AI at the Edge: Transforming Real-Time Data Processing
DOI:
https://doi.org/10.56472/ICCSAIML25-106Keywords:
AI at the Edge, Edge Computing, Real-Time Data Processing, Machine Learning, IoT (Internet of Things), Autonomous Systems, Data Analytics, Edge Devices, Low Latency, Edge AI, Edge Hardware, Network Connectivity, 5G, Privacy and Security, Model Optimization, Industrial IoT, Healthcare, Autonomous Vehicles, Smart Cities, Precision Farming, Federated Learning, AI Deployment, Edge-Cloud IntegrationAbstract
The adoption of Internet of Things (IoT) devices and interconnected systems has led to an urgent requirement for faster, more efficient data processing. Quite frequently, traditional cloud-based architectures have to deal with issues of latency, bandwidth limitations, and network reliability, which makes them less proper for decision-making needs that are time-critical. Concerns like these are met by the AI at the edge by moving the data processing closer to the source using edge devices. Businesses can thus achieve a much shorter period of time for data processing, more patience, and less dependence on cloud connectivity by placing machine learning models in a certain location. This method is completely changing the sectors that use it such as healthcare, manufacturing, transportation, and smart cities. Wearable devices, for example, in healthcare, can monitor patient vitals in real-time, and thus, they can alert caregivers to emergencies earlier. Predictive maintenance systems, AI-powered ones, in manufacturing are capable of sighting equipment failures in the early stages thus they can decrease the cost of the downtime and the production could be promoted. In addition, the demand for edge AI enhances data privacy because it stops sending sensitive information to the cloud when it is not necessary which lowers the potential to the cloud for such risks. Efficient and accessible AI deployment on the edge came true thanks to the latest improvements in lightweight models, superior algorithms, and hardware accelerators such as GPUs and TPUs. Enterprises which go with AI at the edge should be concentrated on the strategic implementation process emphasizing the balance of performance, scalability, and security. Companies and institutions who successfully apply and leverage this technology can reap the benefits of having access to real-time insights, improve operational efficiency, and increase user experience. With the ongoing evolution of AI at the edge, it will be able to reconstruct the industry and provide devices that are intelligent and operational systems which can make decisions in a small amount of time. The whole method will have first of all been reshaped the productions in this digital age
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