Study on the Rough-set-based Clustering Algorithm for Sensor Networks

Liang, Fengmei; Zhang, Liyuan; Sun, Peng

doi:10.12928/eei.v3i2.272

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This new version provides advanced wi-fi modeling capabilities, improved database, and cyber attack analysis. Updated model includes 802.11p PHY and MAC Protocols known as WAVE [24].…”

Section: Network Simulator (Ns)mentioning

confidence: 99%

A comparative study of different network simulation tools and experimentation platforms for underwater communication

Murgod

Sundaram

2021

Bulletin EEI

View full text Add to dashboard Cite

Study of computer networks and their performance parameters like delay, bandwidth utilization, throughput, latency, jittering, and packet loss. have gained significant importance in the recent times. Simulation studies have been preferred for these parameters in computer networks, which in a real time is a difficult task. A network consists of many networking devices as gateways, routers, bridges, wireless access points and hub connected to it. To implement any new protocol or algorithm in a network is costlier and time consuming. Recently lot of research is going on underwater wireless sensor networks (UWSNs). Conducting real time experiments for underwater applications are overpriced, so as an alternative solution for this, we can conduct simulation studies to reduce the cost and quicken the research activities.In this paper we explore the different experimentation platforms and simulation tools available that help the network architects to develop new protocols or do changes to the existing protocol in a productive manner. We classify the tools based on various parameters and provide guidelines for researchers to choose a suitable platform based on their requirements.

show abstract

“…This new version provides advanced wi-fi modeling capabilities, improved database, and cyber attack analysis. Updated model includes 802.11p PHY and MAC Protocols known as WAVE [24].…”

Section: Network Simulator (Ns)mentioning

confidence: 99%

A comparative study of different network simulation tools and experimentation platforms for underwater communication

Murgod

Sundaram

2021

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…In this paper, from the impend approximation measurement of every condition attribute set in the decision table to the decision classification, one improved rule for measuring the importance of the attribute is described [16][17][18][19][20][21].…”

Section: The Principle For Finding the Classification Rulesmentioning

confidence: 99%

Data Mining Based on the Rough Analysis and the Application in the Telecommunication Network Quality Evaluation

Yu-juan¹,

Zhang²

2016

TELKOMNIKA

View full text Add to dashboard Cite

The precision of the classification rule is decided by the construction of the classification algorithm. In this paper, after the introducing of the concepts and the attribute-reduction algorithms of the basic rough set, a data-mining algorithm based on the rough impend approximation measurement is Keywords: data mining, rough set, classification approximation, telecommunication network quality evaluationCopyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionData Mining is concern with mining the valuable data from the massive data in the database to assist the decision-making. Presently, data mining is one of the research directions in the field of Information Decision and Artificial Intelligence. The Rough set method is a new information processing method [1,2], which is based on the classification of the equivalence relation. One of the characteristics of this method is, it is not needed to give the quantity description of some attributes in advance, rather finding out the internal rules of the problem directly from the description set of this problem [3,4]. It has great capability in analyzing the information system, which is incomplete, inaccuracy and includes noise.This Rough set is attended in the world because it has been successfully utilized in the field of Data Mining and Knowledge Discovering in database in recent years. There are three classification of attribute reduction based on rough set: (1) Attribute reduction algorithm based on discernibility matrix, reduction algorithm based on heuristic greedy, (2) Attribute reduction algorithm based on information indicated, and (3) Attribute reduction algorithm based on discernibility matrix was originally proposed by Skowron. In this algorithm, the problem of knowledge reduction of the decision table of information systems was converted to the problem of the reduction of discernibility matrix. Simple, easy to understand and achieve in practical application is the feature of the algorithm, while the disadvantage is that the algorithm reduction is inefficient and is a serious waste of space, not suitable for mass data.To address the above problem, various improved modifications of the difference matrix algorithm was got by reducing the time complexity of difference matrix reduction and many other ways by many scholars, making it a more efficient reduction algorithm. A property selection criteria was designed by greedy heuristic attribute reduction algorithm as the basis for selection of the current best attribute. Using heuristic ideas to reduce properties not only has the advantages in terms of time efficiency, and the final result is usually the best reduction or suboptimal, in fact, our needs in practical application can be satisfied by this result, so the heuristic algorithm is generally preferred as the attribute reduction.Rough set theory is generally indiscernible relationship-based, by introducing the approximation set and the lower approximation set, defined on the set operations, this is often

show abstract

“…The CH possesses numerous functions in addition to sensing the environment such as; data gathering from all cluster members, and its conveyance to the main node termed as Base Station (BS), the conveyance of other CHs data to the next hop, and the fusion of the cluster data. Clustering approach is the most popular energy efficient technique which provides various advantages such as prolonging the network lifetime, scalability and enabling less delay, where it is considered as an advantage for both the lifespan and the scalability of a network [4,5]. In general, clustering algorithms are signified as the compilation of unsupervised classification methods that assigns objects into groups, or the partitioning of datasets into subsets known as clusters.…”

Section: Introductionmentioning

confidence: 99%

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

View full text Add to dashboard Cite

The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

show abstract

Study on the Rough-set-based Clustering Algorithm for Sensor Networks

Cited by 6 publications

References 0 publications

A comparative study of different network simulation tools and experimentation platforms for underwater communication

A comparative study of different network simulation tools and experimentation platforms for underwater communication

Data Mining Based on the Rough Analysis and the Application in the Telecommunication Network Quality Evaluation

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Contact Info

Product

Resources

About