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HALOGrid—HyperAdaptive long short term memory model with intelligent grid optimization
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DOI：10.1016/j.ijepes.2025.111327.png)
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Abstract
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• Lightweight LSTM Variant — designed with residual pathways and embedded attention for efficient temporal modeling on resource-constrained IoT devices. • Adaptive Hyperparameter Tuning — a module with drift detection that activates parameter recalibration only when performance degradation is detected, avoiding unnecessary computation. • Augmented Grid Search (AGS) Algorithm — a multi-stage optimization approach combining coarse-to-fine exploration, stochastic perturbations, and early- stopping heuristics to achieve fast convergence with minimal overhead. • Edge-Cloud Collaborative Deployment — ensures real-time inference at the edge while leveraging cloud resources for model refinement and secure update propagation.
Keywords:
Edge computing
IoT malware detection
Adaptive hyperparameter tuning
Long Short-Term Memory
Augmented grid search
Real-time inference
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