All Posts in "Month: July 2025"

AI at the Edge for Real-Time Data Processing and Low-Latency Applications

As the demand for faster and more efficient data processing intensifies, businesses and technology developers are turning to AI at the edge to meet the challenges of real-time applications and low-latency environments. Edge computing combined with artificial intelligence enables data to be processed locally—close to the source—vastly reducing delays and improving responsiveness. This approach is revolutionizing how organizations handle critical workloads and deliver enhanced user experiences.

What is AI at the Edge?

AI at the edge refers to the deployment of artificial intelligence algorithms and models directly on edge devices such as sensors, gateways, smartphones, or IoT devices. Instead of sending raw data to cloud servers for processing, edge devices analyze and interpret data locally. This decentralization enables rapid decision-making and actions without the dependency on cloud connectivity.

Benefits of AI at the Edge for Real-Time and Low-Latency Applications

1. Reduced Latency

By processing data near the source, AI at … Read More

Betty Gray by — July 20, 2025 in AI in Next-Generation Networking Technologies
How to Implement AI-Driven Quality of Service (QoS) for Video Conferencing

In today’s digitally connected world, video conferencing has become an essential tool for businesses, educators, and social interactions. Ensuring high-quality video and audio during calls is critical for effective communication and collaboration. Implementing AI-driven Quality of Service (QoS) for video conferencing offers a powerful solution to optimize network performance, reduce latency, and enhance user experience. Here’s how to effectively implement AI-driven QoS for video conferencing systems.

Understanding AI-Driven QoS for Video Conferencing

Traditional QoS methods prioritize traffic based on predefined rules. However, these approaches often fall short in dynamically adapting to unpredictable network conditions, especially during video calls where bandwidth and latency can fluctuate rapidly.

AI-driven QoS leverages machine learning and artificial intelligence techniques to analyze real-time network data, predict performance issues, and automatically adjust network parameters. This dynamic optimization helps maintain seamless video and audio quality throughout the conference.

Steps to Implement AI-Driven QoS for Video Conferencing

1. Assess

… Read More
Betty Gray by — July 10, 2025 in AI for Network Traffic Optimization & QoS