Transformer-Enabled Digital Twin for Predictive Energy Aware Lifespan Enhancement in Wireless Sensor Networks
| International Journal of Electronics and Communication Engineering |
| © 2026 by SSRG - IJECE Journal |
| Volume 13 Issue 3 |
| Year of Publication : 2026 |
| Authors : Seethalakshmi Kathiresan, Palanisamy Vellaiyan |
10.14445/23488549/IJECE-V13I3P101
How to Cite?
Seethalakshmi Kathiresan, Palanisamy Vellaiyan, "Transformer-Enabled Digital Twin for Predictive Energy Aware Lifespan Enhancement in Wireless Sensor Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 13, no. 3, pp. 1-12, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P101
Abstract:
Wireless sensor networks are extensively employed for empirical and monitoring purposes. Their performance is severely constrained by limited node energy, making network lifetime maximization a critical challenge. Clustering-based communication has been extensively adopted to reduce energy consumption, yet most existing protocols rely on reactive decisions derived from instantaneous or historical network conditions, often leading to unbalanced energy depletion and premature node failures. To overcome this restriction, this study exploits a Transformer-Enabled Digital Twin (TEDT) framework for predictive energy-aware clustering in WSNs. The proposed approach maintains a virtual replica of the physical network to continuously model energy dynamics and traffic behavior. A Transformer neural network is employed to predict future residual energy of sensor nodes using historical energy sequences, enabling a predict–then–cluster strategy. Cluster head election is performed based on predicted power, communication distance, and node density, which ensures balanced energy utilization and reduced re-clustering overhead. Extensive simulations conducted and demonstrated that the proposed method achieves lower per-round energy consumption, delayed node death events, faster convergence, and significantly extended network lifetime in contrast to conventional clustering protocols involving LEACH, HEED, and DEEC. The results confirm the effectiveness of the proposed predictive clustering for sustainable WSN operation.
Keywords:
Wireless Sensor Networks, Digital Twin, Transformer model, Energy-aware clustering, Network lifetime, Predictive optimization.
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