Network Lifetime Enhancement by Employing Clustering and Sleep Cycle Scheduling Techniques with Ensemble SVM Learner and Crystal Algorithm

International Journal of Electrical and Electronics Engineering

© 2022 by SSRG - IJEEE Journal

Volume 9 Issue 12

Year of Publication : 2022

Authors : P. Vijitha Devi, K. Kavitha

10.14445/23488379/IJEEE-V9I12P103

How to Cite?

P. Vijitha Devi, K. Kavitha, "Network Lifetime Enhancement by Employing Clustering and Sleep Cycle Scheduling Techniques with Ensemble SVM Learner and Crystal Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 30-38, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P103

Abstract:

Clustering wireless sensor networks (WSNs) together is an excellent idea for improved data collection and extending the life of WSNs. When messages are sent among sensor nodes for periodic or sequential clustering, the sensor nodes become overwhelmed. During clustering, there is complex information sharing and instability in energy usage. The main requirements are efficient approaches for extending network lifetime and intra-cluster transmission increases. The main goal of this article is to reduce node energy loss while decreasing message transmission overhead. The network lifetime is increased by upgrading clusters. In this study, we proposed an ensemble SVM learner with a Crystal method to reduce data transmission while applying an appropriate sleep or active schedule to optimise individual sensor node energy consumption. The inputs of residual energy, cluster head distance to sink, and average data rates are applied to ensemble SVM learners with the Crystal algorithm to generate the outputs of both the sleep cycle and the cluster update cycle with the least amount of energy consumption. Based on the experimental investigation, the sensor network lifetime is enhanced, the energy utilisation of cluster heads is optimised, and the proposed method achieves good results than other state-of-art methods.

Keywords:

Wireless sensor network, Cluster head, Update cycle, Ensemble SVM learner, and Crystal algorithm.

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