Sustainable Data Energy and Resource Optimization Framework for Agricultural Consumer Electronics

Efficient resource utilization and energy management are essential for sustainable agricultural consumer electronics in modern IoT systems. Existing frameworks often struggle with adaptability, scalability, and energy efficiency, especially in dynamic and resource-constrained environments. This study aims to address these challenges by proposing the Sustainable Agricultural Optimization Framework (SAOF), which integrates quantum-inspired adaptive learning, bioinspired energy management, and swarm intelligence-based resource allocation. The framework’s Data Optimization Module uses quantum-inspired techniques, which use probabilistic principles to extract meaningful patterns from sparse datasets. The Energy Optimization Module employs adaptive modulation and fractal-based power scaling, a method that dynamically adjusts power levels based on self-repeating patterns in workload fluctuations. The Resource Management Module dynamically allocates tasks across edge-cloud layers using swarm intelligence. The simulations were conducted over 650 minutes and the results demonstrated that SAOF achieved a prediction accuracy of 96.4% and maintained an energy efficiency of 72.5% under stable conditions.