## 2 Related Work
### IEC's State of Art
Industry drivers [8] and the radio communication Sector has identified three important categories, including eBBm, mMTC and URLLC. Some studies predicted that by 2020, 50 billion objects would be connected to the Internet [39]. Some IoT applications have very short response times, some have private data, and some may generate large amounts of data that can be a heavy burden on networks. In addition, Cloud computing is not efficient enough to support these applications. As more devices connect, sensitive applications face severe latency problems. Furthermore, Cloud computing fails to meet mobility and location awareness support needs. With the explosion of data, devices and interactions, cloud architecture alone cannot manage the influx of information [40; 41]. To overcome these problems, a new model called Cloud calculations was proposed since 2012. (1) _Edge Computing:_ Edge computing significantly reduces the amount of data that needs to be transmitted, resulting in traffic and distance that needs to be traveled [42; 43]. Edge computing also means enabling technology to transfer computing to the edge of the network, where downstream data is performed in cloud services and upstream data is operated in IoT services. In computing edge storage and information processing are guided to the edge of the network and near the source of information, i.e., instead of sending information generated by IoT devices in the traditional way to data centers or remote servers for storage and processing; this information are placed on local servers and stored through a local gateway. This approach increases the speed of data analysis and reduces network pressure. The logic of edge computing is that the computing should be done in the vicinity of data sources. In addition, edge computing can be replaced by fog computing [16]. From [PERSON]'s point of view [44], edge computing can be replaced by fog calculation, but edge calculation is more object-oriented, while fog computing is more structure-oriented. Edge computing offers many advantages over traditional architectures, such as optimizing resource use in a cloud computing system. Performing computing at the edge of the network reduces network traffic. Edge computing also improves security by encrypting information close to the network kernel while optimizing data, rather than the kernel, for performance. Control is very important for the edge. (2) _Edge computing and security:_ Edge computing provides the creation of small virtualization infrastructures at the edge of the network, using base stations, radio network controllers, or other aggregation sites. Edge computing is a paradigm similar to fog computing but with different architectures and business models (infrastructure belongs to a single operator and does not include user devices). In addition, edge computing explicitly defines telecommunications infrastructure for providing mobile edge services, such as radio network information, location, and bandwidth management [45]. IEC has similar security concerns to the cloud. However, distributed resources associated with the installation of peripheral networks usually have fewer restrictions and physical access control than traditional data centers, so the risk of diversion is negligible. Besides, IEC reduces the need for more human resources (with different capabilities and security skills) and reduces the risk of errors, weaknesses or missing settings. Range API increases the level of attack and the potential impact of the attack. In fact, such services allow access to sensitive information about the physical and virtual environment, including the location and network traffic of other users. Integrating intelligence edge computing with the entire network operational support system also enables successful penetration and increased commitment to control large infrastructure and regional or national communications services [46]. Finally, IEC is expected to run orchestral services, in short, to combine several applications.