# Edge Artificial Intelligence for Real-Time Decision Making using NVIDIA Jetson Orin, Google Coral Edge TPU and 6G for Privacy and Scalability

**Authors:** Dr.J.V.Anchitaalagammai, Dr.S.Kavitha, R.Buurvidha, T.S.Santhiya, Roopa, Sankari
**Year:** 2025
**DOI:** 10.1109/ICVADV63329.2025.10960953
**URL:** https://doi.org/10.1109/ICVADV63329.2025.10960953

## Abstract

Edge AI or Edge Artificial Intelligence is the provision of AI within edge devices itself without needing a connection to the cloud. AI gets better with more information and time, and so a lot of AI models “live in the cloud.” However, AI made Edge devices is more smarter by deploying models onboard. There are several factors like NVIDIA's Jetson Orin, Google's Coral Edge TPU, and Qualcomm's AI optimized Snapdragon platforms, which has accelerated the adoption of Edge AI and made these onboard Edge devices with modests resources, robust processing capabilities. Experts anticipate that the advent of 6G technology will further expand speeds, making it possible to utilize augmented (AR) and virtual (VR) reality as well as the Internet of Things (IoT). Privacy of the end user is still safeguarded through federated and differential learning which enables intelligent distribution while data is kept close to the source. The concept of optimization techniques such as quantization, pruning, and NAS are resources that help in considerations for deploying AI models at the Edge as highlighted in the literature. Containerization and microservices also provide a great ecosystem for Edge AI deployments supported by Docker, Kubernetes and KubeEdge which provide scalable infrastructure. Evaluations conducted on various use cases rangingfrom predictive maintenance of industrial IoT[l], real- time health monitoring as well as autonomous systems, show the performance of these models to improve in terms of model accuracy, inference computation as well as energy efficiency through the enhanced methodologies. The recent advancements in hardware and software capabilities have placed Edge AI as a preferred option for many applications that have strict latency requirements in addition to needing to make real time decisions. Edge AI is set to revolutionize industries likehealth care, smart cities, industrial automation despite hitches like data unification and barrier from privacylaws. Further studies is suggested to improve on energy management, security measures and reduce barriers to interoperability so as to optimize the chancesof Edge AI in many applications.

## Citation Details

Edge Artificial Intelligence for Real-Time Decision Making using NVIDIA Jetson Orin, Google Coral Edge TPU and 6G for Privacy and Scalability
**Authors**: Dr.J.V.Anchitaalagammai, Dr.S.Kavitha, R.Buurvidha, T.S.Santhiya, Roopa, Sankari
**Year**: 2025
**DOI**: 10.1109/ICVADV63329.2025.10960953
**URL**: https://doi.org/10.1109/ICVADV63329.2025.10960953

**Abstract**: Edge AI or Edge Artificial Intelligence is the provision of AI within edge devices itself without needing a connection to the cloud. AI gets better with more information and time, and so a lot of AI models “live in the cloud.” However, AI made Edge devices is more smarter by deploying models onboard. There are several factors like NVIDIA's Jetson Orin, Google's Coral Edge TPU, and Qualcomm's AI optimized Snapdragon platforms, which has accelerated the adoption of Edge AI and made these onboard Edge devices with modests resources, robust processing capabilities. Experts anticipate that the advent of 6G technology will further expand speeds, making it possible to utilize augmented (AR) and virtual (VR) reality as well as the Internet of Things (IoT). Privacy of the end user is still safeguarded through federated and differential learning which enables intelligent distribution while data is kept close to the source. The concept of optimization techniques such as quantization, pruning, and NAS are resources that help in considerations for deploying AI models at the Edge as highlighted in the literature. Containerization and microservices also provide a great ecosystem for Edge AI deployments supported by Docker, Kubernetes and KubeEdge which provide scalable infrastructure. Evaluations conducted on various use cases rangingfrom predictive maintenance of industrial IoT[l], real- time health monitoring as well as autonomous systems, show the performance of these models to improve in terms of model accuracy, inference computation as well as energy efficiency through the enhanced methodologies. The recent advance  # Truncate if very long

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