Human-Centric AIoT and Swarm Intelligence for Dynamic Energy Optimization and Decision-Making in Smart Factories

Description

The convergence of Artificial Intelligence of Things (AIoT) and Swarm Intelligence (SI) represents a transformative shift in smart manufacturing, particularly when designed to include human-in-the-loop feedback. This paper proposes a unified framework for real-time energy optimization, dynamic pricing response, and collaborative decision-making using AI-driven forecasting, swarm coordination, and human oversight. A synthetic dataset simulating four factories shows strong correlations between AI prediction accuracy, energy cost reduction, swarm agent efficiency, and supervisory interventions. The results suggest such hybrid systems reduce energy waste, increase resilience, and enable ethically aligned industrial automation.

Steps to reproduce

This research presents a novel framework integrating AIoT-enabled demand response and Swarm Intelligence with human-in-the-loop (HITL) control to optimize energy consumption and decision-making in smart factories. Using a 500-row synthetic dataset simulating operations across multiple zones, the study analyzes real-time energy demand, load forecasting errors, swarm-based task allocations, and supervisory interventions. Results show this integrated architecture reduces peak-time energy costs by ~30%, enhances grid stability, and ensures ethical, human-aligned automation—hallmarks of Industry 5.0.

Institutions

• Independent University
• North South University

Categories

Licence