# Recent Advances in Information-Centric Networks (ICNs)
###### Abstract
As indicated in the title, the method uses a machine learning technique based on multinomial naive Bayes to classify the received contents, together with an adaptive resource allocation strategy based on the Lagrange method. Simulation experiments confirm the potential usefulness of the strategy to manage the trade-off between efficiency and user satisfaction. One of the most important components in NDN is the cache strategy, since it improves bandwidth usage, server load, and service time [4]. [PERSON] et al. address this issue in the third contribution, titled \"Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network\". They propose the Weighted-CAPIC caching algorithm to improve the priority class performance, through adjusting different weights for every content class in addition to considering the variation of the content in providing a cache portion in the NDN router, without having to coordinate with other routers. The work shows that the proposed caching algorithm provides a higher cache hit ratio for both the priority class and the whole system. Another work related to cache issues is presented in the paper by [PERSON] et al. titled \"Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN\" (contribution 10), the last paper to be accepted and published. The work focuses on enhancing cache performance through maximizing the cache hit ratio in software-defined networking (SDN), that is, a context where the ICN offers flexibility, scalability, and efficient resource management [5]. The authors propose a statistical model that generates users' content preferences, incorporating key elements observed in real-world scenarios. Furthermore, they introduce a graph neural network-double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that this caching strategy achieves a cache hit ratio significantly higher than the state-of-the-art policy. The Internet of Vehicles (IoV) is also a promising research area in the field of the Internet of Things [6]. Advances in ICN, edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). In this context, in contribution 4, [PERSON] et al. provide us with a comprehensive review of utilizing intelligence in EC and ICN to address current challenges in their application to the IIoV. In particular, they focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding, and overall network performance. Furthermore, they discuss potential solutions and highlight potential research directions. Despite the large amount of works published in the literature about different implementations of NDN architecture, there are few NDN implementations in real networks [7]. Contribution 5 by [PERSON] et al., titled \"Insights from the Experimentation of Named Data Networks in Mobile Wireless Environment\", evaluates the performance of an NDN-based implementation in a mobile wireless network, as part of a smart city infrastructure. This work is especially interesting since ICN has been considered an alternative for overcoming the drawbacks of host-centric architectures when applied to mobile Internet of Things (IoT) networks. Such drawbacks are the frequent topology changes and intermittent connectivity caused by the mobility of the network nodes. The implementation is evaluated in three scenarios, and the results show that the implementation works properly, also deriving insights about correct NDN parameterization. Contribution 6 brings us another interesting area of application for NDN architecture: satellite communications using LEO constellations with inter-satellite links (ISLs).