The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.

University campus networks need wired (ethernet) and dense wireless fidelity networks that have devices like access points, switches, and routers that are always turned on. Consequently, they generate two important problems: the energy bill and the influence of carbon dioxide into the atmosphere. Energy savings are the solution to those problems. There are several proposals to augment the energy savings separately in ethernet and wireless fidelity, but there is no integrated method to simultaneously reduce them in both parts of the networks. Our novel method combines idle cycling and machine learning techniques to efficiently obtain energy savings in both parts of the network simultaneously. We categorize network devices into two groups: (a) those that are always turned on and (b) those that can be dynamically turned on or off based on network performance. We formulated two algorithms that decide when to turn on and off access points. We use Ward's machine learning hierarchical clustering technique to optimize the energy savings of our model in the network of the Unidades Tecnológicas de Santander (Bucaramanga, Colombia). We showed that energy savings of 21.5 kWh per day are possible. The success of the model in this context highlights its potential to achieve substantial energy savings.

Tele-education went from being an option to becoming a necessity given the current public health problems. Due to this, the educational sector has included blended courses as part of its training offer. Mobile Ad Hoc Networks (MANETs) will therefore, become an important resource to work, under this class format, with students outside the classroom, in accordance with the standards recommended inside a university campus. MANETs require certain parameters that ensure the quality of communications and identify the factors that influence Quality of Service (QoS). The purpose of this study is to determine the factors that affect the quality of communications in a tele-education environment. For this purpose, the QoS of a MANET supporting a real-time video streaming is evaluated based on throughput. As a result, the main factors that directly or indirectly affect the operation of the network could be identified, which may help in making decisions regarding aspects such as the number of nodes and the node mobility speed. Moreover, the versatility and scalability of the MANET was proven, as when the number of nodes went from 5 to 10, throughput increased by 14 %. This also occurred with the transmission rate factor when the video was streamed in a channel with a variable bitrate (64–4096 kbps).

Efficient load balancing is a fundamental aspect within a densely populated Wireless Fidelity (WiFi) network, particularly when the goal is to evenly distribute bandwidth and regulate Internet usage. The distribution of network traffic for load balancing can be achieved by utilizing quality of service protocols and access control lists or filters. This document introduces the application of a machine learning-based prediction model to outline time intervals of congestion in a densely populated WiFi network employing dynamic load balancing. Historical data related to associated users and WiFi network bandwidth serve as key indicators to assess the network's congestion level. Dynamic load balancing is executed by allocating bandwidth to priority traffic based on observed congestion levels. To implement load balancing, access control lists based on time and quality of service parameters are configured within the network devices.The experimental findings reveal a notable improvement in the performance of a WiFi network through the implementation of our dynamic load balancing approach, resulting in a 50% reduction in lost packets. This innovative method allows for the definition of priority traffic types during different congestion periods.