Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning

Wail Zita; Nassma Zita

doi:10.35377/saucis...1736606

Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning

Abstract

The dynamic and resource-constrained nature of Internet of Things (IoT) environments demands software systems that are both adaptive and transparent. Traditional static architectures fall short in addressing these challenges, particularly in scenarios requiring real-time decision-making and energy efficiency. This paper presents a modular and explainable self-adaptive software architecture that leverages Deep Q-Learning (DQN) for intelligent adaptation and integrates hybrid explainability mechanisms using SHAP and LIME. The proposed architecture supports runtime decision-making through a layered design, enabling context-aware sensing, battery-aware adaptation, and interpretable actuation. A custom reward function optimizes energy consumption, latency, and service quality, while explainability modules provide insight into both global learning behavior and individual runtime decisions. Experimental evaluation in a simulated smart home environment demonstrates the effectiveness and transparency of the system, highlighting its potential for deployment in constrained and safety-critical IoT settings.

Keywords

Ethical Statement

This study did not require ethics committee approval as it was based on publicly available datasets and did not involve human participants, animals, or sensitive personal data.

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Details

Primary Language

English

Subjects

Software Architecture, Software Engineering (Other)

Journal Section

Research Article

Authors

Wail Zita ^*
0009-0007-0744-7795
Türkiye

Nassma Zita
0009-0007-4707-3396
Türkiye

Early Pub Date

May 11, 2026

Publication Date

June 17, 2026

Submission Date

July 7, 2025

Acceptance Date

December 5, 2025

Published in Issue

Year 2026 Volume: 9 Number: 2

DOI

https://doi.org/10.35377/saucis...1736606

IZ

https://izlik.org/JA58NS22KG

Cite

RIS / Bibtex

APA

Zita, W., & Zita, N. (2026). Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning. Sakarya University Journal of Computer and Information Sciences, 9(2), 364-376. https://doi.org/10.35377/saucis...1736606

AMA

1.Zita W, Zita N. Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning. SAUCIS. 2026;9(2):364-376. doi:10.35377/saucis.1736606

Chicago

Zita, Wail, and Nassma Zita. 2026. “Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning”. Sakarya University Journal of Computer and Information Sciences 9 (2): 364-76. https://doi.org/10.35377/saucis. 1736606.

EndNote

Zita W, Zita N (June 1, 2026) Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning. Sakarya University Journal of Computer and Information Sciences 9 2 364–376.

IEEE

[1]W. Zita and N. Zita, “Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning”, SAUCIS, vol. 9, no. 2, pp. 364–376, June 2026, doi: 10.35377/saucis...1736606.

ISNAD

Zita, Wail - Zita, Nassma. “Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning”. Sakarya University Journal of Computer and Information Sciences 9/2 (June 1, 2026): 364-376. https://doi.org/10.35377/saucis. 1736606.

JAMA

1.Zita W, Zita N. Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning. SAUCIS. 2026;9:364–376.

MLA

Zita, Wail, and Nassma Zita. “Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 2, June 2026, pp. 364-76, doi:10.35377/saucis. 1736606.

Vancouver

1.Wail Zita, Nassma Zita. Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning. SAUCIS. 2026 Jun. 1;9(2):364-76. doi:10.35377/saucis. 1736606