All Posts in "AI in Next-Generation Networking Technologies"

Empowering the Airwaves: Developing Cognitive Radio Networks with AI for Autonomous Spectrum Management in Indonesia

The burgeoning demand for wireless connectivity in Indonesia, fueled by a rapidly growing digital economy and a geographically diverse landscape, places immense pressure on the limited radio frequency spectrum. Traditional static spectrum allocation methods struggle to keep pace with the dynamic needs of various applications and user demands, leading to spectrum scarcity and inefficient utilization. Cognitive Radio Networks (CRNs), empowered by Artificial Intelligence (AI), offer a promising solution for autonomous spectrum management, paving the way for more efficient and flexible use of the airwaves across Indonesia’s diverse regions.

The Spectrum Crunch in Indonesia:

Indonesia’s unique geographical characteristics, spanning thousands of islands, and its burgeoning digital adoption necessitate robust and efficient wireless communication infrastructure. However, the traditional approach of allocating fixed frequency bands to licensed users often results in:

  • Spectrum Underutilization: Licensed bands can remain idle for significant periods while other unlicensed bands experience congestion.
  • Limited Access for New Technologies and
… Read More
Betty Gray by — August 22, 2025 in AI in Next-Generation Networking Technologies
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
Navigating the Connected World: Using Machine Learning for Intelligent Routing in Large-Scale IoT Networks

The Internet of Things (IoT) has moved from a futuristic concept to a tangible reality, with billions of interconnected devices generating an unprecedented volume of data. From smart homes and wearable technology to industrial sensors and connected vehicles, IoT networks are becoming increasingly pervasive. However, managing and optimizing these large-scale deployments presents significant challenges, particularly when it comes to routing data efficiently and reliably. Traditional routing protocols, often designed for static or less dynamic networks, struggle to cope with the inherent characteristics of large-scale IoT deployments: heterogeneity, resource constraints, mobility, and unpredictable traffic patterns.

This is where the transformative power of Machine Learning (ML) comes into play. By leveraging data-driven insights, ML algorithms can enable intelligent routing decisions, leading to enhanced network performance, improved energy efficiency, and greater overall resilience in large-scale IoT networks.

The Limitations of Traditional Routing in IoT:

Traditional routing protocols, such as RPL (Routing Protocol for … Read More

Betty Gray by — June 18, 2025 in AI in Next-Generation Networking Technologies
AI-Powered 5G Network Slicing and Dynamic Resource Orchestration for Enhanced Performance

The advent of 5G technology promises unprecedented connectivity speeds, ultra-low latency, and massive device support that will revolutionize industries and user experiences alike. To fully unlock the potential of 5G networks, innovative approaches like network slicing and dynamic resource orchestration are essential. When powered by artificial intelligence (AI), these technologies enable intelligent, adaptive management of network resources, ensuring enhanced performance, reliability, and efficiency.

Understanding 5G Network Slicing

Network slicing is a fundamental feature of 5G that allows a single physical network infrastructure to be partitioned into multiple virtual networks or “slices,” each tailored to specific use cases or service requirements. For example, different slices may cater to enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), or massive machine-type communications (mMTC).

Each slice operates independently with customized resources, quality of service (QoS), and security policies, enabling operators to serve diverse applications simultaneously without compromising performance.

The Role of Dynamic Resource

… Read More
Betty Gray by — May 17, 2025 in AI in Next-Generation Networking Technologies