| Autor: |
Mohamad Abou Taam, Wathiq Laftah Al-Yaseen, Ali Kadhum Idrees, Oussama Zahwe |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
MENACOMM |
| DOI: |
10.1109/menacomm.2018.8371007 |
| Popis: |
One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wireless Sensor Networks (PWSNs) because of the widespread use of this type of networks in various real life applications. The amount of data clearly grows at an unexpected rate. The high-density deployment of the sensor nodes will lead to high data redundancy in the collected readings of the sensor nodes. An energy-saving data aggregation may be an essential way to remove the data redundancy. In this article, we propose a Distributed Data Aggregation based Modified K-means (DiDAMoK) Technique for enhancement the lifetime of the PWSNs. DiDAMoK is distributed inside each sensor node. It works into periods. Each period is composed of three stages. First, the sensor readings are collected and saved in the sensor node. Second, the modified K-means is employed on these readings to convert them into clusters of readings. The number of clusters is dynamic and depends on the nature of collected readings. In the third stage, One representative reading of each cluster will be transmitted to the sink. The performance of the DiDAMoK technique is evaluated using OMNeT++ network simulator and based on real sensed data of a WSN. Simulation results explain that our DiDAMoK technique can efficiently decrease the consumed energy of the whole PWSN due to reducing the sensed readings number transmitted to the sink node while keeping a suitable data accuracy at the sink. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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