## 7 IEC in 6G and Open Research Challenges in IEC with 6G Network
### 6G Network Market  
Many expectations of emerging technologies and network of 6G and beyond, based on the 6G Network and its infrastructure. Positioning of 2030 Networks will depend on different edge computing technologies and advanced communications. Integration between 6G Network and edge computing mobile and wireless is expected to be more effective and practical. Many verticals will depend on the 6G Network infrastructure to enter the market. 6G Network promotes the horizontal ICT sector by enabling new services which will foster the creation of cutting-edge applications in a variety of industries.  
One of the most important factors in entering new markets is the flexibility achieved through the allocation of the resources used and the ability to program and customize them. Such customization and computing will fulfill the need for bandwidth, latency and localization. Furthermore, the ability of compounding service that depends on both time or data transfer and tactile internet will enable new communication services to enter new markets. Some of the emerging market examples are education, healthcare, energy, entertainment and industry.  
Taking into account the rapid evolution of wireless networks and the core of the network, it is believed that AI in general and machine learning in particular will play a significant role in next-generation networks (i.e., networks of the fifth and subsequent generations). Recently, machine learning has been used in a variety of applications, such as virtual personal assistants, video surveillance on social networks, filtering spam and malware by email, search engines, etc. In the future, machine learning (ML)-based approaches, including reinforcement learning, supervised/unsupervised learning, deep learning, transfer learning as well as federated learning for AI in MEC have become hot topics. For research in the field of using AI for communication networks, the following global tasks can be distinguished at present:  
1. Unambiguous identification of traffic in the communication network, without introducing additional delays in the flow, in order to meet the requirements of communication networks with ultra-low delays.
2. System online monitoring of a communication network from data flow, including virtual, to multi-parameter models of a network segment with many devices and systems.
3. Short-term and long-term forecasting of the load both on network elements and on entire segments.
4. Short-term and long-term prediction of the behavior of data streams at the data transfer level and service flows at the control level.
5. Long-term forecasting of the load on the network and computing infrastructure, taking into account trends in traffic profiles, types of services, in order to determine and automatically generate proposals for reducing or expanding the network, as well as its threshold characteristics.
6. Efficient allocation of 5G radio coverage with a prediction of cell load.
7. Enhancing signal quality with predictive physical layer codecs.
8. Short-term and long-term forecasting of user needs for certain services.
9. Predicting the user's movement geographically, as well as the formation of a model of his preferences in the content.  
Recognition and prediction of malicious attacks on the system with the formation of a proactive response to a possible attack.
* Application of AI technologies to consistently distribute services over the network on edge computing and fog computing frameworks.
* AI-based MEC system for integrating heterogeneous IoT technologies with 5G cellular system.
* Security-aware data offloading and resource allocation for edge computing systems