Accessibility navigation

A green cluster-based routing scheme for large scale wireless sensor networks

Chanak, P., Banerjee, I. and Sherratt, R. S. ORCID: (2020) A green cluster-based routing scheme for large scale wireless sensor networks. International Journal of Communication Systems, 33 (9). e4375. ISSN 1099-1131

Text - Accepted Version
· Please see our End User Agreement before downloading.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/dac.4375


In Wireless Sensor Networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among Cluster Heads (CHs) and Cluster Member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering-based routing algorithms that can balance out the trade-off between load distribution and network lifetime “green cluster-based routing scheme”. This paper proposes a new energy aware green cluster-based routing algorithm to preventing premature death of large scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule-based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and the improved efficiency of our scheme.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Biomedical Sciences
Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:89029


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation