A Bio-Inspired Smart Home: AI-Driven Adaptive Energy Management with Edge Computing, Blockchain Security, and Self-Powered IoT Sensors

Ahmed Shuhaiber, Zayed University

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Computer Science

Publication Date

6-28-2025

Abstract

The evolution of smart home systems has emphasized energy efficiency, automation, and security; however, existing models often compromise user adaptability, real-time decision-making, and sustainability. This paper presents a bio-inspired, eco-smart home framework that integrates self-powered IoT sensors, edge computing, AI-driven adaptive interfaces, and blockchain-enhanced security to optimize energy management while maintaining occupant comfort. The proposed system employs triboelectric energy-harvesting sensors to reduce battery dependency, edge computing (NVIDIA Jetson Xavier NX, Raspberry Pi 4) to minimize latency, and AI-powered adaptive dashboards to personalize automation based on user behavior. Additionally, a blockchain-based authentication mechanism ensures secure data exchange between IoT devices, cloud infrastructure, and actuators. A prototype implementation and real-world evaluation demonstrate significant improvements in energy efficiency, responsiveness, and user engagement compared to traditional smart home systems. This research contributes a novel, sustainable, and intelligent architecture that redefines the future of self-learning, eco-friendly smart homes.

ISBN

[9783031976506]

ISSN

0302-9743

Publisher

Springer Nature Switzerland

Volume

15895 LNCS

First Page

129

Last Page

141

Disciplines

Computer Sciences

Keywords

Adaptive User Experience, Blockchain Security, Edge Computing, Sustainable IoT Smart Home, Triboelectric Sensors

Scopus ID

105010821622

Indexed in Scopus

yes

Open Access

no

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