Genetic parser and deep residual bivariate Pascal-based energy-aware routing and scheduling for edge-enabled smart grids
- 1Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology
- 2Department of Advanced Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research
- 3Department of Information Technology, Vignan’s Foundation for Science, Technology and Research
Abstract
Edge-enabled smart grid communication systems operate under tight energy budgets and fluctuating traffic conditions, making it essential to use routing and scheduling methods that control energy use without compromising delay or reliability. Many existing approaches treat routing and scheduling as separate tasks, which often leads to poor coordination—especially when traffic surges unexpectedly or when edge devices face uneven energy availability. In this work, a unified solution is developed that couples a Genetic Parser, used to generate energy-aware routing and slot assignments, with a deep residual model that leverages Bivariate Pascal statistics to anticipate short-term traffic and energy behavior. The Genetic Parser selects paths that distribute load efficiently, while the predictive module estimates the likelihood of delay increases or energy spikes so the scheduler can adjust in advance. Tests carried out on representative smart grid scenarios show clear gains: energy use drops by roughly 18–24%, network lifetime increases by about 27%, and end-to-end latency falls by 15–20% when compared with established methods. Packet delivery also remains consistently high, reaching 98.4% even when traffic conditions vary rapidly. These results indicate that combining evolutionary search with lightweight predictive modeling can improve both stability and overall efficiency in next-generation smart grid communication networks.
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BibTeX
@article{nallakumar2026genetic,
title = {{Genetic parser and deep residual bivariate Pascal-based energy-aware routing and scheduling for edge-enabled smart grids}},
author = {R. Nallakumar and P. M. Benson Mansingh and A. Britto Manoj and P. Ramadoss},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62021-4},
url = {https://doi.org/10.1038/s41598-026-62021-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - Genetic parser and deep residual bivariate Pascal-based energy-aware routing and scheduling for edge-enabled smart grids
AU - R. Nallakumar
AU - P. M. Benson Mansingh
AU - A. Britto Manoj
AU - P. Ramadoss
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62021-4
UR - https://doi.org/10.1038/s41598-026-62021-4
AB - Edge-enabled smart grid communication systems operate under tight energy budgets and fluctuating traffic conditions, making it essential to use routing and scheduling methods that control energy use without compromising delay or reliability. Many existing approaches treat routing and scheduling as separate tasks, which often leads to poor coordination—especially when traffic surges unexpectedly or when edge devices face uneven energy availability. In this work, a unified solution is developed that couples a Genetic Parser, used to generate energy-aware routing and slot assignments, with a deep residual model that leverages Bivariate Pascal statistics to anticipate short-term traffic and energy behavior. The Genetic Parser selects paths that distribute load efficiently, while the predictive module estimates the likelihood of delay increases or energy spikes so the scheduler can adjust in advance. Tests carried out on representative smart grid scenarios show clear gains: energy use drops by roughly 18–24%, network lifetime increases by about 27%, and end-to-end latency falls by 15–20% when compared with established methods. Packet delivery also remains consistently high, reaching 98.4% even when traffic conditions vary rapidly. These results indicate that combining evolutionary search with lightweight predictive modeling can improve both stability and overall efficiency in next-generation smart grid communication networks.
SN - 2045-2322
ER -