# 6G Cognitive Information Theory: A Mailbox Perspective
## 10 Customized UAV Networking Enabled Edge Intelligence  
As is well-known, there is a large amount of diversified data in the heterogeneous Internet of Things, including the designed drone network. Multiple sensors indicate that it is needed to conduct numerous analyses on massive data. Thus, is there a way to embed distributed intelligent algorithms into a different stage of the data value chain? Multi-level data fusion can be a potential research direction. Section 6 proposes a brand new concept of data fusion for data life cycle extensions. Using the deep fusion of the data value chain, applications, and human-machine interaction as a motivation, a general architecture that uses different data-fusion technologies is proposed. The authors believe that using data fusion for data life cycle extensions can help to improve data intelligence from low level to high level in six aspects: data fusion for data generation, acquisition, transmission, storage, analytics, and application. Thus, a new and unique solution for a \"people-oriented\" user service and experience can be achieved. This section gives an innovative enabling technology and application, i.e., customized UAV networking enabled edge intelligence. As an IoT equipment carried within computers and sensors, UAVs are widely used in many fields, such as military, monitoring, logistics, aerial photography, smart city, and so on. Compared with the traditional manned aircraft, UAVs have the advantages of small size, good mobility, convenient secondary transformation, and low cost. Since the 1990s, UAV technology has developed rapidly; new materials have increased the endurance of UAVs; evolved communication technology has improved the data transmission speed of UAVs; advanced flight control technology have enabled UAVs to fly completely or intermittently according to a set program. However, in the above-mentioned UAV network functions, UAVs are only used for cruising, monitoring, transportation and other simple aircraft functions, and the surplus computing and storage resources of its airborne computer are idle. As a mobile air IoT device, the UAV network is the most typical structure of the IoT-Edge network extension. How to provide more computing, storage, and communication resources for IoT devices when UAVs perform flight, monitoring, and edge services is an important research topic to explore and inspire edge intelligence. UAVs with mobile characteristics can become one of the potential choices of mobile edge computing nodes. It is an extension function of a UAV network and it is compatible with edge computing upwards and provides airborne resources downwards. In the new MEC network architecture assisted by customized UAVs, the traditional MEC base station can still exist, and UAVs can also be used as backup base stations when the fixed base station is damaged. In the UAV cluster flight scenario controlled by a ground station and with a change of UAV trajectory, it can also provide a higher-performance in computing offloading or storage services. Therefore, there are more suitable and low-cost mobile edge computing nodes in the key monitoring areas of complex terrain or crowd gathering places such as desert, wilderness, and ocean. This section will discuss the above vision through the following two important research directions:  
_(1) Path planning and obstacle avoidance of customized UAV networking enabled edge intelligence._ As air flight equipment, safe flight is the primary premise and basic guarantee for UAVs to provide many airborne services including computing and offloading.