base station to favor near nodes with more energy to be cluster heads. Furthermore, WDDC is dynamic; it divides network lifetime in two zones in which it changes its behavior. Simulation results show that our approach performs better than the other distributed clustering protocols such as SEP and DEEC in terms of energy efficiency and lifetime of the network.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Hierarchical Coordination for Data Gathering (HCDG) in Wireless Sensor NetworksCSCJournals
A wireless sensor network (WSN) consists of large number of sensor nodes where each node operates by a finite battery for sensing, computing, and performing wireless communication tasks. Energy aware routing and MAC protocols were proposed to prolong the lifetime of WSNs. MAC protocols reduce energy consumption by putting the nodes into sleep mode for a relatively longer period of time; thereby minimizing collisions and idle listening time. On the other hand, efficient energy aware routing is achieved by finding the best path from the sensor nodes to the Base Sta-tion (BS) where energy consumption is minimal. In almost all solutions there is always a tradeoff between power consumption and delay reduction. This paper presents an improved hierarchical coordination for data gathering (HCDG) routing schema for WSNs based on multi-level chains formation with data aggregation. Also, this paper provides an analytical model for energy consumption in WSN to compare the performance of our proposed HCDG schema with the near optimal energy reduction methodology, PEGASIS. Our results demonstrate that the proposed routing schema provides relatively lower energy consumption with minimum delay for large scale WSNs.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
A new methodology is developed to analyse existing water quality monitoring networks. This methodology
incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental
health risk, the value of information, and redundancy reduction. The work starts with a formulation of a
conceptual framework for groundwater quality monitoring to represent the methodology’s context. This
work presents the development of Bayesian techniques for the assessment of groundwater quality. The
primary aim is to develop a predictive model and a computer system to assess and predict the impact of
pollutants on the water column. The process of the analysis begins by postulating a model in light of all
available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior
distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield
the current knowledge represented by the posterior distribution of model parameters. This process of
updating information about the unknown model parameters is then repeated in a sequential manner as
more and more new information becomes available.
EDGE PRESERVATION OF ENHANCED FUZZY MEDIAN MEAN FILTER USING DECISION BASED M...ijsc
Image noise refers to random variations in the basic characteristics of image like brightness, intensity or
color difference. These variations are not present in the image which is captured but may occur due to
environmental conditions like sensor temperature or due to circuit of the scanner or other similar issues.
Basically noise means unwanted signals in the image. Various filters have been designed for removal of
almost all types of noise. It has been seen in most of the cases that as a result of high amount of filtering or
repetitive filtering of image for the removal of noise, edges of images mostly get distorted or smeared out. It
means that most of the filtering techniques lead to loss of fine edges of the images which needs to be
preserved in order to enhance the quality of image. This paper has focused on to improve the enhanced
fuzzy median mean filter so that fine edges get preserved in a better way. Experiments have been performed
in MATLAB. Comparative analysis have been done on the basis of PSNR, MSE, BER and RMSE and it has
shown that border correction applied on images improves the results of enhanced fuzzy median mean filter.
Kurumsal müşterilerimize ihtiyaçları doğrultusunda tasarımcı ağımızla özgün, trendlere uygun, kullanışlı hediyelik tasarım ürünler sunuyoruz. Bu çözümler ile kurumsal müşterilerimizin müşterileri, iş birlikçileri, tedarikçileri ve çalışanları ile ilişkilerini güçlendirmesini sağlıyoruz.
DISSECT AND ENRICH DIVIDE AND RULE SCHEME FOR WIRELESS SENSOR NETWORK TO SOLV...ijsc
In remote sensor system, sensor hubs have the restricted battery power, so to use the vitality in a more
productive manner a few creator's created a few strategies, yet at the same time there is have to decrease
the vitality utilization of hubs. In this paper, we presented another method called as 'Partition and Rule
strategy' to unravel the scope dividing so as to open the system field into subfield and next maintain a
strategic distance from the vitality gap issue with the assistance of static bunching. Essentially, in gap and
run plan system range is isolated into three locales to be specific internal, center and external to conquer
the issue of vitality utilization. We execute this work in NS-2 and our recreation results demonstrate that
our system is far superior than old procedures.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
In the recent years, wireless sensor network (WSN) have witnessed increased interest in information gathering in applications such as combat field reconnaissance, security surveillance, environmental monitoring, patient health monitoring and so on. Thus, there is a need for scalable and energy-efficient routing, data gathering and aggregation protocols in these WSN environments. Various hierarchical
clustering Protocols have been proposed by authors for WSN to improve system stability, lifetime, and energy efficiency. Clustering involves grouping nodes into disjoint and non-overlapping clusters. In this paper we motivate the need for clustering. Secondly, we present general classification of published clustering schemes. Thirdly, we review some existing clustering algorithms proposed for WSNs; highlighting their objectives, features, and so on. Finally, we develop an Average Energy (AvE) prediction algorithm using exponential decay function y=Ae-ax+B. We then combine this function with the
probabilistic distributed LEACH of algorithm to determine suitable CHs. The combined algorithm was implemented on MATLAB simulator and tested for homogenous network. The result gathered from the simulation shows that the extended algorithm in homogenous network mode is able to achieve 39%
stability, 11% Average energy Dissipation per round and 40% Lifespan better than LEACH-Homo. This paper proposes a new direction in improving energy efficiency of WSN routing protocol, which is desirable in some critical WSN applications. .
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.
HERF: A Hybrid Energy Efficient Routing using a Fuzzy Method in Wireless Sens...ijdpsjournal
Wireless Sensor Network (WSN) is one of the major research areas in computer network field today.Data
dissemination is an important task performed by wireless sensor networks. The routing algorithms of this
network depend on a number of factors such as application areas, usage condition, power, aggregation
parameters. With respect to these factors, different algorithms are recommended. One of the most
important features of routing algorithms is their flexibility and ability to self-organize themselves
according to such parameters. The existence of flexibility in routing protocols can satisfy calls for on
demand and table driven methods. Switching between these two methods would be impossible except by
compatibility between nodes' and switcher. Energy is another significant factor in wireless sensor
networks due to limited battery power and their exchangeability. To arrive at a network with mentioned
features, we have proposed an algorithm for hybrid energy efficient routing in wireless sensor networks
which uses two algorithms, i.e. EF-Tree (Earliest-First Tree) and SID (Source-Initiated Dissemination) to
disseminate data, and employs a fuzzy method to choose cluster head, and to switch between two
methods, i.e. SID and EF-Tree. In this routing, the whole network is clustered and the appropriate clusterhead
is selected according to fuzzy variables. Then, analyzing the changes in fuzzy variables and If fuzzy,
then rule, one routing in EF-Tree or SID is chosen for information transmission. The results of
simulations indicate that HERF has improved energy efficiency.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
In wireless sensor networks (WSN), conserving energy and increasing lifetime of the network are a critical issue that has been addressed by substantial research works. The clustering technique has been proven particularly energy-efficient in WSN. The nodes form groups (clusters) that include one cluster head and member clusters. Cluster heads (CHs) are able to process, filter, gather the data sent by sensors
belonging to their cluster and send it to the base station. Many routing protocols which have been proposed are based on heterogeneity and use the clustering scheme such as SEP and DEEC. In this paper we introduce a new approach called WDDC in which cluster heads are chosen on the basis
of probability of ratio of residual energy and average energy of the network. It also takes into consideration distances between nodes and the base station to favor near nodes with more energy to be cluster heads. Furthermore, WDDC is dynamic; it divides network lifetime in two zones in which it changes its behavior. Simulation results show that our approach performs better than the other distributed clustering protocols such as SEP and DEEC in terms of energy efficiency and lifetime of the network.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Hierarchical Coordination for Data Gathering (HCDG) in Wireless Sensor NetworksCSCJournals
A wireless sensor network (WSN) consists of large number of sensor nodes where each node operates by a finite battery for sensing, computing, and performing wireless communication tasks. Energy aware routing and MAC protocols were proposed to prolong the lifetime of WSNs. MAC protocols reduce energy consumption by putting the nodes into sleep mode for a relatively longer period of time; thereby minimizing collisions and idle listening time. On the other hand, efficient energy aware routing is achieved by finding the best path from the sensor nodes to the Base Sta-tion (BS) where energy consumption is minimal. In almost all solutions there is always a tradeoff between power consumption and delay reduction. This paper presents an improved hierarchical coordination for data gathering (HCDG) routing schema for WSNs based on multi-level chains formation with data aggregation. Also, this paper provides an analytical model for energy consumption in WSN to compare the performance of our proposed HCDG schema with the near optimal energy reduction methodology, PEGASIS. Our results demonstrate that the proposed routing schema provides relatively lower energy consumption with minimum delay for large scale WSNs.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
A new methodology is developed to analyse existing water quality monitoring networks. This methodology
incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental
health risk, the value of information, and redundancy reduction. The work starts with a formulation of a
conceptual framework for groundwater quality monitoring to represent the methodology’s context. This
work presents the development of Bayesian techniques for the assessment of groundwater quality. The
primary aim is to develop a predictive model and a computer system to assess and predict the impact of
pollutants on the water column. The process of the analysis begins by postulating a model in light of all
available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior
distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield
the current knowledge represented by the posterior distribution of model parameters. This process of
updating information about the unknown model parameters is then repeated in a sequential manner as
more and more new information becomes available.
EDGE PRESERVATION OF ENHANCED FUZZY MEDIAN MEAN FILTER USING DECISION BASED M...ijsc
Image noise refers to random variations in the basic characteristics of image like brightness, intensity or
color difference. These variations are not present in the image which is captured but may occur due to
environmental conditions like sensor temperature or due to circuit of the scanner or other similar issues.
Basically noise means unwanted signals in the image. Various filters have been designed for removal of
almost all types of noise. It has been seen in most of the cases that as a result of high amount of filtering or
repetitive filtering of image for the removal of noise, edges of images mostly get distorted or smeared out. It
means that most of the filtering techniques lead to loss of fine edges of the images which needs to be
preserved in order to enhance the quality of image. This paper has focused on to improve the enhanced
fuzzy median mean filter so that fine edges get preserved in a better way. Experiments have been performed
in MATLAB. Comparative analysis have been done on the basis of PSNR, MSE, BER and RMSE and it has
shown that border correction applied on images improves the results of enhanced fuzzy median mean filter.
Kurumsal müşterilerimize ihtiyaçları doğrultusunda tasarımcı ağımızla özgün, trendlere uygun, kullanışlı hediyelik tasarım ürünler sunuyoruz. Bu çözümler ile kurumsal müşterilerimizin müşterileri, iş birlikçileri, tedarikçileri ve çalışanları ile ilişkilerini güçlendirmesini sağlıyoruz.
DISSECT AND ENRICH DIVIDE AND RULE SCHEME FOR WIRELESS SENSOR NETWORK TO SOLV...ijsc
In remote sensor system, sensor hubs have the restricted battery power, so to use the vitality in a more
productive manner a few creator's created a few strategies, yet at the same time there is have to decrease
the vitality utilization of hubs. In this paper, we presented another method called as 'Partition and Rule
strategy' to unravel the scope dividing so as to open the system field into subfield and next maintain a
strategic distance from the vitality gap issue with the assistance of static bunching. Essentially, in gap and
run plan system range is isolated into three locales to be specific internal, center and external to conquer
the issue of vitality utilization. We execute this work in NS-2 and our recreation results demonstrate that
our system is far superior than old procedures.
Power generation today is an increasingly demanding task, worldwide, because of emphasis on
efficient ways of generation. A power station is a complicated multivariable controlled plant, which
consists of boiler, turbine, generator, power network and loads. The power sector sustainability depends
on innovative technology and practices in maintaining unit performance, operation, flexibility and
availability . The demands being placed on Control & Instrumentation engineers include economic
optimization, practical methods for adaptive and learning control, software tools that place state-of-art
methods . As a result, Fuzzy techniques are explored which aim to exploit tolerance for imprecision,
uncertainty, and partial truth to achieve robustness, tractability, and low cost. This paper proposes use of
fuzzy techniques in two critical areas of Soot Blowing optimization and Drum Level Control.
Presently, in most of the Power stations the soot blowing is done based on a fixed time schedule. In many
instances, certain boiler stages are blown unnecessarily, resulting in efficiency loss. Therefore an fuzzy
based system is proposed which shall indicate individual section cleanliness to determine correct soot
blowing scheme. Practical soot blowing optimization improves boiler performance, reduces NOx emissions
and minimizes disturbances caused by soot blower activation. Due to the dynamic behaviour of power
plant, controlling the Drum Level is critical. If the level becomes too low, the boiler can run dry resulting
in mechanical damage of the drum and boiler tubes. If the level becomes too high, water can be carried
over into the Steam Turbine which shall result in catastrophic damage. Therefore an fuzzy based system is
proposed to replace the existing conventional controllers
Safety through design is a critical consideration in oven and furnace manufacturing. This slideshow takes a look at industry standards governing their design and identifies common safety oversights.
FACE SKETCH GENERATION USING EVOLUTIONARY COMPUTING ijsc
In this paper, an evolutionary genetic algorithm is used to generate face sketch from the face description. Face sketch generation without face image is extremely important for the law enforcement agencies. The genetic algorithm is used for generating face sketch through several iterations of the lgorithm. The face image description is captured through graphical user interface just by clicking options for each face
features. Face features are used to extract face images and generate initial population for the genetic algorithm. Genetic operators such as selection, crossover and mutation are used for next generation of the population. The Genetic algorithm cycle is repeated until the user is satisfied with face sketch generated. The novelty of the paper includes face sketch generation from face image description. The result shows that
evolutionary based technique for sketch generation produces the desired face sketch.
This work considers the multi-objective optimization problem constrained by a system of bipolar fuzzy relational equations with max-product composition. An integer optimization based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem. Some critical features associated with the feasible domain and optimal solutions of the bipolar max-Tp equation constrained optimization problem are studied. An illustrative example verifying the idea of this paper is included. This
is the first attempt to study the bipolar max-T equation constrained multi-objective optimization problems
from an integer programming viewpoint.
Some slip, trip and fall hazards are obvious, but many are
difficult to spot. Each of the following pictures have at least
one slipping or tripping hazard. Can you spot them all?
DATA AGGREGATION IN WIRELESS SENSOR NETWORK BASED ON DYNAMIC FUZZY CLUSTERING cscpconf
Wireless Sensor Networks (WSN) use a plurality of sensor nodes that unceasingly collected and
sent data from a specific area to a base station. Cluster based data aggregation is one of the
popular protocols in WSN. Clustering is an important procedure for extending the network
lifetime in WSNs. Cluster Heads (CH) aggregate data from relevant cluster nodes and send it to
the base station. A main challenge in WSNs is to select suitable CHs. In another communication
protocol based on a tree construction, energy consumption is low because there are short paths
between the sensors. In this paper, we propose Dynamic Fuzzy Clustering (DFC) data
aggregation. The proposed method first uses fuzzy decision making approach for the selection
of CHs and then a minimum spanning tree is constructed based on CHs. CHs are selected
efficiently and accurately. The combining clustering and tree structure is reclaiming the
advantages of the previous structures. Our method is compared to Low Energy Adaptive
Clustering Hierarchy (LEACH), Cluster and Tree Dara Aggregation (CTDA), Modified Cluster
based and Tree based Data Aggregation (MCTDA) and Cluster based and Tree based Power
Efficient Data Collection and Aggregation (CTPEDCA).Our method decreases energy
consumption of each node. In DFC data aggregation, the node lifetime is increased and the
survival of the WSN is improved.
CLUSTER HEAD SELECTION ALGORITHMS FOR ENHANCED ENERGY EFFICIENCY IN WIRELESS ...IJCSES Journal
The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network.
Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes,
and this has significant effects on the network's energy consumption. The objective of this paper is to
analyze existing cluster head selection algorithms and the parameters they implement to enhance energy
efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers
were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library,
Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster
Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These
algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more
parameters that can be used to elect cluster heads to improve on energy consumption
CLUSTER HEAD SELECTION ALGORITHMS FOR ENHANCED ENERGY EFFICIENCY IN WIRELESS ...IJCSES Journal
The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network.
Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes,
and this has significant effects on the network's energy consumption. The objective of this paper is to
analyze existing cluster head selection algorithms and the parameters they implement to enhance energy
efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers
were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library,
Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster
Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These
algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more
parameters that can be used to elect cluster heads to improve on energy consumption.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
The development of an energy-efficient routing protocol, capable of extending the life of the network, is one of the main constraints of wireless sensor networks (WSN). Research studies on WSN routing prove that clustering offers an effective approach to prolong the lifetime of a WSN, particularly when it is combined with multi-hop communication that can reduces energy costs by minimizing the distance between transmitter and receiver. Most clustering algorithms using multi-hop in data transmission encounter the hotspot problem. In this work, an Energy-efficient Multi-hop routing with Unequal Clustering approach (EMUC) is proposed, to create clusters of different sizes, which depend on the distance between the sensor node and the base station. Equilibrate the energy dissipation between the cluster heads is the purpose of this approach by adopting multi-hop communication to relay data to the base station. The implementation of multi-hop mode to transmit data to the base station reduces the energy cost of transmission over long distances. The effectiveness of this approach is validated through performed simulations, which prove that EMUC balances energy consumption between sensor nodes, mitigates the hotspots problem, saves more energy and significantly extends the network lifetime.
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc, decentralized manner. Although WSNs have gained in popularity, they still have several serious shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA, which impacted the improvement of network lifetime. In the second stage developed a novel model such as Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This approach considers increasing longevity but also raises the network's overall quality of service (QoS). In the analysis, the TCCS model is applied to both the centralized and distributed networks and compared with the existing methods. When compared to the previous methods, the simulation results show that the proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
ENERGY AWARE TALENTED CLUSTERING WITH COMPRESSIVE SENSING (TCCS) FOR WIRELESS...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather
information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc,
decentralized manner. Although WSNs have gained in popularity, they still have several serious
shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the
Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node
selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage
provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA,
which impacted the improvement of network lifetime. In the second stage developed a novel model such as
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This
approach considers increasing longevity but also raises the network's overall quality of service (QoS). In
the analysis, the TCCS model is applied to both the centralized and distributed networks and compared
with the existing methods. When compared to the previous methods, the simulation results show that the
proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93
percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum
network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
A CLUSTER-BASED ROUTING PROTOCOL AND FAULT DETECTION FOR WIRELESS SENSOR NETWORKIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be
deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a
consequence, the main goal is to reduce the overall energy consumption using clustering protocols which
have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and
routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend
the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices
and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated
widely and the results are compared with related works. The experimental results show that the proposed
algorithm provides an effective improvement in terms of energy consumption, data accuracy and network
lifetime
Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...ijsrd.com
Wireless sensor networks are widely considered as one of the most important technologies. The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Energy efficiency is thus a primary issue in maintaining the network. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks. This paper elaborates several techniques like LEACH, HEED, LEACH-B, PEACH, EEUC of cluster head selection for energy efficient in wireless sensor networks.
OPTIMIZED CLUSTER ESTABLISHMENT AND CLUSTER-HEAD SELECTION APPROACH IN WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
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.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Net...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
DATA GATHERING ALGORITHMS FOR WIRELESS SENSOR NETWORKS: A SURVEYijasuc
Recent developments in processor, memory and radio technology have enabled wireless sensor networks
which are deployed to collect useful information from an area of interest. The sensed data must be
gathered and transmitted to a base station where it is further processed for end-user queries. Since the
network consists of low-cost nodes with limited battery power, power efficient methods must be employed
for data gathering and aggregation in order to achieve long network lifetimes. In an environment where in
a round of communication each of the sensor nodes has data to send to a base station, it is important to
minimize the total energy consumed by the system in a round so that the system lifetime is maximized. With
the use of data fusion and aggregation techniques, while minimizing the total energy per round, if power
consumption per node can be balanced as well, a near optimal data gathering and routing scheme can be
achieved in terms of network lifetime. Several application specific sensor network data gathering protocols
have been proposed in research literatures. However, most of the proposed algorithms have been some
attention to the related network lifetime and saving energy are two critical issues for wireless sensor
networks. In this paper we have explored general network lifetime in wireless sensor networks and made an
extensive study to categorize available data gathering techniques and analyze possible network lifetime on
them.
ENERGY EFFICIENT DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKSijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor node creates challenges for routing in WSNs. The research work presents the organized and detailed description of energy conservation method for WSNs. In the proposed method reclustering and multihop data transmission processes are utilized for data reporting to base station by sensor node. The accurate use of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for longer times inside the cluster. The result shows that the proposed clustering approach has higher stable region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy Adaptive Clustering Hierarchy (LEACH) for WSNs.
SLGC: A New Cluster Routing Algorithm in Wireless Sensor Network for Decrease...IJCSEA Journal
Decrease energy consumption and maximizing network lifetime are important parameters in designing and protocols for wireless sensor network (WSN).Clustering is one of the efficient methods in energy consumption by Cluster-Head in WSN. Besides, CH can process and aggregate data sent by cluster members, thus reducing network traffic for sending data to sink. In this paper presents a new cluster routing algorithm by dividing network into grids. In each grid computes the center-gravity and threshold of energy for selecting the node that has the best condition base on these parameters in grid for selecting Cluster-Head in current round, also SLGC selecting Cluster-Heads for next rounds thereby this CHs reduce the volume of controlling messages for next rounds and inform nodes for sending data into CH of respective round. This algorithm prolong network lifetime and decrease energy consumption by selecting CH in grid and sending data of grid to sink by this CH. Result of simulation shows that SLGC algorithm in comparison with the previous clustering algorithm has maximizing network lifetime and decrease energy consumption in network.
Similar to FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK (20)
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Gopinath Rebala
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FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK
1. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
DOI:10.5121/ijsc.2016.7101 1
FUZZY-CLUSTERING BASED DATA GATHERING
IN WIRELESS SENSOR NETWORK
Arezoo Abasi and Hedieh Sajedi
Department of Mathematics, Statistics and Computer Science, College of Science,
University of Tehran, Tehran, Iran
ABSTRACT
Wireless Sensor Networks (WSN) is spatially distributed, collection of sensor nodes for the purpose of
monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a base station. The critical challenge is to minimize
the energy consumption in data gathering and forwarding from sensor nodes to the sink. Cluster based
data aggregation is one of the most popular communication protocols in this field. Clustering is an
important procedure for extending the network lifetime in wireless sensor networks. Cluster Heads (CH)
aggregate data from relevant cluster nodes and send it to the base station. A main challenge in WSNs is to
select suitable CHs. Another communication protocol is based on a tree construction. In this protocol,
energy consumption is low because there are short paths between the sensors. In this paper, Dynamic
Fuzzy Clustering data aggregation is introduced. This approach is based on clustering and minimum
spanning tree. The proposed method initially uses fuzzy decision making approach for the selection of CHs.
Afterward a minimum spanning tree is constructed based on CHs. CHs are selected efficiently and
accurately. The combining clustering and tree structure is reclaiming the advantages of the previous
structures. Our method is compared to the well-known data aggregation methods, in terms of energy
consumption and the amount of energy residuary in each sensor network lifetime. Our method decreases
energy consumption of each node. When the best CHs selected and the minimum spanning tree is formed by
the best CHs, the remaining energy of the nodes will be preserved. Node lifetime has an important role in
WSN. Using our proposed data aggregation algorithm, survival of the network is improved.
KEYWORDS
Sensor networks; Energy efficiency; Data aggregation; Fuzzy decision making.
1.INTRODUCTION
Wireless sensor network (WSN) is a collection of thousands of low-cost, low power
electronically programmable devices, which are deployed in a monitored area in stochastic
manner [2]. In this area, there is no opportunity for maintenance and battery replacement for the
most of the applications, which use the sensor nodes to surveillance the remote field [1]. Potential
applications of sensor networks include Industrial automation, Automated and smart homes,
Video surveillance, Traffic monitoring, Medical device monitoring, Monitoring of weather
conditions, Air traffic control, Robot control.
The most-distinguishing attributes of nodes used in WSNs are the limited power supply, storage
capacity and communication bandwidth required. In WSN, bandwidth utilization and energy
saving is a very important criterion for any existing and new applications. Normally, data
collected from WSNs are large which makes it essential to eliminate redundant data, minimize the
number of transmissions, and improve the energy consumption. The effort to reduce the number
of data packet transmission with the in-network processing is called data aggregation [3].
2. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
2
Our sensor's battery is limited. The lifetime on each node depends on the power that has
significantly affected the relationship between the nodes. One of the accurate requirements of
these nodes is the efficient use of the saved energy. Multiple algorithms have been designed for
impressive handling of nodes energy in WSNs using several clustering schemes [4, 5]. Optimal
data aggregation can save nodes energy. In this sensor network, data are gathered by the sensor
nodes from our study area. There is a data transmission method that merges data from several
sensor nodes into one pack, which is data aggregation. Decreasing the disjointed communication
at different levels and in turn to reduce the total energy consumption is the main aim of data
aggregation.
There are dissipated different amounts of energy to process raw data. There are two popular
protocols: Cluster based data aggregation [6] and Tree based data aggregation [7]. Some of WSNs
consists of clusters, in which each cluster has a CH. CHs have a significant impress in network
lifetime. An ideal CH is the one, which has the highest residual energy, maximum number of
neighbour nodes around the CH and the shortest distance from the base station[8]. Whatever the
selected CH is more similar to the ideal CH, network lifetime is increased.
We can use Multiple Attribute Decision Making (MADM) approach to select CHs with multi
criteria [9]. This method quantitatively selects alternatives based on their multiple criteria. The
main problem is the difficult estimation of the exact values of all the criteria. Synchronous
consideration of all criteria in CHs selections can be used MADM approach. In case of multi
criteria, fuzzy based MADM methodologies are efficient and impressive [10, 11].
In this paper, we proposed a hybrid approach called DFC data aggregation, which gathers and
combines data and avoids redundant data transformations, therefore successively reduces power
consumption and bandwidth.
Proposing DFC data aggregation, we preserve the advantages and minimize the disadvantages of
the clustering and tree based approaches. We use DFC data aggregation to extend the lifetime of
WSNs and energy consumption of sensor nodes. The optimized CHs are selected to spread energy
efficiently using multi criteria. CHs are selected based on the residual energy, the number of
neighbour nodes and distance from the base station. After cluster formation, CHs receive data
from member nodes in clusters, aggregate data and send it to the base station. A spanning tree
covers all the sides as vertices and consists no cycles. The tree is constructed in the procedure that
the node with the smallest identifier is chosen as the root [10, 12]. All the nodes with the shortest
path conjunct to the selected root. The protocol requires that each node exchange configuration
message in a specific format, which contains its own identifier, its chosen root, and the distance to
this selected root. Each node updates its configuration message upon identifying a root with a
smaller identifier or the shortest-path neighbour. In addition, the neighbour for which the shortest
route configuration message comes from is chosen as the parent of a node whenever it is detected.
In this paper, we employ multi criteria decision-making approach, Fuzzy Analytic Hierarchy
Process (FAHP) and hierarchical fuzzy in clusters on WSNs [13, 14]. The Analytic Hierarchy
Process (AHP) considers a set of assessment criteria, and a set of alternatives among which the
best decision is to be made. The AHP generates a weight for each evaluation criteria according to
the comparisons of the criteria. The superior the weight, the more significant the corresponding
criterion. The AHP method could improve the network lifetime significantly.
In this research, we also analyse other methods, including Low Energy Adaptive Clustering
Hierarchy (LEACH) [14], Tree Dara Aggregation (CTDA) [16], Modified Cluster based and Tree
based Data Aggregation (MCTDA) [16], and Cluster based and Tree based Power Efficient Data
3. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
3
Collection and Aggregation (CTPEDCA)[17]. We compared our proposed method with these
mentioned methods in terms of energy consumption and the amount of energy remaining in each
sensor network lifetime.
Simulation conclusions illustrate that our proposed approach is more efficient than LEACH,
CTDA MCTDA and CTPEDCA algorithms considering energy consumption.
The remainder of this paper is organized as follows. Section 2 is about previous work. In Section
3, we describe our system model. The proposed algorithm is explained in Section 4. Section 5
describes the experimental results and discussions and finally Section 6 concludes the paper.
2.PREVIOUS WORK
Energy efficiency is one of the key design requirements in battery-powered wireless sensor
networks. One important solution for it is to minimize the number of transmitted message in the
network [18].
Several works have been done on data aggregation in wireless sensor network that reduce the
power consumption. Clustering in WSNs is an effective procedure to decrease the energy
consumption of sensor nodes. In cluster based routing algorithms for wireless networks, LEACH
is famous because it is simple and efficient. In LEACH, CH nodes are selected randomly and all
the non-CH nodes are formed based on the received signal power from the CHs. In LEACH each
node can become a CH, there is no pattern in electing CHs and all nodes have the same chance to
be a CH, thus LEACH is not efficient. CHs are selected randomly and the energy is divided
between all the nodes equally. CHs aggregate all received data from all nodes in the clusters [15].
LEACH forms clusters based on the received signal strength and use the CHs as portals to the
sink. All the data processing like data fusion and aggregation are locally accomplished into the
cluster. CH is selected periodically among the nodes of the cluster. LEACH forms distributed
clusters, where nodes make independent decisions without any concentrated control. In LEACH,
each CH has a straight communicates with the base station no matter the distance is close or not.
When the network is massive, the communication between CHs and the base station consumes
much energy for the long distance transmission. In LEACH, the size of clusters can be increased
if the number of CHs is reduced. This makes induced excessive delays introduced by the number
of nodes in the same cluster [19, 20].
The work in [21] presents the hybrid approach for cluster-based aggregation, which adaptively
selects the appropriate data aggregation function. This paper shows an improvement in energy
consumption with the velocity of the target. Dynamic clustering shows better performance when
velocity of the target is high.
CTDA is a hybrid cluster and tree based algorithm and is proposed for data aggregation. It
employs a data aggregation mechanism in the CH to lessen the amount of data transmitted.
Therefore, CTDA decreases the energy dissipation in communication. CTDA decreases data
transfer volume so it enhances energy efficiency and attains the purpose of saving energy of the
sensor nodes. CTDA decreases the number of nodes, which directly send data to the base station.
In WSN with constrained energy, it is inefficient for sensors to select CHs randomly. CTDA
method does not perform any calculation in choosing the CHs and select CHs randomly. It is non-
optimal to selected CHs by chance because it imposes an additional burden to the network.
CTDA does not consider the amount of remaining energy in the nodes and it increases the wasted
energy and decreases the lifetime of the network [16].
4. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
4
In MCTDA method, minimum spanning tree does not do data aggregation and only data of CHs
by tree structure is sent to the base station [22].
CTPEDCA uses the full distribution in hierarchical WSNs. CTPEDCA is based on clustering and
Minimum Spanning Tree routing strategy for CHs and the time complexity is small. CTPEDCA
can balance the energy consumption of all the nodes, particularly the CH nodes in each round and
extend the lifetime of the networks. In each round, CTPEDCA allows only one CH communicate
directly to the base station. In CTPEDCA, a CH with the maximum remaining energy is selected
as the base, CH0. CH0 constructs a minimum spanning tree between all CHs and broadcasts tree
information for all the CHs. If the number of CH is K, K-1 CHs send data only to CH0 and CH0
transmit data to the base station. The disadvantage of this method is the network is dependent on
the CH0. CH0 is placed under pressure and needs a lot of energy. If CH0 is failed, the network
also failed. When the base station is too far, this method is not useful [17].
In WSN, improving the energy performance and maximizing the networking lifetime are the main
challenges. For this reason a hierarchical clustering scheme, called Location Energy Spectral
Cluster Algorithm (LESCA) is proposed in [23]. LESCA specifies the number of clusters in a
WSN automatically. It is based on spectral classification and considers the remaining energy and
some properties of nodes. LESCA uses the K-way algorithm and proposes new features of the
network nodes such as average energy, distance to the base station, and distance to cluster centers
in order to determine the clusters and to elect the cluster’s heads of a WSN. If the clusters are not
constructed in an optimal way and/or the number of the clusters is greater or less than the optimal
number of clusters, the total consumed energy of the sensor network per round is increased
exponentially.
3.MODEL DESCRIPTION AND ASSUMPTIONS
In this work, we consider a multi-hop WSN consisting of n stationary and location-aware sensor
nodes, denoted by {s1,s2,...,sn}, which are distributed randomly throughout an area. The network
contains the sink node denoted by s0 in a preassigned location that collects data from all sensor
nodes.
In our proposed study, we consider the following assumptions:
• All the nodes know their location and nodes are distributed randomly in the experimental
area.
• The base station has no energy constraint and is located at the top of the area.
• The initial number of CHs is constant and does not change over time.
The superiority of protocols is changed because there are different presumptions about the radio
features, such as energy dissipation in transmitter and receiver modes. In our plan, a simple model
is used for the radio energy dissipation, which is the transmitter, power amplifier, and receiver
dissipates energy to run the radio electronics [24]. The distance between the transmitter and the
receiver is used for the free space (d2 power loss) and the multipath fading (d4 power loss)
channel models.
In general, the free space (fs) model is used when the distance is less than a threshold d0 and if
more than the threshold d0, the multipath (mp) model is used [24].
5. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
5
Therefore, when n bit data message is transmitted over a distance d to achieve an acceptable
signal, the energy expended by the radio ETX can be expressed as Eq. (1).
(1)
where, εfs is the energy consumed by the amplifier to transmit at a shorter distance. εmp is the
energy consumed by the amplifier to transmit at a longer distance. Eelec is the energy dissipated in
the electronic circuit to transmit or receive the signal, which relied on agents such as the digital
coding, modulation, filtering and spreading of the signal. is the radio energy consumed to
receive this message, which is calculated by Eq.(2).
ERX (n) = n * Eelec (2)
4.DYNAMIC FUZZY CLUSTERING (DFC) DATA AGGREGATION
This paper proposes an algorithm for data aggregation called Dynamic Fuzzy Clustering (DFC)
data aggregation. DFC data aggregation uses the concepts of cluster and tree based algorithms.
The main idea of the cluster based routing is to lessen the amount of data transmission via engage
the data aggregation mechanism in the CH. DFC data aggregation decreases the energy
dissipation and saves the residual energy of the nodes. DFC data aggregation has three steps:
• CHs selection
• Cluster construction
• Tree formation of CHs
Our proposed method is inspired from two approaches named Pareto Optimal Solutions [25-26]
and Fuzzy TOPSIS. At the beginning of the network, we select CHs based on Fuzzy TOPSIS [8].
The clusters are formed based on the distance between nodes and CHs. Then, the tree is organized
due to CHs situation. This process continues until the first CH dies or the CH energy gets lower
than a defined threshold. In this case, we determine CHs based on Fuzzy TOPSIS again. We
determine the CHs based on maximizing the amount of energy efficiency. Although the initial
number of CHs is assumed constant, it can decrease clustering the performing of the algorithm.
The number of CHs is related to the several parameters, i.e., network topology, residual energy of
nodes, and the relative costs of calculation versus communication. The iteration of the mentioned
steps creates rounds in DFC algorithm.
In the sequel, we describe the steps of DFC data aggregation.
4.1.CH selection
Multi Criteria Decision Making (MCDM) techniques have been applied to quantitative decision-
making problems [27]. MCDM can be divided into two main categories. Multi-attribute decision-
making (MADM) approach [24] is one of the main categories of MCDM techniques. On the other
hand, multi objective decision-making (MODM) [25] is another main category in MCDM
techniques.
In this paper, we use MODM (Pareto optimal technique) and MADM (fuzzy TOPSIS) for
selecting CHs.
6. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
6
MODM (Pareto optimal technique)
The Pareto optimal solutions introduced by the economist Vilfredo Pareto [26]. Pareto
technique determines the solution space which solutions are non-dominated. Pareto solution
space specifies an area which comprising of all conceivable solutions in multi objective decision
making problems. The solution space is classified into three groups, namely, completely
dominated, neither dominant nor dominating and non-dominated.
MADM (fuzzy TOPSIS)
It is often difficult to determine the exact values of attributes of the sensor nodes [8]. Thus, we
use a fuzzy approach to determine the comparative significance of criteria instead of exact values.
In this algorithm, five fuzzy linguistic variables are considered. Figure 1 illustrates the fuzzy
triangular functions. The triangular membership functions are determined in Table 1.
Table1. Transformation of fuzzy triangular membership function
Rank Triangular membership
function
Very Weak
(VW)
(0.00, 0.10, 0.25)
Weak (W) (0.15, 0.30, 0.45)
Moderate (M) (0.35, 0.50, 0.65)
Strong (S) (0.55, 0.70, 0.85)
Very Strong
(VS)
(0.75, 0.90, 1.00)
Figure 1. Fuzzy triangular functions
TOPSIS approach has contributed to solving the decision-making problems. In fuzzy TOPSIS
approach, decision matrix has “m” alternatives and “n” attributes that could be assumed as a
problem of “n” dimensional hyperplane has “m” points whose location is given by the value of
7. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
7
their attributes [10]. i, j, respectively are and . The decision matrix is as
the following:
(3)
The weight of the th column of matrix A is shown as (4):
(4)
where and are fuzzy numbers. We have determined 0.5, 0.25, and 0.25 weights to the
remaining energy, number of neighbours, and distance from the sink s0, respectively. P is a fuzzy
decision matrix, which is normalized as the follow:
is the weighted, normalized fuzzy decision matrix.
(5)
In order to simplify the above matrix ( we summarize it as follows:
(6)
The best conceivable solution is the shortest distance from the ideal solution, and the worst
conceivable solution is the furthest distance from the ideal solution.
The best and the worst solutions are obtained from the weighted, normalized fuzzy decision
matrix given by (6). The Best Solutions are defined as and is an acronym for the Worst
Solutions:
(7)
The worst solutions are defined as:
(8)
We select a solution which is the nearest from the best conceivable solution and the furthest from
the worst ideal solution. The distances of each alternative from the best solution and the worst
solution are the separation measures. Distance of Best Solutions (DBS) and Distance of Worst
Solutions (DWS) are given as:
(9)
(10)
Rank indices of TOPSIS are estimated as:
(11)
8. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
8
Superior TOPSIS rank nodes are selected as the CHs. Each selected CH gets a unique identifier
(ID).
4.2.Cluster Construction
All the selected CHs disseminated identity message to non-CH nodes in the network. Each node
calculates the distance from all the CHs then joins to the cluster, which has the minimum distance
from its CH. K specifies the number of CHs. A distance matrix is used for re-clustering nodes
based on the distance to the selected CHs. The distance metric used here is the Euclidean metric.
The Euclidean distance between CH and a node is relying on their situations. Consider X and Y
are two nodes, i and j demonstrates two node locations. Euclidean distance is calculated based on
Eq. (12):
(12)
Each element in the distance matrix represents the difference between the CH and the node. After
cluster formation, each CH is accountable for gathering the data from all the nodes in the cluster.
When a framework (of data) from all the nodes in the cluster is consummated and aggregation is
performed, each CH dispatches the framework to the base station. The proceeds of re-
clustering and data transportation is continued for R rounds until all the nodes being dead. If the
number of nodes in the cluster gets smaller than the predefined threshold, the cluster is merged
with the neighbouring clusters.
4.3.Tree formation of CHs and Data transmission
After cluster formation, the CH sends message to all non-cluster nodes in wireless sensor network
which includes the CH ID, location, cluster size (for example the number of nodes in each
cluster), and remaining energy. CHs also send their data and location to the base station. Base
station prepares a minimum spanning tree based on the position of CH nodes so the minimum
spanning tree is between CH nodes and the base station. In this plan, CHs use free area channel
model to send data to the base station. In each round, the minimum distance from a vertex to
another vertex is chosen based on the location of CH nodes in the tree. Combining data from
several sensors used for removing the redundant transmission. Non CH nodes send their data by
the framework to the CH while they are in transmission mode, so data transmission is broken into
frameworks. Nodes could dispatch their data without any collision in the network. In this
research, we assumed that nodes are all the time synchronized by having the base station sent out
synchronization pulses to each node. When the CH receives the data from all the non CH nodes, it
performs data aggregation to produce a useful data message for sending to the base station. After
aggregating data, CHs transmit their resultant data along the tree (by the minimum spanning tree
between CH nodes). Finally, the base station receives the final resultant data. Non-CH nodes
could leave clusters when its energy is finished. If any non-CH node leaves, the related cluster
releases it. If CH node is dead or a new node is joined to the network, the CH selection algorithm
should be re-run.
In this paper, we consider two versions of DFC, named DFC-1 and DFC-2. In DFC-1, a node
consumes its finite energy budget during the algorithm. We consider a specific threshold in DFC-
2 for the CHs. When the amount of energy of a CH passes from the specified threshold, a new CH
is selected. In DFC-2, our threshold is achieved when the amount of energy of CH is reduced by
half.
9. International Journal on Soft Computing (IJSC) Vol.7, No. 1, February 2016
9
5.SIMULATION RESULTS AND ANALYSIS
In this section, we demonstrate detailed simulation experiments to evaluate the performance of
the proposed algorithm. We adopt MATLAB as the platform tool, which is popularly used in the
simulation experiments of wireless sensor networks [18].
In our proposed algorithm, the number of nodes is set to 100. The sink is situated far away from
the area. In Cluster based approach, we consider ten CHs (K=10) in the network, which divide the
nodes into ten clusters. We do an experiment in which different numbers of CHs are evaluated by
the three criteria. The numbers of studied CHs are 5, 8, 10, and 15. We have compared them in
remaining energy, energy consumption and the number of dead nodes. After R rounds, the most
optimal CHs have more remaining energy, minimize the amount of consumed energy and the
number of dead nodes. We set R=140. In Figure 2, experimental results show that K CHs are the
most optimal conditions in comparison with other CHs. The selected optimal CHs have the lowest
wasted energy and dead nodes, these CHs can keep more energy.
Figure 2. The effect of number of clusters in the DFC based on the number of dead nodes and used energy
and remaining energy at round 140.
For selecting the best CHs, we have used Pareto optimal solution. Pareto optimal CHs are
considered three criteria containing the remaining energy of the node, the minimum distance from
the sink, and the number of adjacent nodes. Our properties of the criteria are normalized in the
range [0, 1].
Membership function is applicable for converting the quantities into linguistic variables,
afterward variables are converted into a fuzzy triangular membership function. We specify the
fuzzy best solutions and fuzzy worst solutions. According to these quantities, we calculate the
separation rate and rating indices for the selecting node. The lifetime of the network is extended
in the period of the number of cycles until the first node in the network runs out of its complete
energy. CHs are chosen for each node until all the nodes expand their whole energy. In a Tree
based data aggregation approach, an aggregated tree is constructed based on a minimum spanning
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tree which source nodes are thought out as leaves, so data are forwarded by the parent node for
each node. The tree-based procedure has a low distance between each node and its parents,
thereby wasted energy is diminished. Nevertheless, the depth of the tree is high. This hybrid
method uses the advantages of the clustering and the tree structures while minimizing the
disadvantages of them. A comparison of our proposed method against LEACH, CTDA, and
CTPEDCA is represented that the present protocol is more effective than other mentioned
methods in WSNs. We use four different routing protocols: LEACH protocol, CTDA algorithm,
MCTDA algorithm, and our propounded method DFC algorithm for evaluating the performance
of our proposed protocol with the rests. The simulation conclusions show the proposed approach
has better efficiency than LEACH, CTDA, and CTPEDCA. We use a uniform simulation
environment to facilitate comparison of energy savings and consume energy between protocols.
Hundred sensor nodes are randomly spread in an area, which are shown in Figure 3 with coloured
dots. The base station is placed far away from the area, at coordinates (50,200) which is shown in
Figure 3 with an orange diamond.
Figure 3. A simulated wireless sensor network with nodes and a base station at the top.
In Table 2, the parameters used to develop the simulation in our experiments are listed.
Table 2. Simulation parameters
Parameter Value
Network Size 100 × 100 m
Number of the
nodes
100
Eelec 50nJ / bit
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εfs 10 pJ/bit/m2
εmp 0.0013 pJ/bit/m4
BS location (50,200)
EDA (data
aggregation)
5nJ / bit / signal
Control Packet size 800
Data Packet size 4000
R 140
K 10
A node is considered "dead" when it spent all its energy in the transferring process and unable to
send and receive the data. The simulation results of dead nodes are shown in Figure 4.
Figure 4. The number of dead nodes during the simulation.
Although the number of dead nodes in CTDA is low, but CTDA has many disadvantages. CTDA
selects CHs randomly and it does not have any calculations to select the CHs. CTDA may select a
CH with very low energy or choose a CH with the least number of neighbours. The number of
dead nodes in DFC-1 and DFC-2 is less than LEACH and MCTDA. This pros is because of the
CHs are calculated and elected based on three criteria the remaining energy, distance to the base
station and the number of neighbours around.
According to the short distance between nodes in the proposed approach, network lifetime is
increased. Furthermore, to decrease node solubility, DFC-1 and DFC-2 algorithms are more
energy efficient all over the simulation. In DFC-2, we define a threshold for the amount of energy
in CH, when the node's energy is less than the threshold, the new CH is replaced.
The simulation results of residual energy are illustrated in Figure 5. Our findings show that the
remaining energy is increased. Choosing the correct CH in the proposed method makes shorter
distance between nodes. Nodes are selected as CHs, which have the largest number of
neighbours. Thus, less energy are wasted so each node can hold more energy. Energy
consumption of the nodes is reduced.
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Figure 5. Remaining Energy of the nodes.
Figure 6 demonstrates the total dissipated energy by using LEACH, CTPEDCA, CTDA, and the
proposed algorithm during the network simulation.
According to the results in Figure 6, CHs with the highest rank send data to the base station,
which decrease the total energy consumption. In LEACH method, usually CHs attempt to send
data to the base station due to the great distance through a multipath expunction channel model
and consumption of energy is high. In MCTDA, accumulate data does not operate between the
nodes in the tree and all data packets of CHs are sent to the base station, so energy consumption is
high.
In DFC method, the amount of energy consumption is less because the CHs are selected intently.
The minimum spanning tree constructed by elite CHs, for this reason DFC saves more energy
than other mentioned algorithms.
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Figure 6. Total Consumed Energy.
6.CONCLUSIONS AND FUTURE WORKS
A fundamental challenge in the design of WSNs is the proper utilization of resources that are
scarce. In this paper, we employ Fuzzy TOPSIS method for finding the best CHs in WSNs. Three
criteria contain remaining energy, distance of the nodes from the base station and the number of
neighbour nodes. These criteria are discussed in order to optimize the number of CHs. The tree-
based method constructs a minimum spanning tree distance between CHs and the base station,
which lead to decreasing energy dissipation. We proposed an energy effective algorithm in this
paper, called DFC. DFC is a cluster and tree based data aggregation. Our proposed algorithm is
compared with LEACH, CTDA and MCTDA protocols. The conclusions of this simulation
demonstrate that the DFC is a considerable energy saving node which increase the network
lifetime compared to the above- mentioned protocols.In the future, we will work on the extension
of the DFC for mobility and heterogeneity of both nodes and sink.
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AUTHORS
Arezoo Abasi was born in Tehran, Iran in 1994. She studies Computer Science at University of Tehran
since 2012. She was awarded Khaje Nasir price in 2010. Her interests include artificial intelligence, soft
computing and wireless sensor network.
Hedieh Sajedi received a B.Sc. degree in Computer Engineering from Poly Technique University of
Technology in 2003, and M.Sc. and Ph.D degrees in Computer Engineering (Artificial Intelligence) from
Sharif University of Technology, in 2006 and 2010, respectively. She is currently an Assistant Professor at
the Department of Computer Science, University of Tehran, Iran. Her research interests include Computer
Networks, Machine Learning, and Signal Processing.