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Volume 13 | Issue 5 | Year 2026 | Article Id. IJECE-V13I5P116 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I5P116

A Neuro-Symbolic Digital Twin Framework of Self-Correcting and Stabilized IOT Conflict Resolution in Smart-Homes

Fayez Alharbi

Received Revised Accepted Published
12 Feb 2026 14 Mar 2026 16 Apr 2026 27 May 2026

Citation :

Fayez Alharbi, "A Neuro-Symbolic Digital Twin Framework of Self-Correcting and Stabilized IOT Conflict Resolution in Smart-Homes," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 181-197, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P116

Abstract

The growing independence of AI-controlled IoT smart-home solutions requires conflict management systems that ensure long-term environmental safety and energy-saving, as well as user-friendly comfort. The current deep learning-based correction models experience hidden context reversal, where corrective actions are overly applied or fluctuate, causing unstable comfort levels and unwarranted power consumption. To solve this, a novel Autonomous Smart-Home Device Conflict Resolution and Correction System (ASH-DCRC) is suggested. The system constantly processes the logs of devices and sensor streams through a Causal-Action Graph Transformer (CAGT) to yield causal action graphs and foresee conflict pathways. These conceptualized actions are verified within a Digital Twin simulator to confirm safety and environmental impact and then implemented into reality. The action decisions are then optimized by two sequential DNN layers: the Trust-Embedded Adaptive Policy Selector (TAPS) to select an action based on its reliability, and the Temporal Equilibrium Stabilizer (TES) to select an action based on stability without oscillation. Lastly, Digital Twin-guided Neuro-Symbolic Reinforcement Learning applies to the continuous enhancement in a secure manner by exploring and adapting over the long run. The experimental results prove that ASH-DCRC is much better than the chosen baseline and literature models with 98.8% accuracy, 0.82 s conflict-resolution latency, and 18.7% energy-efficiency gain. These findings confirm that the ASH-DCRC is a stable, energy-aware, and comfort-preserving conflict-resolution solution to next-generation smart-home intelligence.

Keywords

Smart Home Automation, Iot Device Interaction, Iot Device Conflict Resolution, Neuro-Symbolic And Causal Learning, Deep Neural Networks.

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