Title
An Analysis of Variance-Based Methods for Data Aggregation in Periodic Sensor Networks.
Abstract
Given the vast area to be covered and the random deployment of the sensors, wireless sensor networks WSNs require scalable architecture and management strategies. In addition, sensors are usually powered by small batteries which are not always practical to recharge or replace. Hence, designing an efficient architecture and data management strategy for the sensor network are important to extend its lifetime. In this paper, we propose energy efficient two-level data aggregation technique based on clustering architecture with which data is sent periodically from nodes to their appropriate Cluster-Heads CHs. The first level of data aggregation is applied at the node itself to eliminate redundancy from the collected raw data while the CH searches, at the second level, nodes that generate redundant data sets based on the variance study with three different Anova tests. Our proposed approach is validated via experiments on real sensor data and comparison with other existing data aggregation techniques.
Year
DOI
Venue
2015
10.1007/978-3-662-48567-5_6
Trans. Large-Scale Data- and Knowledge-Centered Systems
Keywords
DocType
Volume
Periodic Sensor Networks (PSNs), Data aggregation, Clustering architecture, Identical nodes behaviour, One way Anova model
Journal
22
ISSN
Citations 
PageRank 
0302-9743
2
0.39
References 
Authors
16
5
Name
Order
Citations
PageRank
Hassan Harb1303.10
Abdallah Makhoul220.39
David Laiymani320.39
Oussama Bazzi42914.86
Ali Jaber5366.21