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.
Highlights
IntroductionA Wireless Sensor Networks (WSNs) is a specific kind of ad-hoc networks that is able to sense and process information
In recent years, a widespread use of Wireless Sensor Networks (WSNs) are found in various applications
Both algorithms in their experiment are streaming the same dataset stream packet with size 126 byte/message in a (1000×1000) m2 sensed area and Base Stations (BSs) located at the center, with node scattered randomly each with an initial energy equal to
Summary
A WSN is a specific kind of ad-hoc networks that is able to sense and process information. They can be used in many areas such as environmental, industrial, military, and agriculture fields. WSNs consist of built-in, independent and tiny equipment’s called sensor nodes. Sensor nodes contain four main components: energy source, processing unit, sensing [1] unit and transducer. The reports are transferred by sensor nodes and collected by special controllers called Base Stations (BSs). A WSN has many resource constraints including high computational power and limited energy source. WSNs depend on their nodes that consumes battery energy. The WSNs nature makes it difficult to recharge sensor nodes batteries. Energy efficiency is an important design objective in WSNs [2] and their algorithms should be precisely designed based on energy saving
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