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Integrated Divide and Conquer with Enhanced k-means technique for Energy-saving Data Aggregation in Wireless Sensor Networks | IEEE Conference Publication | IEEE Xplore

Integrated Divide and Conquer with Enhanced k-means technique for Energy-saving Data Aggregation in Wireless Sensor Networks


Abstract:

In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the big data contributors due to the wide range of real-life applications th...Show More

Abstract:

In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the big data contributors due to the wide range of real-life applications that use this type of networks. The data volume increases in unexpected ratio. The dense WSN can lead to an increase in the redundant data in the gathered measures of the sensor node. Therefore, it is essential to apply energy-efficient data aggregation to remove the data redundancy and maintain a suitable rate of accuracy. This paper proposes an Integrated Divide and Conquer with Enhanced K-means technique (IDiCoEK) for energy-saving data aggregation in WSNs. The IDiCoEK aggregates the measures in two levels: the node and cluster head levels. A divide and conquer algorithm is applied at the sensor node to remove the redundant data from the collected measures and then send it to the cluster head. The cluster head applies an enhanced K-means approach for clustering the received data sets from the sensor nodes into groups of near similar sets and then the best representative set will be sent to the base station from each group. The IDiCoEK performance is assessed using OMNeT++ network simulator with real data readings of sensor nodes. Results demonstrate that our IDiCoEK technique can save energy by decreasing the measures sent to the sink whilst conserving a suitable level of data accuracy at the sink node.
Date of Conference: 24-28 June 2019
Date Added to IEEE Xplore: 22 July 2019
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Conference Location: Tangier, Morocco

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