Adaptive Endurance: A Framework for AI Resilience in Edge Environments
Year: 2025
Type: article
Abstract: Artificial Intelligence has achieved precision but struggles with imperfection. Most AI systems are optimized for ideal conditions clean data, stable networks, and uninterrupted performance. However, in the real world, especially in remote environments like Papua, intelligence must do more than perform; it must endure. This report introduces the Adaptive Endurance Framework (AEF), a conceptual model for developing AI systems capable of recovery, adaptation, and learning under unstable edge conditions. Inspired by human resilience, the framework defines three core layers Detection, Adjustment, and Reflection designed to help AI maintain functionality during uncertainty. By measuring recovery efficiency instead of mere accuracy, AEF proposes a shift toward a new kind of intelligence: one that doesn’t just compute but persists.
Author: Mamahit, Evan Ricardo Ekly
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7091660048
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