Adaptive Edge Intelligence Framework for Resource-Constrained IoT in Consumer Electronics

Edge computing is increasingly being identified as an essential tool for enabling real-time processing of data on resource-constrained Internet of Things (IoT) devices, especially within consumer electronics. Current deep learning architectures are less adaptable to resource fluctuations, as they fail to dynamically adapt and remain less efficient on resource-constrained devices. This research introduces as it improves decision support on resource-constrained Internet of Things devices by adapting complexity levels for real-time resource profiling. The proposed architecture integrates collaborative computing and peer-to-peer networking, while resource management is achieved through the context-aware component, which utilizes behavior analytics and environmental observations. The experiment showed efficiency improvement by 28% on the proposed architecture compared to other architectures, improving inference accuracy by 94.3%, and improving resource management and latency by 10-15% and 30%, respectively.