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.

Original Source URL: https://aircconline.com/ijsc/V7N1/7116ijsc01.pdf

https://airccse.org/journal/ijsc/current2016.html

International Journal on Soft Computing ( IJSC )

 

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

http://airccse.org/journal/ijsc/ijsc.html

 

Scope & Topics

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field

Topics considered include but are not limited to: 

●        Fuzzy Systems ●        Neural Networks ●        Machine learning ●        Probabilistic Reasoning ●        Evolutionary Computing ●        Pattern recognition ●        Hybrid intelligent systems, ●        Software agents ●        Morphic Computing ●        Image processing, ●        E-commerce, e-medicine

●        Rough Sets ●        Symbolic machine learning, ●        Wavelet ●        Signal or Image Processing ●        Vision Recognition ●        Biomedical Engineering ●        Telecommunications ●        Reactive Distributed AI ●        Nano & Micro-systems ●        Data Visualization

Paper Submission

Authors are invited to submit papers for this journal through E-mail: ijsc@aircconline.com or through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

·         Submission Deadline : July 31, 2021

·         Notification                 : August 25, 2021

·         Final Manuscript Due : August 30, 2021

·         Publication Date          : Determined by the Editor-in-Chief

Contact Us

Here's where you can reach us: ijscjournal@yahoo.com or ijsc@aircconline.com Submission System 

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Google scholar link: https://scholar.google.co.in/citations?user=4BpotHUAAAAJ&hl=en

A Review on Text Mining in Data Mining

Yogapreethi.N1, Maheswari.S2

1M.E.Scholar, Department of Computer Science & Engineering, Nandha Engineering College, Erode-638052, Tamil Nadu, India

2Associate Professor, Department of Computer Science & Engineering, Nandha Engineering College, Erode-638052, Tamil Nadu, India

ABSTRACT

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 text into data for analysis. This survey is about the various techniques and algorithms used in text mining.

KEYWORDS

Data mining, Text mining, knowledge discovery

Original Source URL: https://aircconline.com/ijsc/V7N3/7316ijsc01.pdf

https://airccse.org/journal/ijsc/current2016.html