# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
###### Abstract
The data collected by sensors is processed at the edge (MEC), enabling dynamic adjustments, while algorithms help automate agricultural machinery operations. Efficient data analysis at the MEC boosts agricultural efficiency by using RNIS information to adapt to the conditions of applications and devices in a precise and timely manner. In the face of that, RNIS is essential in providing detailed RAN information. As seen in Figure 1, the concept of Agriculture 4.0 brings a series of applications in different scenarios and sectors of the production chain. These applications use a variety of sensors and devices scattered around the environment, such as temperature and humidity sensors, drones for irrigating crops, robots for planting, and autonomous heavy machinery. These scattered devices generate information and help control remote operations. Using the B5G network, more IoT devices are scattered around the environment, communicating, generating, and sharing information. The data is sent to the network's edge, where it is processed and used by interested applications. Hence, this contributes to decision-making and improving the quality of services. In this way, applications that depend on the context of the environment can perform even better, driving the advancement of agriculture and industry and bringing innovative solutions to the sector [19; 20]. Considering scenarios with higher performance demands, 5G networks have faced challenges related to the increase in traffic volume, such as managing large amounts of data generated in the network and an increase in computational demands [13], all of which stem from the popularization of services, applications, and activities that require increasingly precise information about UE [13]. The current RNIS and MEC specifications do not include specific 3 GPP UE link information, which raises uncertainties about how RNIS should interact with 5G mobile networks. The lack of detail raises questions about how RNIS should obtain information from the 5G Radio Access Network, highlighting the case that RNIS itself needs improvement to obtain and expose such radio network information effectively and whether communication between the MEC and 5G networks [21] needs improvement to provide such information-capture mechanisms. In addition, the correct exposure of NG-RAN information needs an improvement in terms of integration between MEC and B5G [18; 22; 23; 24]. This paper proposes RNIS as a service to the MEC framework in B5G networks, highlighting the improvements needed and the paths available to enhance the delivery of relevant information using Agriculture 4.0 as a use case. The importance of RNIS and the need to improve it to effectively obtain and display radio network information are discussed. The RNIS aims to improve performance and enable new business models based on information available to MEC applications (MEC apps). We have proposed the development of an RNIS coupled with MEC integrated with B5G at the network's edge, collecting and providing RAN information to enhance agricultural applications. We have validated our proposed service using Agriculture 4.0 applications as a use case in different scenarios. Our main contributions are as follows:  
* New RNIS as a service to the MEC framework in B5G networks
* Specification and architecture of the new RNIS improvements
* Implementation and validation of the RNIS available on public repository [25] in order to utilize the community to build on this knowledge. In addition to this Introduction section, Section 2 discusses the related work. Section 3 describes our proposal describing the specification and architecture of the new RNIS as  
Figure 1: Agriculture 4.0 in the context of emerging technologies such as B5G and MEC. a service improvements, and also details regarding implementation. Section 4 presents the validation, evaluation and results. Section 5 finalizes our work by presenting our conclusions and future work directions.