DAHDA: Dynamic Adaptive Hierarchical Data Aggregation for Clustered Wireless Sensor Networks

DAHDA: Dynamic Adaptive Hierarchical Data Aggregation for Clustered Wireless Sensor Networks A Wireless Sensor Network (WSN) has gained a tremendous attention of researchers with its dynamic applications. The constant monitoring of critical scenarios has make WSNs an attractive choice for researchers at a large scale. The main objective is to increase the network lifetime for optimal and efficient utilization of resources in WSNs. For optimum functionality, various approaches have been proposed based upon clustering. Network lifetime is related with energy level of sensor nodes deployed in region of interest. As sensor nodes have limited lifetime, so there is a need to develop an algorithm for aggregating the sensors data in WSNs. A novel Dynamic Adaptive Hierarchical Data Aggregation (DAHDA) algorithm has been presented for evolving, uniform and non-uniform networks while maintaining the data accuracy. In addition, the algorithm is able to handle sudden bursts in the underlying data by recording the data in the area of interest for the whole event duration. The experimental evaluation on real and synthetic data shows that the algorithm performs well in terms of extending the lifetime of the network, maintaining the original distribution of the sensors as long as possible and maintaining the accuracy of the sensed data. DAHDA is an adaptive hierarchical aggregation algorithm for WSNs. The proposed algorithm has been simulated and its performance has been compared with the existing approaches in terms of residual energy, number of alive nodes, data accuracy, sudden burst detection, sensor distribution, lifetime of last node, first node and average lifetime of node for uniform, non-uniform and evolving networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

DAHDA: Dynamic Adaptive Hierarchical Data Aggregation for Clustered Wireless Sensor Networks

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4843-6
Publisher site
See Article on Publisher Site

Abstract

A Wireless Sensor Network (WSN) has gained a tremendous attention of researchers with its dynamic applications. The constant monitoring of critical scenarios has make WSNs an attractive choice for researchers at a large scale. The main objective is to increase the network lifetime for optimal and efficient utilization of resources in WSNs. For optimum functionality, various approaches have been proposed based upon clustering. Network lifetime is related with energy level of sensor nodes deployed in region of interest. As sensor nodes have limited lifetime, so there is a need to develop an algorithm for aggregating the sensors data in WSNs. A novel Dynamic Adaptive Hierarchical Data Aggregation (DAHDA) algorithm has been presented for evolving, uniform and non-uniform networks while maintaining the data accuracy. In addition, the algorithm is able to handle sudden bursts in the underlying data by recording the data in the area of interest for the whole event duration. The experimental evaluation on real and synthetic data shows that the algorithm performs well in terms of extending the lifetime of the network, maintaining the original distribution of the sensors as long as possible and maintaining the accuracy of the sensed data. DAHDA is an adaptive hierarchical aggregation algorithm for WSNs. The proposed algorithm has been simulated and its performance has been compared with the existing approaches in terms of residual energy, number of alive nodes, data accuracy, sudden burst detection, sensor distribution, lifetime of last node, first node and average lifetime of node for uniform, non-uniform and evolving networks.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Aug 16, 2017

References

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