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

Abstract

Highlights What are the main findings? In HA-PM, the gateway fuzzy logic controller determines a nominal node-to-gateway reporting period based on the usable energy state of charge, observed network traffic, and current weather/control context. This enables node-side power-mode and task-scheduling decisions under local service and energy constraints. The node selects the power-mode and task-scheduling plan with the lowest estimated total node energy from a defined set of feasible plans that satisfy mandatory sensing, reporting, and timing requirements under the reporting calendar. Across the evaluated simulation scenarios, HA-PM improved supercapacitor-level operational continuity and adapted synchronization behavior under adverse energy-harvesting and traffic-load conditions. What are the implications of the main findings? Keeping full fuzzy inference at the gateway allows resource-constrained nodes to use adaptive reporting policies while retaining responsibility for command processing and local planning. Linking reporting policies with mandatory local service requirements offers a practical basis for energy-management design in energy-harvesting LPWAN IoT systems. 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.