An Adaptive Content Caching Model to Improve the NDN-Based IoT Network Performance

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. Our simulation results represent that EPCT considerably improves the performance through key metrics. In particular, it increases the cache hit ratio up to 38%, reduce the average hop count by 26%, and decreases the content retrieval latency around 31% when it is compared with existing caching techniques such as CEE, MPC, and CFPC. These considerable improvements highlight the EPCT potential to increase data availability and ease the network traffic in dynamic IoT settings.