@article{Al-Jamal2026, 
author = {Mohammad Q. Al-Jamal and Mahmoud Al Jamal and Bashar S. Khassawneh and Ayoub Alsarhan and Amina Salhi and Tahani Alsubait},
title = {A Bilevel Deep Learning Optimization Framework for Joint Energy Harvesting Prediction and Energy-Aware Scheduling in IoT-Based Wireless Sensor Networks},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {38},
keywords = {Internet of Things, wireless sensor networks, bilevel optimization, energy-aware scheduling},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.079984},
doi = {10.32604/cmc.2026.079984},
abstract = {Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to enable risk-sensitive control. At the upper level, a constrained, horizon-based scheduler selects per-node actions (duty cycle, sensing rate, transmission power) to extend network lifetime and balance residual energy while enforcing safety thresholds and operational bounds; bilevel coupling is realized via differentiable hypergradient updates, complemented by trust-region action smoothing and adaptive primal–dual constraint handling to suppress energy-critical states under uncertainty. On a real-world WSN energy–security dataset, the proposed model attains the best lower-level learning performance with  MAE=0.004,  RMSE=0.006, and  R2=0.995 for residual-energy regression, and up to  0.98 accuracy/ 0.98 F1 for secure-and-efficient classification. End-to-end scheduling results show that the full framework improves estimated network lifetime by up to  1.60×, reduces residual-energy variance to  0.60×, and lowers safety violations to  0.35× relative to a fixed-policy baseline, demonstrating robust, secure, and sustainable IoT-enabled WSN operation.}
}