Adaptive modeling algorithm for energy optimization in nanosensor networks for smart IoT systems

Efficient energy management in nanosensor networks remains a challenge in the design of intelligent IoT systems.This study proposed an adaptive modeling algorithm (NAMA) that dynamically adjusts the trade-off between energy consumption and stability in real-time nanosensor systems.The algorithm utilizes an energy-aware cost function combining total energy usage with a tunable parameter () that is adapted based on consumption thresholds.The research focused on a sensor network model distributed across the artificial skin of a robotic system, consisting of nanosensors operating in the terahertz (THz) band.The model incorporates integral metrics to evaluate transmission, reception, and sensing power costs, and applies adaptive control rules, such as transmission suppression or reactive cut-off, to minimize global energy usage.Simulation results demonstrate that NAMA achieves reduction in energy variance and over 11% extension of operational lifetime, compared to fixed-weight energy strategies, measured at the 80% cumulative energy threshold.Moreover, under a realistic energy-per-operation cost of 1 J, the adaptive algorithm enables the system to execute over 23,000 operations, with more than 26,000 operations remaining within a 50 mJ energy limit.This confirms the capability of the algorithm to efficiently manage energy distribution while preserving network longevity.The adaptive trade-off coefficient () responds effectively to environmental dynamics and energy saturation levels, maintaining network stability while optimizing power consumption.The proposed method contributes to the field by integrating control-theoretic adaptation with real-time nanosensor energy modeling, used Matlab/Python environment.It offers a practical and scalable solution for energy optimization in next-generation smart skin and embedded IoT systems.