A scalable and dynamic data aggregation aware routing protocol for wireless sensor networks

  • Authors:

  • Leandro A. Villas;Daniel L. Guidoni;Regina B. Araújo;Azzedine Boukerche;Antonio A.F. Loureiro

  • Affiliations:

  • Federal University of Minas Gerais, Brazil, Brazil and University of Ottawa, Ottawa, Canada;Federal University of Minas Gerais, Belo Horizonte, Brazil and University of Ottawa, Ottawa, Canada;Federal University of São Carlos, São Carlos, Brazil;University of Ottawa, Ottawa, Canada;Federal University of Minas Gerais, Belo Horizonte, Brazil

  • Venue:
  • Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
  • Year:
  • 2010

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Abstract

Data aggregation plays an important role in energy constrained wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and consequently reducing communication costs. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSN. Although there are many proposed solutions to data aggregation in WSN, most of them build the data aggregation structure based on the order in which events occur. This kind of structure leads to low quality routing trees and does not address the load balancing problem, since the same tree is used throughout the network life. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, and selects routes with the highest aggregation rate. The routing tree created by DST does not depend on the order of events and is not held fixed along the occurrence of events. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate and quality of the routing tree. Results show that the routing tree built by DST provides the best aggregation quality compared with other algorithms outperforming them for different scenarios in all evaluations performed.