An Adaptive Content Caching Model to Improve the NDN-Based IoT Network Performance
Qaizar Javed, Arfan Jaffar, Muhammad Ali Naeem, Izhar Ahmed Khan, Mohammed Alsuhaibani · IEEE Access · 2025
The current Internet of Things (IoT) networks experience considerable challenges due to the limitation of device capacities, energy constraints, and data accessibility problems. The existing IP-based Internet architecture is inadequate to handle the huge volume of interconnected devices and data because of restricted address capacity and ineffective data retrieval processes. To address these challenges, Named Data Networking (NDN) appears as a promising solution, offering content-centric approaches and proficient in-network caching. This study presents a novel caching technique, named the Efficient Pre-Caching Technique (EPCT), which depends on two mechanisms, such as content selection using similarity functions and content placement. The proposed technique aims to improve cache hit ratios, latency, and hop count ratios. A full performance test in a simulated environment shows that EPCT is better than other NDN-based IoT caching techniques by highlighting its ability to make NDN-based IoT networks more efficient overall.