Coordinated and adaptive information collecting in target tracking wireless sensor networks

•Download as DOCX, PDF•

0 likes•90 views

NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in Vijayangar

Engineering

Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Coordinated and Adaptive Information Collecting in
Target Tracking Wireless Sensor Networks
Abstract
Energy conservation has been known as the most important issue for many applications in
wireless sensor networks (WSNs). For data gathering in a clustered sensor network, most
existing work focuses on fusing data at cluster heads (CHs). However, in target tracking
applications, these approaches do not consider that one target is usually detected by sensor nodes
in multiple clusters, which leads to redundant data transmissions from CHs to the sink so as to
waste much energy. To solve it, this paper proposes a coordinated and adaptive information
collecting strategy (CAICS) for target tracking in clustered WSNs. In CAICS, a coordinated
node selection method considering the spatial correlation of sensing nodes is proposed to
dynamically select the best set of nodes for tracking missions. Moreover, an adaptive
aggregation node is selected for data collection to reduce the redundant data transmission and
balance the energy cost. Experimental results demonstrate that the proposed approach reduces
the computational complexity of node selections without degrading tracking accuracy and
achieves a significant energy consumption reduction and network lifetime extension compared
with the state-of-the-art approaches.

SYSTEM SPECIFICATION
Software Specification
Operating system : Linux (Ubuntu)
Software : Network Simulator 2.35
Programming languages : Tool Command Language, AWK, C++
Hardware Specification
Main processor : Dual Core
Hard disk capacity : 80GB
Cache memory : 2 GB
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper
Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement (Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (NearMaruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com

What's hot

Clustering in wireless sensor networks with compressive sensing

shivani Shivanichou1

IJCSIT

Poonam Debnath

The segmentation of 3 d images using the random walking technique on a random...

I3E Technologies

Energy efficient virtual machine (vm) migration in cloud data centers

Dinesh Raj Paneru

A0360109

iosrjournals

Principal component analysis (PCA) is a widespread and widely used in various areas of science such as bioinformatics, econometrics, and chemometrics among others. Once that PCA is based in the eigenvalues and the eigenvectors which are a very weak approach to high dimension systems with degrees of sparsity and in these situations the PCA is no longer a recommended procedure. Sparsity is very common in near infrared spectroscopy due to the large number of spectra required and the water absorption broad bands what makes these spectra very similar and with heavy sparsity in matrix dataset, demoting the precision and accuracy, in the multivariate modeling and within projections of data matrix in smaller dimensions. To overcoming these shortcomings the LASSO, a not PCA based method, model was applied to a NIR spectra dataset from Biodiesel and its performance was, statistically, compared with traditional multivariate modeling such as PCR and PLSR.

LASSO MODELING AS AN ALTERNATIVE TO PCA BASED MULTIVARIATE MODELS TO SYSTEM W...

mathsjournal

MACHINE LEARNING ON MAPREDUCE FRAMEWORK

Abhi Jit

Spectrum is a most expensive resource for wireless communication. We could not allocate individual spectrum to each user. Current spectrum measurement campaign shows that utilization of spectrum is very low or there has spectrum white space (Spectrum Holes) exists spatially or temporally. Therefore, we need to investigate that spectrum holes or white spaces for efficient spectrum utilization on Big Spectrum Data using Machine Learning techniques.

Distribution Modelling and Analytics of Large Spectrum Data: Spectrum Occupan...

Mitul Panchal

Optique presentation

DBOnto

High throughput multistandard transform core supporting mpeg h 264 vc 1 usin...

LogicMindtech Nologies

LASSO MODELING AS AN ALTERNATIVE TO PCA BASED MULTIVARIATE MODELS TO SYSTEM W...

mathsjournal

A rough set-based incremental approach for updating approximations under dyna...

Ecway Technologies

Cloud-based research collaboration platforms render scalable, secure and inventive environments that enabled academic and scientific researchers to share research data, applications and provide access to high- performance computing resources. Dynamic allocation of resources according to the unpredictable needs of applications used by researchers is a key challenge in collaborative research environments. We propose the design of Cloud Container based Collaborative Research (CCCORE) framework to address dynamic resource provisioning according to the variable workload of compute and data-intensive applications or analysis tools used by researchers. Our proposed approach relies on–demand, customized containerization and comprehensive assessment of resource requirements to achieve optimal resource allocation in a dynamic collaborative research environment. We propose algorithms for dynamic resource allocation problem in a collaborative research environment, which aim to minimize finish time, improve throughput and achieve optimal resource utilization by employing the underutilized residual resources.

CCCORE: Cloud Container for Collaborative Research

IJECEIAES

Online prediction of driver distraction based on brain activity patterns

I3E Technologies

International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education. International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.

A Novel Multi- Viewpoint based Similarity Measure for Document Clustering

IJMER

PNNL April 2011 ogce

marpierc

A modeling approach for cloud infrastructure planning considering dependabili...

ieeepondy

What's hot (17)

Clustering in wireless sensor networks with compressive sensing

IJCSIT

The segmentation of 3 d images using the random walking technique on a random...

Energy efficient virtual machine (vm) migration in cloud data centers

A0360109

LASSO MODELING AS AN ALTERNATIVE TO PCA BASED MULTIVARIATE MODELS TO SYSTEM W...

MACHINE LEARNING ON MAPREDUCE FRAMEWORK

Distribution Modelling and Analytics of Large Spectrum Data: Spectrum Occupan...

Optique presentation

High throughput multistandard transform core supporting mpeg h 264 vc 1 usin...

LASSO MODELING AS AN ALTERNATIVE TO PCA BASED MULTIVARIATE MODELS TO SYSTEM W...

A rough set-based incremental approach for updating approximations under dyna...

CCCORE: Cloud Container for Collaborative Research

Online prediction of driver distraction based on brain activity patterns

A Novel Multi- Viewpoint based Similarity Measure for Document Clustering

PNNL April 2011 ogce

A modeling approach for cloud infrastructure planning considering dependabili...

Viewers also liked

Bibliographic tools - Mendeley

Isaac Oteyo

Collecting, Managing, and Sharing Information and its Sources with Zotero

John Pell

Tryscience Grid For Kids 1

Tom Loughran

One hen

primarysource

Kojo's Academy Overview and Learning Goals

One Hen Inc

Collecting information

Manisha Verma

EndNote X2 Workshop for FASS Graduate Students

NUS Libraries

Kotler mm 14e_03_ippt_ge

Rizwanah Parwin

Collection development: selection Context, criteria on selection and electron...

Jennifer Laluna

Data Classification Presentation

Derroylo

Classification of data

Jay Pardasani

BOOK SECTECTION TOOLS Selection aids or tools provide necessary information about books, their contents, bibliographic characteristics, physical formats, possible uses and where they can be obtained. There is no single selection tool capable of per-forming all the functions of buying guide needed by the librarian. The librarian can have fingertip access to large sources of information. Selecting 2or3 tools appropriality There are three types of book section tools or aids, namely: (A) Per-publication Review Media (B) Post- publication Review Media (C) Review of learned periodicals and Specialised Journals (A) Pre- publication Review Media 1. Publisher’s Weekly. New York, R.R. Bowker Co. 1872 The weekly is standard American book trade journal. It is a valuable professional aid for selecting books in different types of libraries specially where there is urgency of purchasing popular books. The weekly mentions non-book materials only but they are not reviewed. Its scope and content is larger than bookseller. It is an essential reading for the librarians to get information about new books. Publisher’s weekly reviewed 3670 adult books and 520 juvenile works in 1976. Its circulation now, is over 32,000. 2. The New York Times Books Review (weekly). New York, the New York Times Co. 1896 It is the most popular and oldest reviewing periodical in the USA. Review of books for children and young adults regularly appear in each issue. Each issue lists best selling paperbacks. Fiction reviews are exhaustive and complete-reviews are written by experts. 3. Library Journal (semi-Monthly, Sep-jun: Monthly, July-August). New York , R.R.Bowker Co. 1876 Public libraries and academic libraries prefer this journal for selection of books. Professional libraries and teachers in library science write the reviews. The journal is primarily devoted to books and provides varying attention to other media also. Nearly 500 book reviews appear in a year. Reviews generally appear one or two months after the books are published. Reviews are arranged by broad subject headings. The arrangement is alphabetical by subect.

Book Selection Tools

tonyviamll89

Viewers also liked (12)

Bibliographic tools - Mendeley

Collecting, Managing, and Sharing Information and its Sources with Zotero

Tryscience Grid For Kids 1

One hen

Kojo's Academy Overview and Learning Goals

Collecting information

EndNote X2 Workshop for FASS Graduate Students

Kotler mm 14e_03_ippt_ge

Collection development: selection Context, criteria on selection and electron...

Data Classification Presentation

Classification of data

Book Selection Tools

Similar to Coordinated and adaptive information collecting in target tracking wireless sensor networks

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.

Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...

inventionjournals

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

ENERGY AWARE TALENTED CLUSTERING WITH COMPRESSIVE SENSING (TCCS) FOR WIRELESS...

IJCNCJournal

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.

Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...

ijsrd.com

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

FUZZY-CLUSTERING BASED DATA GATHERING IN WIRELESS SENSOR NETWORK

ijsc

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.

Efficient Cluster Based Data Collection Using Mobile Data Collector for Wirel...

ijceronline

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.

A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor Network

IJCNCJournal

A CLUSTER-BASED ROUTING PROTOCOL AND FAULT DETECTION FOR WIRELESS SENSOR NETWORK

IJCNCJournal

In recent years, WSNs have attracted significant attention due to the improvements in various sectors such as communication, electronics, and information technologies. When the clustering algorithm incorporates both Euclidean distance and energy, it automatically decreases the energy consumption. So, the major goal of this research is to reduce energy consumption for prolong the lifetime of the network. In order to achieve an energy-efficient process, Energy and Distance Aware Multi-Objective Golden Eagle Optimization (ED-MOGEO) is proposed in this research. In addition, this proposed solution reduces retransmissions and delays to improve the performance metrics. And so, this research carried out two major fitness functions (Euclidean distance and energy) for creating an energy-efficient WSN. Furthermore, energy consideration is used to reduce the nodes unavailability which results in packet loss during the transmission. For generating the routing path between the source and the Base Station (BS), the ED-MOGEO algorithm is used. From the simulation results, it shows that Proposed ED-MOGEO achieves better performances in terms of residual energy (14.36 J), end-to-end delay (12.9 ms), packet delivery ratio (0.994), normalized routing overhead (0.11), and throughput (1.131 Mbps) when compared to existing Cluster-Based Data Aggregation (CBDA) and Elephant Herding Optimization (EHO)-Greedy methods.

Cluster Based Routing using Energy and Distance Aware Multi-Objective Golden ...

IJCNCJournal

CLUSTER BASED ROUTING USING ENERGY AND DISTANCE AWARE MULTI-OBJECTIVE GOLDEN ...

IJCNCJournal

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 CLUSTERING

cscpconf

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.

Data aggregation in wireless sensor network based on dynamic fuzzy clustering

csandit

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. .

ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...

ijwmn

In wireless sensor network (WSN) there are two main problems in employing conventional compression techniques. The compression performance depends on the organization of the routes for a larger extent. The efficiency of an in-network data compression scheme is not solely determined by the compression ratio, but also depends on the computational and communication overheads. In Compressive Data Aggregation technique, data is gathered at some intermediate node where its size is reduced by applying compression technique without losing any information of complete data. In our previous work, we have developed an adaptive traffic aware aggregation technique in which the aggregation technique can be changed into structured and structure-free adaptively, depending on the load status of the traffic. In this paper, as an extension to our previous work, we provide a cost effective compressive data gathering technique to enhance the traffic load, by using structured data aggregation scheme. We also design a technique that effectively reduces the computation and communication costs involved in the compressive data gathering process. The use of compressive data gathering process provides a compressed sensor reading to reduce global data traffic and distributes energy consumption evenly to prolong the network lifetime. By simulation results, we show that our proposed technique improves the delivery ratio while reducing the energy and delay

A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...

ijasuc

Energy Efficiency and prolonged network lifetime are few of the major concern areas. Energy consumption rated of sensor nodes can be reduced in various ways. Data aggregation, result sharing and filtration of aggregated data among sensor nodes deployed in the unattended regions have been few of the most researched areas in the field of wireless sensor networks. While data aggregation is concerned with minimizing the information transfer from source to sink to reduce network traffic and removing congestion in network, result sharing focuses on sharing of information among agents pertinent to the tasks at hand and filtration of aggregated data so as to remove redundant information. There exist various algorithms for data aggregation and filtration using different mobile agents. In this proposed work same mobile agent is used to perform both tasks data aggregation and data filtration. This approach advocates the sharing of resources and reducing the energy consumption level of sensor nodes.

Mobile Agents based Energy Efficient Routing for Wireless Sensor Networks

Eswar Publications

pxc3903794

harpreet singh

A Wireless Sensor Network includes the distributed sensor nodes using limited energy, to monitor the physical environments and forward to the sink node. Energy is the major resource in WSN for increasing the network lifetime. Several works have been done in this field but the energy efficient data gathering is still not improved. In order to amend the data gathering with minimal energy consumption, an efficient technique called chaotic whale metaheuristic energy optimized data gathering (CWMEODG) is introduced. The mathematical model called Chaotic tent map is applied to the parameters used in the CWMEODG technique for finding the global optimum solution and fast convergence rate. Simulation of the proposed CWMEODG technique is performed with different parameters such as energy consumption, data packet delivery ratio, data packet loss ratio and delay with deference to dedicated quantity of sensor nodes and number of packets. The consequences discussion shows that the CWMEODG technique progresses the data gathering and network lifetime with minimum delay as well as packet loss than the state-of-the-art methods.

Energy efficient chaotic whale optimization technique for data gathering in w...

IJECEIAES

Energy efficiency has recently turned out to be primary issue in wireless sensor networks. Sensor networks are battery powered, therefore become dead after a certain period of time. Thus, improving the data dissipation in energy efficient way becomes more challenging problem in order to improve the lifetime for sensor devices. The clustering and tree based data aggregation for sensor networks can enhance the network lifetime of wireless sensor networks. Non-dominated Sorting Genetic Algorithm (NSGA) -III based energy efficient clustering and tree based routing protocol is proposed. Initially, clusters are formed on the basis of remaining energy, then, NSGA-III based data aggregation will come in action to improve the inter-cluster data aggregation further. Extensive analysis demonstrates that proposed protocol considerably enhances network lifetime over other techniques.

NSGA-III Based Energy Efficient Protocol for Wireless Sensor Networks

IJCSIS Research Publications

The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.

AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...

cscpconf

Similar to Coordinated and adaptive information collecting in target tracking wireless sensor networks (20)