Energy-Aware Scheduling Algorithm for Industrial IoT Networks in Smart Factories

Abstract

Industrial Internet of Things (IIoT) networks deployed in smart factories require ultra-reliable, low-latency, and deterministic communication while simultaneously ensuring energy efficiency for battery-powered and resource-constrained devices. Conventional scheduling algorithms such as Earliest Deadline First (EDF) and Round Robin (RR) primarily optimize latency and throughput, often overlooking energy-awareness, which leads to premature node depletion, reduced network lifetime, and increased maintenance costs. To address these challenges, this paper proposes a novel Energy-Aware Adaptive Priority Scheduling (EA-APS) algorithm specifically designed for industrial IoT environments. The proposed approach introduces a composite scheduling score that integrates residual node energy, task criticality, deadline urgency, transmission cost, and real-time network congestion conditions into a unified decision metric. Unlike traditional static-priority mechanisms, EA-APS dynamically adjusts scheduling weights based on network state, enabling adaptive trade-offs between energy preservation and time-critical performance. A hybrid optimization framework is developed to minimize overall energy consumption while satisfying strict deadline and reliability constraints inherent to smart manufacturing systems. Extensive simulations conducted in a large-scale industrial scenario with mixed traffic patterns demonstrate that EA-APS significantly enhances system performance. Results show up to 28% improvement in network lifetime and 22% reduction in average energy consumption compared to EDF and RR schemes, while maintaining packet delivery reliability above 99.2% and substantially reducing deadline miss ratios. These findings confirm that the proposed EA-APS algorithm effectively balances energy efficiency and real-time communication requirements, making it a suitable and scalable scheduling solution for next-generation Industry 4.0 smart factory deployments.