Improving K-Means Algorithm by Grid-Density Clustering for Distributed WSN Data Stream
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 11
Abstract
At recent years, Wireless Sensor Networks (WSNs) had a widespread range of applications in many fields related to military surveillance, monitoring health, observing habitat and so on. WSNs contain individual nodes that interact with the environment by sensing and processing physical parameters. Sometimes, sensor nodes generate a big amount of sequential tuple-oriented and small data that is called Data Streams. Data streams usually are huge data that arrive online, flowing rapidly in a very high speed, unlimited and can’t be controlled orderly during arrival. Due to WSN limitations, some challenges are faced and need to be solved. Extending network lifetime and reducing energy consumption are main challenges that could be solved by Data Mining techniques. Clustering is a common data mining technique that effectively organizes WSNs structure. It has proven its efficiency on network performance by extending network lifetime and saving energy of sensor nodes. This paper develops a grid-density clustering algorithm that enhances clustering in WSNs by combining grid and density techniques. The algorithm helps to face limitations found in WSNs that carry data streams. Grid-density algorithm is proposed based on the well-Known K-Means clustering algorithm to enhance it. By using Matlab, the grid-density clustering algorithm is compared with K-Means algorithm. The simulation results prove that the grid-density algorithm outperforms K-Means by 15% in network lifetime and by 13% in energy consumption.
Authors and Affiliations
Yassmeen Alghamdi, Manal Abdullah
Keywords
Related Articles
- EP ID EP417752
- DOI 10.14569/IJACSA.2018.091181
- Views 114
- Downloads 0
A New Algorithm for Post-Processing Covering Arrays
Software testing is a critical component of modern software development. For this reason, it has been one of the most active research topics for several years, resulting in many different algorithms, methodologies and to...
Predictive Method for Service Composition in Heterogeneous Environments within Client Requirements
Cloud computing is a new delivery model for Information Technology services. Many actors and parameters play an important role in provisioning of dynamically elastic and virtualized resources at the levels of infrastruct...
A Mobile-based Tremor Detector Application for Patients with Parkinson’s Disease
Parkinson’s disease affects millions of people worldwide and its frequency is steadily increasing. No cure is currently available for Parkinson’s disease patients, and most medications only treat the symptoms. This treat...
Introducing a Cybersecurity Mindset into Software Engineering Undergraduate Courses
Cybersecurity is a growing problem globally. Software helps to drive and optimize businesses in every aspect of modern life. Software systems have been under continued attacks by malicious entities, and in some cases, th...
Mind-Reading System - A Cutting-Edge Technology
In this paper, we describe a human-computer interface (HCI) system that includes an enabler for controlling gadgets based on signal analysis of brain activities transmitted from the enabler to the gadgets. The enabler is...