Gateway-Driven Hybrid Adaptive Power Management for Energy-Harvesting LPWAN IoT Nodes Using Fuzzy Logic

Abstract

Low-Power Wide-Area Network (LPWAN)-based Internet of Things (IoT) deployments require energy management strategies that adapt to dynamic operating conditions without increasing the processing burden on energy-harvesting nodes. These nodes must coordinate data reporting with mandatory local services within a finite energy budget. Conventional fixed duty-cycling and synchronization policies do not jointly account for node energy availability, network traffic, and environmental factors influencing energy harvesting. This study presents hybrid adaptive power management (HA-PM), a framework combining gateway-based reporting-period adaptation with node-side scheduling and power-mode selection. A gateway fuzzy logic controller (FLC) evaluates supercapacitor state of charge (SoC), network traffic, and weather/control context to adjust the reporting period. The node selects the feasible plan with the lowest estimated total node energy from a finite catalog, subject to mandatory service and timing requirements. The reporting period specifies data-reporting requirements, while the local schedule determines available low-power intervals. Simulations compare HA-PM with fixed-period baselines across representative deployment, workload, harvesting, and adverse-condition scenarios. Results indicate that HA-PM reduces communication-related energy consumption, increases low-power-mode residency, and improves supercapacitor-level operational continuity under the evaluated conditions. Stress tests further demonstrate adaptive responses to adverse harvesting and traffic scenarios. These findings support HA-PM as a practical, context-aware energy management strategy for energy-harvesting LPWAN IoT applications, although the reported results are contingent on the specified simulation models, parameters, and operating scenarios.