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JPN1406 Snapshot and Continuous Data Collection in Probabilistic Wireless S...chennaijp
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Efficient Cluster Based Data Collection Using Mobile Data Collector for Wirel...ijceronline
Establishing an efficient data gathering scheme in wireless sensor networks is a challenging task. Lot of researches has been carried out to establish energy efficient data gathering scheme to avoid heavy traffic received by the nodes near the sink. Data gathering scheme is a significant factor in determining the network lifetime. In this paper we propose an efficient data gathering scheme by introducing clustering and mobility into the wireless sensor network. We consider data collection in wireless sensor networks by utilizing mobile data collector and cluster heads. Cluster heads are chosen and clusters are formed to collect data from the sensor nodes. The proposed scheme finds the shortest tour for the mobile data collector to collect data from the cluster heads. The shortest tour saves time and energy in data gathering.
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Distribution of maximal clique size of theIJCNCJournal
Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the well-known Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of
the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category
of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the
vertices with respect to maximal clique size and node degree.
JPN1406 Snapshot and Continuous Data Collection in Probabilistic Wireless S...chennaijp
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Efficient Cluster Based Data Collection Using Mobile Data Collector for Wirel...ijceronline
Establishing an efficient data gathering scheme in wireless sensor networks is a challenging task. Lot of researches has been carried out to establish energy efficient data gathering scheme to avoid heavy traffic received by the nodes near the sink. Data gathering scheme is a significant factor in determining the network lifetime. In this paper we propose an efficient data gathering scheme by introducing clustering and mobility into the wireless sensor network. We consider data collection in wireless sensor networks by utilizing mobile data collector and cluster heads. Cluster heads are chosen and clusters are formed to collect data from the sensor nodes. The proposed scheme finds the shortest tour for the mobile data collector to collect data from the cluster heads. The shortest tour saves time and energy in data gathering.
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Distribution of maximal clique size of theIJCNCJournal
Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the well-known Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of
the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category
of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the
vertices with respect to maximal clique size and node degree.
Clustering Using Shared Reference Points Algorithm Based On a Sound Data ModelWaqas Tariq
A novel clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharing a common mutual nearest neighbor are considered as dense regions/ blocks. These blocks are the seeds from which clusters may grow up. Therefore, CSHARP is not a point-to-point clustering algorithm. Rather, it is a block-to-block clustering technique. Much of its advantages come from these facts: Noise points and outliers correspond to blocks of small sizes, and homogeneous blocks highly overlap. This technique is not prone to merge clusters of different densities or different homogeneity. The algorithm has been applied to a variety of low and high dimensional data sets with superior results over existing techniques such as DBScan, K-means, Chameleon, Mitosis and Spectral Clustering. The quality of its results as well as its time complexity, rank it at the front of these techniques.
Anchor Positioning using Sensor Transmission Range Based Clustering for Mobil...ijdmtaiir
In wireless sensor network, energy efficiency is a
major concern as the sensors have minimum energy capacity.
As the sensor energy consumption plays a vital role in
determining the network lifetime, many strategies have been
proposed for energy conservation. One of them is mobile data
gathering (MDG).In Mobile Data Gathering either the sink can
go on tour to collect the data or a Mobile Data Collector
(MDC) can collect the data from sensors and drops them back
to the static sink. In this paper, static sink with mobile data
collector is considered for the topic of interest. The mobile data
collector will reach the cluster of sensors at some appropriate
locations called as anchor points. For the formation of clusters,
many algorithms have been proposed so far, two of them ,
Square Grid based Clustering (SGC) and Sensor Transmission
based Clustering (STC) are analysed here for the selection of
anchors in the anchor based mobile data gathering approach
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...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.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Mobile data gathering with load balanced clustering and dual data uploading i...Shakas Technologies
In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer.
An Efficient top- k Query Processing in Distributed Wireless Sensor NetworksIJMER
Wireless Sensor Networks (WSNs) are usually defined as large-scale, ad-hoc, multi-hop and
wireless unpartitioned networks of homogeneous, small, static nodes deployed in an area of interest.
Applications of sensor networks include monitoring volcano activity, building structures or natural
habitat monitoring. In this paper, we present the problem of processing probabilistic top-k queries in a
distributed wireless sensor networks. The basic problem in top-k query processing is that, a single method
cannot be used as a solution to the problem of top-k query processing because there are many types of
top-k query processing. The method has to be based on the situation, the classification and the type of
database and the query model. Here we develop three algorithms, namely, sufficient set-based (SSB),
necessary set-based (NSB), and boundary-based (BB), for inter- cluster query processing with bounded
rounds of communications. Moreover, in responding to dynamic changes of data distribution in the
overall network, we develop an adaptive algorithm that dynamically switches among the three proposed
algorithms to minimize the transmission cost.
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.
Clustering Using Shared Reference Points Algorithm Based On a Sound Data ModelWaqas Tariq
A novel clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharing a common mutual nearest neighbor are considered as dense regions/ blocks. These blocks are the seeds from which clusters may grow up. Therefore, CSHARP is not a point-to-point clustering algorithm. Rather, it is a block-to-block clustering technique. Much of its advantages come from these facts: Noise points and outliers correspond to blocks of small sizes, and homogeneous blocks highly overlap. This technique is not prone to merge clusters of different densities or different homogeneity. The algorithm has been applied to a variety of low and high dimensional data sets with superior results over existing techniques such as DBScan, K-means, Chameleon, Mitosis and Spectral Clustering. The quality of its results as well as its time complexity, rank it at the front of these techniques.
Anchor Positioning using Sensor Transmission Range Based Clustering for Mobil...ijdmtaiir
In wireless sensor network, energy efficiency is a
major concern as the sensors have minimum energy capacity.
As the sensor energy consumption plays a vital role in
determining the network lifetime, many strategies have been
proposed for energy conservation. One of them is mobile data
gathering (MDG).In Mobile Data Gathering either the sink can
go on tour to collect the data or a Mobile Data Collector
(MDC) can collect the data from sensors and drops them back
to the static sink. In this paper, static sink with mobile data
collector is considered for the topic of interest. The mobile data
collector will reach the cluster of sensors at some appropriate
locations called as anchor points. For the formation of clusters,
many algorithms have been proposed so far, two of them ,
Square Grid based Clustering (SGC) and Sensor Transmission
based Clustering (STC) are analysed here for the selection of
anchors in the anchor based mobile data gathering approach
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...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.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Mobile data gathering with load balanced clustering and dual data uploading i...Shakas Technologies
In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer.
An Efficient top- k Query Processing in Distributed Wireless Sensor NetworksIJMER
Wireless Sensor Networks (WSNs) are usually defined as large-scale, ad-hoc, multi-hop and
wireless unpartitioned networks of homogeneous, small, static nodes deployed in an area of interest.
Applications of sensor networks include monitoring volcano activity, building structures or natural
habitat monitoring. In this paper, we present the problem of processing probabilistic top-k queries in a
distributed wireless sensor networks. The basic problem in top-k query processing is that, a single method
cannot be used as a solution to the problem of top-k query processing because there are many types of
top-k query processing. The method has to be based on the situation, the classification and the type of
database and the query model. Here we develop three algorithms, namely, sufficient set-based (SSB),
necessary set-based (NSB), and boundary-based (BB), for inter- cluster query processing with bounded
rounds of communications. Moreover, in responding to dynamic changes of data distribution in the
overall network, we develop an adaptive algorithm that dynamically switches among the three proposed
algorithms to minimize the transmission cost.
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.
Adaptive and Energy Efficient Compressive Sensing Clustering for Wireless Sen...paperpublications3
Abstract: Adaptive and energy efficient compressive sensing clustering for wireless sensor networks. In this paper mainly used the term CS that is compressive sensing. Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper used compressive sensing used for clustering method. The enhanced hybrid compressive sensing method of using CS was proposed to reduce the number of transmissions in sensor networks.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
ENERGY-EFFICIENT DATA COLLECTION IN CLUSTERED WIRELESS SENSOR NETWORKS EMPLOY...ijwmn
In this paper, a energy-efficient data collection method is proposed in which an integration between
Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is
exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be
transformed in DCT domain, we propose that each cluster from the networks only sends a small number of
large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either
directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the
large coefficients received at the BS. We further analyze and formulate the communication cost as the
power consumption for transmitting data in such networks based on stochastic problems. Some common
clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise
and noiseless environments for the proposed method are considered.
ENERGY-EFFICIENT DATA COLLECTION IN CLUSTERED WIRELESS SENSOR NETWORKS EMPLOY...ijwmn
In this paper, a energy-efficient data collection method is proposed in which an integration between
Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is
exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be
transformed in DCT domain, we propose that each cluster from the networks only sends a small number of
large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either
directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the
large coefficients received at the BS. We further analyze and formulate the communication cost as the
power consumption for transmitting data in such networks based on stochastic problems. Some common
clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise
and noiseless environments for the proposed method are considered.
ENERGY-EFFICIENT DATA COLLECTION IN CLUSTERED WIRELESS SENSOR NETWORKS EMPLOY...ijwmn
In this paper, a energy-efficient data collection method is proposed in which an integration between
Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is
exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be
transformed in DCT domain, we propose that each cluster from the networks only sends a small number of
large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either
directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the
large coefficients received at the BS. We further analyze and formulate the communication cost as the
power consumption for transmitting data in such networks based on stochastic problems. Some common
clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise
and noiseless environments for the proposed method are considered.
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.
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 Review of Atypical Hierarchical Routing Protocols for Wireless Sensor Networksiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A PROPOSAL FOR IMPROVE THE LIFETIME OF WIRELESS SENSOR NETWORKIJCNCJournal
In the Wireless Sensor Network (WSN), sensor nodes are connected together through radio frequency (RF).Routing protocol is used to transmit data among sensor nodes. In the paper, we proposed a new routing protocol based on LEACH protocol. This is energy-efficient clustering algorithm. The proposed protocol enlarges WSN life-time by considering remaining energy and distance from nodes to BS in the election of cluster head. Comparing the result simulation between LEACH and proposed protocol showed that proposed protocol will prolong the network life-time.
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.
Distributed processing of probabilistic top k queries in wireless sensor netw...IEEEFINALYEARPROJECTS
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JAVA 2013 IEEE DATAMINING PROJECT Distributed processing of probabilistic top...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Fuzzy-Clustering Based Data Gathering in Wireless Sensor Network ijsc
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.
FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK ijsc
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
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JPN1410 Secure and Efficient Data Transmission for Cluster-Based Wireless Se...chennaijp
Get the latest IEEE ns2 projects in JP INFOTECH; we are having following category wise projects like Industrial Informatics, Vehicular Technology, Networking, WSN and Manet.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/ns2-projects/
JPN1409 Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networkschennaijp
Get the latest IEEE ns2 projects in JP INFOTECH; we are having following category wise projects like Industrial Informatics, Vehicular Technology, Networking, WSN and Manet.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/ns2-projects/
JPN1408 Hop-by-Hop Message Authentication and Source Privacy in Wireless Sen...chennaijp
Get the latest IEEE ns2 projects in JP INFOTECH; we are having following category wise projects like Industrial Informatics, Vehicular Technology, Networking, WSN and Manet.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/ns2-projects/
JPN1405 RBTP: Low-Power Mobile Discovery Protocol through Recursive Binary T...chennaijp
Get the latest IEEE ns2 projects in JP INFOTECH; we are having following category wise projects like Industrial Informatics, Vehicular Technology, Networking, WSN and Manet.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/ns2-projects/
JPN1404 Optimal Multicast Capacity and Delay Tradeoffs in MANETschennaijp
Get the latest IEEE ns2 projects in JP INFOTECH; we are having following category wise projects like Industrial Informatics, Vehicular Technology, Networking, WSN and Manet.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/ns2-projects/
JPM1410 Images as Occlusions of Textures: A Framework for Segmentationchennaijp
JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/matlab-projects/
JPM1407 Exposing Digital Image Forgeries by Illumination Color Classificationchennaijp
JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/matlab-projects/
JPM1417 Characterness: An Indicator of Text in the Wildchennaijp
JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/matlab-projects/
JPM1416 A Unified Data Embedding and Scrambling Methodchennaijp
JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/matlab-projects/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
JPN1412 Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing
1. Transmission-Efficient Clustering Method for Wireless
Sensor Networks Using Compressive Sensing
ABSTRACT:
Compressive sensing (CS) can reduce the number of data transmissions and
balance the traffic load throughout networks. However, the total number of
transmissions for data collection by using pure CS is still large. The hybrid method
of using CS was proposed to reduce the number of transmissions in sensor
networks. However, the previous works use the CS method on routing trees. In this
paper, we propose a clustering method that uses hybrid CS for sensor networks.
The sensor nodes are organized into clusters. Within a cluster, nodes transmit data
to cluster head (CH) without using CS. CHs use CS to transmit data to sink. We
first propose an analytical model that studies the relationship between the size of
clusters and number of transmissions in the hybrid CS method, aiming at finding
the optimal size of clusters that can lead to minimum number of transmissions.
Then, we propose a centralized clustering algorithm based on the results obtained
from the analytical model. Finally, we present a distributed implementation of the
clustering method. Extensive simulations confirm that our method can reduce the
number of transmissions significantly.
2. EXISTING SYSTEM:
By using CS in data gathering, every node needs to transmit M packets for a set of
N data items. That is, the number of transmissions for collecting data from N nodes
is MN, which is still a large number.In the hybrid method, the nodes close to the
leaf nodes transmit the original data without using the CS method, but the nodes
close to the sink transmit data to sink by the CS technique.Applied hybrid CS in
the data collection and proposed an aggregation tree with minimum energy
consumption.
Clustering is a standard approach for achieving efficient and scalable performance
in wireless sensor networks. Traditionally, clustering algorithms aim at generating
a number of disjoint clusters that satisfy some criteria. Previously, a novel
clustering problem that aims at generating overlapping multi-hop clusters have
formulated. Overlapping clusters are useful in many sensor network applications,
including inter-cluster routing, node localization, and time synchronization
protocols. A randomized, distributed multi-hop clustering algorithm (KOCA) for
solving the overlapping clustering problem is also proposed. KOCA aims at
generating connected overlapping clusters that cover the entire sensor network
with a specific average overlapping degree. Through analysis and simulation
experiments we show how to select the different values of the parameters to
3. achieve the clustering process objectives. Moreover, the results show that KOCA
produces approximately equal-sized clusters, which allows distributing the load
evenly over different clusters. In addition, KOCA is scalable; the clustering
formation terminates in a constant time regardless of the network size.
DISADVANTAGES OF EXISTING SYSTEM:
Geographic locations and node distribution of the sensor nodes are Ignored.
The nodes close to the sink transmit data to sink
PROPOSED SYSTEM:
The two metrics to evaluate the performance of the clustering with hybrid
CS proposed in this paper: the number of transmissions which is required to
collect data from sensors to the sink, and the reduction ratio of transmissions
(reduction ratio for short) of our method compared with other methods.
Four other data collection methods are considered. In the clustering without
CS method, the same cluster structure to our method is used, but CS is not
4. used.In the shortest path tree (SPT) without CS, the shortest path tree is used
to collect data from sensors to the sink.
In the SPT with hybrid CS, the shortest path tree is used to collect data from
sensors to the sink, and CS is used in the nodes who has more than M
descendant nodes (including itself). In the optimal tree with hybrid CS, a
tree having minimum transmissions is used.
In this paper, we propose a clustering method that uses the hybrid CS for
sensor networks. The sensor nodes are organized into clusters. Within a
cluster, nodes transmit data to the cluster head (CH) without using CS. A
data gathering tree spanning all CHs is constructed to transmit data to the
sink by using the CS method. One important issue for the hybrid method is
to determine how big a cluster should be. If the cluster size is too big, the
number of transmissions required to collect data from sensor nodes within a
cluster to the CH will be very high. But if the cluster size is too small, the
number of clusters will be large and the data gathering tree for all CHs to
transmit their collected data to the sink will be large, which would lead to a
large number of transmissions by using the CS method.
5. In this regard, we first propose an analytical model to find the optimal size
of clusters that can lead to minimum number of transmissions. Then, we
propose a centralized clustering algorithm based on the results obtained from
the analytical model.
ADVANTAGES OF PROPOSED SYSTEM:
We use the CS method on the clustering in sensor networks. The clustering
method generally has better traffic load balancing than the tree data
gathering method. This is because the number of nodes in clusters can be
balanced when we divide clusters.
In addition, the previous works ignored the geographic locations and node
distribution of the sensor nodes.
While in sensor networks, the information of node distribution can help the
design of data gathering method that uses less data transmissions.
Reduction of Transmission Number.
6. SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
7. Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7/LINUX.
Implementation : NS2
NS2 Version : NS2.2.28
Front End : OTCL (Object Oriented Tool Command
Language)
Tool : Cygwin (To simulate in Windows OS)
REFERENCE:
Ruitao Xie and Xiaohua Jia, Fellow, IEEE, Computer Society, “Transmission-
Efficient Clustering Method for Wireless Sensor Networks Using Compressive
Sensing,” ,MARCH 2014.