Signal‐Aware Energy Harvesting and Scheduling Framework for Rechargeable Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSNs) have to continuously monitor process and transmit signals from the industrial environment with strict energy limitations. The impact of energy depletion–induced signal interruptions is significant on the reliability of tasks and the real‐time industrial operation. To overcome this challenge, a signal‐aware reliant task scheduling scheme (RTSS) for rechargeable IWSNs with energy harvesting mechanisms is proposed in this paper. The RTSS is based on a research gap–driven principle: For every researched limitation, a corresponding innovation is developed: a real‐time signal–energy coupling model that continuously monitors signal traffic density and node energy levels during periods of active signal processing; a knowledge‐learning mechanism that dynamically classifies nodes as available, idle, or low‐energy, allowing for proactive task allocation decisions without reliance on static energy assumptions; a dynamic task reallocation strategy that seamlessly transfers tasks from energy‐degraded nodes to available alternate processing nodes before signal interruptions; and an embedded energy harvesting module that recharges low‐energy nodes during idle processing periods to ensure long‐term network operability. The RTSS continuously updates its knowledge base over a scheduling interval, thereby enhancing the accuracy of the scheduling over time and continuity of its signal. The evaluation through simulation with a real‐world computer numerical control (CNC) machine dataset of 200 tasks and 60 sensor nodes verifies the superiority of the proposed RTSS over the state‐of‐the‐art RTSS baselines: joint power function–network‐in‐box (JPF‐NIB), low‐complexity channel resource allocation algorithm (LCCRAA), and enhanced energy optimization model (EEOM), in all critical performance metrics. In particular, the RTSS yields improvements of 10.48%, 10.87%, and 10.38% in the signal awareness task scheduling rate, successful signal delivery ratio, and energy harvesting rate, respectively, and reduces the average energy consumption by 8.63% and signal interruptions by 13.66% compared with the best performing baseline. The findings demonstrate the adaptability, scalability, and reliability of the RTSS for handling energy constraints in industrial applications with the requirement of uninterrupted signal processing and task execution.