Fuzzy Logic-inspired Dynamic Energy Optimization Mechanism for Resource-Constrained IoT Sensor Nodes with Renewable Power Sources

Energy sustainability remains a critical challenge in Internet of Things (IoT) networks, where sensor nodes are inherently constrained by limited battery capacity. Energy Harvesting (EH) has emerged as a viable solution, enabling sensor nodes to replenish power from ambient sources such as solar radiation and radio frequency (RF) signals. However, the stochastic and intermittent nature of harvested energy poses significant challenges to conventional routing schemes that assume stable energy availability. This work proposes a fuzzy-logic based adaptive routing and positioning framework designed for EH-enabled IoT networks to ensure balanced energy utilization and resilient connectivity. The framework operates through two key phases: (i) strategic placement of EH-nodes guided by contextual parameters such as residual energy, node density, and solar intensity, and (ii) energy-aware routing that dynamically selects optimal communication paths based on link quality and node energy levels. By leveraging the decision-making flexibility of fuzzy-logic, the system adapts in real time to fluctuating energy conditions, enhancing network longevity and reliability. Extensive simulations demonstrate that the proposed framework significantly improves energy efficiency, network sustainability, and data delivery performance compared to conventional routing approaches, highlighting its potential for large-scale, energy-constrained IoT deployments.