Context-Aware Edge AI: Adapting Machine Learning Models to Local Conditions

Edge computing has emerged as a transformative paradigm in distributed computing, bringing computational resources closer to data sources and end users. The integration of artificial intelligence with edge computing, known as Edge AI, enables intelligent decision-making at the network periphery. However, the dynamic and heterogeneous nature of edge environments presents unique challenges for deploying machine learning models. Context-aware Edge AI addresses these challenges by adapting models to local conditions such as environmental factors, resource constraints, user preferences, and temporal variations. This paper provides a comprehensive examination of context-aware Edge AI, exploring fundamental concepts, architectural frameworks, adaptation mechanisms, and implementation strategies. We discuss various types of contextual information, including spatial, temporal, environmental, and computational contexts, and review techniques for model adaptation such as online learning, transfer learning, model compression, and dynamic neural networks. Through detailed examples across domains including autonomous vehicles, smart cities, healthcare, and industrial IoT, we illustrate the practical applications and benefits of context-aware approaches. The paper also addresses key challenges including privacy preservation, energy efficiency, model drift, and system heterogeneity, while reviewing state-of-the-art methodologies and future research directions in this rapidly evolving field. Supplementary Material File (edge_ai_context_awareness_machine_learning_adaptation.pdf) - Download - 127.46 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 270views 138downloads Citations Download citation Surya Rao Rayarao, Naga Donikena. Context-Aware Edge AI: Adapting Machine Learning Models to Local Conditions. Authorea. 24 October 2025. DOI: https://doi.org/10.22541/au.176132904.45824162/v1 DOI: https://doi.org/10.22541/au.176132904.45824162/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.