Making Consumer Devices Smarter: Energy-Adaptive and Sustainable Edge AI for Real-Time Intelligence
Year: 2026
Type: article
Abstract: Modern consumer devices demand real-time artificial intelligence (AI)-based inference under strict battery, latency, and computation constraints. Existing cloud-based inference introduces communication delay. Static edge inference and traditional device–edge collaboration do not address redundant computation among nearby consumer devices that observe similar contexts. This article proposes an energy-adaptive and sustainable edge AI framework for smart consumer devices. The novelty of our model lies in combining three mechanisms, namely, energy-aware model scaling, context-aware micro-federation, and adaptive inference-role coordination within an integrated consumer-edge workflow. The framework evaluates device-level inference cost and runtime suitability and selects lightweight or high-accuracy models accordingly. This allows nearby devices with overlapping contexts to reuse inference outputs from selected primary nodes. Evaluation using OPPORTUNITY and ExtraSensory context-recognition datasets shows that the proposed framework reduces normalized energy consumption by 39%. The model reduces latency from 210 ms to 82 ms and redundant computation by 52%, while maintaining 95.8% inference accuracy. These representative results indicate that the proposed framework improves real-time responsiveness, reduces unnecessary device computation, and supports sustainable edge intelligence for next-generation consumer electronics.
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203758977