Context Aware Real Time Data Processing Algorithms for Enhanced Performance in Edge Computing Networks

Edge computing has become a disruptive technology to address the increased need of low latency and context-based data processing in the resource-constrained IoT and IIoT settings. Yet, the traditional methods are not flexible and resistant to changes in making decisions in a dynamic environment. This paper presents CANE-Net (Context-Aware Neural Edge Network), a neural network-based intelligent framework that is lightweight, but capable of processing real-time data and changing in accordance with the differences in the context. CANE-Net incorporates a purposeful encoder of context, alongside an adaptive architecture of GRU, to make temporal representation and situational responsiveness better. The model is trained and tested on the Edge-IIoTset cybersecurity dataset, having a well-defined Python-based implementation. The experimental test shows up with 98.5 accuracy and results indicate that CANE-Net is always better in terms of accuracy, precision, recall, and F1-score than the current baseline models, which means that it is highly reliable and effective. In general, this paper demonstrates that context-aware neural processing plays a crucial role in enhancing realtime edge analytics and has an intelligent and scalable basis with which to be implemented in smart cities, industrial systems, and edge-oriented security applications.