Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Oct 12;22(20):7730.
doi: 10.3390/s22207730.

A Formal Energy Consumption Analysis to Secure Cluster-Based WSN: A Case Study of Multi-Hop Clustering Algorithm Based on Spectral Classification Using Lightweight Blockchain

Yves Frédéric Ebobissé Djéné  1   2 , Mohammed Sbai El Idrissi  3 , Pierre-Martin Tardif  4 , Ali Jorio  2 , Brahim El Bhiri  2 , Youssef Fakhri  1
Affiliations

A Formal Energy Consumption Analysis to Secure Cluster-Based WSN: A Case Study of Multi-Hop Clustering Algorithm Based on Spectral Classification Using Lightweight Blockchain

Yves Frédéric Ebobissé Djéné et al. Sensors (Basel). .

Abstract

Wireless Sensors Networks are integrating human daily life at a fast rate. Applications cover a wide range of fields, including home security, agriculture, climate change, fire prevention, and so on and so forth. If WSN were initially flat networks, hierarchical, or cluster-based networks have been introduced in order to achieve a better performance in terms of energy efficiency, topology management, delay minimization, load balancing, routing techniques, etc. As cluster-based algorithms proved to be efficient in terms of energy balancing, security has been of less importance in the field. Data shared by nodes in a WSN can be very sensitive depending on the field of application. Therefore, it is important to ensure security at various levels of WSN. This paper proposes a formal modeling of the energy consumed to secure communications in a cluster-based WSN in general. The concept is implemented using the Proof of Authentication (POAh) paradigm of blockchain and applied to a Multi-hop Clustering Algorithm based on spectral classification. The studied metrics are residual energy in the network, the number of alive nodes, first and last dead node.

Keywords: clustering; cryptography; energy; security; wireless sensors network.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Communication model in MHCA-SC.
Figure 2
Figure 2
Sample Packet.
Figure 3
Figure 3
300-Node model sample.
Figure 4
Figure 4
500-Node model sample.
Figure 5
Figure 5
500-Node Cluster sample.
Figure 6
Figure 6
Mean residual energy. (a) 300-node model. (b) 400-node model. (c) 500-node model. (d) Residual energy percentage.
Figure 7
Figure 7
Alive nodes. (a) 300-node model. (b) 400-node model. (c) 500-node model.
Figure 8
Figure 8
First dead node. (a) 300-node model. (b) 400-node model. (c) 500-node model.
Figure 9
Figure 9
FDN evolution. (a) 300-node model. (b) 400-node model. (c) 500-node model.
Figure 10
Figure 10
Last dead node. (a) 300-node model. (b) 400-node model. (c) 500-node model.
Figure 11
Figure 11
Residual energy: LESCA vs. SMHCA-CS vs. MHCA-CS (500 Nodes).
Figure 12
Figure 12
FDN: LESCA vs. SMHCA-CS vs. MHCA-CS (500 Nodes).
Figure 13
Figure 13
Alive nodes: LESCA vs. SMHCA-CS vs. MHCA-CS (500 nodes).
See this image and copyright information in PMC

References

    1. Zeb A., Islam A.K.M.M., Zareei M., Mamoon I.A., Mansoor N., Baharun S., Katayama Y., Komaki S. Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy. Int. J. Distrib. Sens. Netw. 2016;12:4979142. doi: 10.1177/155014774979142. - DOI
    1. Shahraki A., Taherkordi A., Haugen Ø., Eliassen F. Clustering Objectives in Wireless Sensor Networks: A Survey and Research Direction Analysis. Comput. Netw. 2020;180:107376. doi: 10.1016/j.comnet.2020.107376. - DOI
    1. Akila I.S., Manisekaran S.V., Venkatesan R. Modern Clustering Techniques in Wireless Sensor Networks. In: Sallis P., editor. Wireless Sensor Networks. Volume 6. IntechOpen; Rijeka, Hrvatska: 2017. pp. 121–130.
    1. Heinzelman W.R., Chandrakasan A., Balakrishnan H. Energy-Efficient Communication Protocol for Wireless Microsensor Networks; Proceedings of the 33rd Annual Hawaii International Conference on System Sciences; Maui, HI, USA. 4–7 January 2000; - DOI
    1. Bandyopadhyay S., Coyle E.J. An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks; Proceedings of the IEEE INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428); San Francisco, CA, USA. 30 March–3 April 2003; pp. 1713–1723.