Research Article
Adaptive Hierarchical Network Structures for Wireless Sensor Networks
@INPROCEEDINGS{10.1007/978-3-642-29096-1_5, author={Dimitrios Amaxilatis and Ioannis Chatzigiannakis and Shlomi Dolev and Christos Koninis and Apostolos Pyrgelis and Paul Spirakis}, title={Adaptive Hierarchical Network Structures for Wireless Sensor Networks}, proceedings={Ad Hoc Networks. Third International ICST Conference, ADHOCNETS 2011, Paris, France, September 21-23, 2011, Revised Selected Papers}, proceedings_a={ADHOCNETS}, year={2012}, month={5}, keywords={Algorithm Engineering Clustering Self-Stabilization Implementation Protocols Software Design Cross-layer Cross-platform}, doi={10.1007/978-3-642-29096-1_5} }- Dimitrios Amaxilatis
Ioannis Chatzigiannakis
Shlomi Dolev
Christos Koninis
Apostolos Pyrgelis
Paul Spirakis
Year: 2012
Adaptive Hierarchical Network Structures for Wireless Sensor Networks
ADHOCNETS
Springer
DOI: 10.1007/978-3-642-29096-1_5
Abstract
Clustering is a crucial network design approach to enable large-scale wireless sensor networks (WSNs) deployments. A large variety of clustering approaches has been presented focusing on various aspect such as minimizing communication overhead, controlling the network topology etc. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like ideal wireless communication channels and perfect energy consumption estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper, we design a new clustering protocol that adapts to the changes in the environment and the needs and goals of the user applications. We provide a protocol that is deployable protocol in real WSNs. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the clustering problem, regarding adapting to environmental conditions and the quality of topology control. Our study clearly demonstrates the applicability of our approach and the benefits it offers to both research & development communities.
