# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 2 Related Work
[18] point out that the data in a RAN is generated on a large scale and needs to be processed on devices with limited resources, such as storage and processing capacity, which represents a challenge. Scalability challenges arise as the number of mobile terminals generating data and MEC-hosted applications consuming RNIS grows. This work focuses on implementing alternative solutions for distributing messages in the publish-subscribe model (RabbitMQ and Apache Kafka) and evaluating and comparing the performance of solutions for implementing RNIS in MEC. This work compares candidate solutions for implementing RNIS by consuming information from an eNodeB. However, the solution considered aspects of the Fourth Generation network. [PERSON] et al. [31] discuss how many studies have focused on predicting UE behavior. However, most predictive algorithms require a lot of computing power, causing them to migrate to analytical applications deployed at the network's edge. Hence, the authors propose a mobile edge service that allows external applications to provide information about the UE's predicted or expected activity and mobility. These applications can gather information from the radio location network to establish patterns in the UE's behavior, thus making the information available through the network to help optimize resource management procedures. This mobile edge service allows other analytical applications to provide prognostic information about UE behavior to the RAN. Moreover, a dedicated application can send the values of the expected UE behavior parameters to the RAN using the proposed service interfaces, which can optimize mobility management procedures and manage user connections intelligently. However, this work needs to address the aspects of RNIS based on improvements to the emerging conditions for B5G. Following the previous work, [PERSON] et al. [32] discussed issues in providing the network with the prognosis of the UE behavior calculation. Hence, they proposed an extension of the RNIS, which involves improving the service to allow MEC apps to contribute prognostic data on user behavior. This extension enables service improvement and proactive decision-making at the network edge in 5G networks. Also, the proposed extension allows MEC apps to subscribe to RNIS on UE-related cell information and inform the radio access bearer, enabling an analysis of user behavior and mobility patterns. The proposal aims to extend the functionality of RNIS by allowing an exchange of information where RNIS can receive new information from the MEC app in addition to the RAN itself. However, it does not deal with improving information collection for interested subscribers. Based on the works discussed, most studies suggest the use of RNIS functions and the processing of information from the RAN for the LTE 4G networks. However, none of the literature takes into account the latest ETSI RNIS specification, unlike our proposed service and [PERSON] et al. 's work, which also discusses integration with 5G. This information is presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Work**} & **RNIS Integration** & **RNIS Integration** & **Using RNIS** & **New RNIS** & **Improvement of** \\  & **with MEC Using** & **with MEC Using** & **for MEC** & **Functionality** & **RNIS** \\  & **4G** & **5G** & **applications** & **Functionality** & **Information** \\ \hline [PERSON] et al. [16] & X & & X & & \\ \hline [PERSON] et. al. [28] & X & & X & & \\ \hline [PERSON] et. al. [29] & X & & X & & \\ \hline [PERSON] et. al. [30] & X & & X & & \\ \hline [PERSON] et al. [18] & X & & X & & \\ \hline [PERSON] et. al. [31] & X & X & X & & \\ \hline [PERSON] et. al. [32] & & X & X & X & \\ \hline
**Our Proposal** & & X & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with the related literature. All of the works which proposed using RNIS for MEC applications, such as that of [PERSON] et al. [18], have enabled the implementation of RNIS by validating different brokers. [PERSON] et al.