# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value  
There are many types of sensors, such as thermosensitive, photosensitive, gas-sensitive, force-sensitive, magneto-sensitive, humidity-sensitive, acoustic, radiation-sensitive, color, and taste sensors. Specifically, radar, sonar, other acoustics, infrared/thermal imaging camera, TV cameras, sonobuoys, seismic sensors, magnetic sensors; electronic support measures (ESMs), phased array, MEMS, accelerometers, and global positioning system (GPS) are included. These sensors produce diversified types of data, such as text, video, infrared/image, ultrasonic, two-dimensional radar scan, lidar point cloud, gas, and temperature and humidity. After the sensor data are acquired, possible application scenarios can be smart electricity meters, quality evaluation, security monitoring, and other services directly deployed on edge network or intelligent services deployed in the cloud, including smart cities, autonomous driving, knowledge mining, privacy protection, and pricing mechanisms. However, to realize these services, the existing intelligent IoT architecture based on data fusion is faced with the following challenges:  
* _Multi-modal data IntelliSense._ Currently, optimization of the underlying data-acquisition device is required by an increasing number of big-data platforms with the purpose of using richer sensors to obtain more diverse data. A system that relies on a single sensor for data acquisition is likely to have the following problems: (1) Due to the limited sensor performance, the perception range is limited and the data type is single. (2) There can be a loss of data due to the failure of a sensor at some perceptual point. (3) There can be a system failure because a sensor is not available. (4) There is one-sidedness for perception due to single-perspective information. Therefore, the adoption of a multi-modal sensor would be essential to solve the above problems. Compared with a single sensor, multi-modal sensors have the advantages of multi-dimensional perception, multi-authentication, and enhanced information security [43]. Nonetheless, systems with multi-modal sensors also need to address the following challenges: (1) How to expand the breadth and depth of the space coverage, massiveness, and diversification of ready data. (2) How to enhance the robustness in the architecture of the intelligent IoT, sensor reliability, and data security. (3) How to ensure the time uniformity of data perception. (4) How to solve the computing and communication complexity of the multi-dimensional perception system. (5) How to avoid information uncertainty caused by multivariate data;
* _Edge-network intelligence._ The edge network is composed of smartphones, tablets, computers, and various sensor nodes connected to the core network through the edge computing carrier [44]. Heterogeneous edge-computing carriers include the edge server, nanocluster, edge gateway, raspberry PI, base-station, and other access points. The functions of the edge network include providing device access and network management for hardware facilities, providing data acquisition and storage for application services, and providing model-based reasoning and algorithm decision-making for local computing. To make the edge network more intelligent, a combination of AI technology and edge networks is the future direction in intelligent edge networks. Data generation, data acquisition, and data transmission are three important challenges for edge-network intelligence. (1) Data generation can be realized through the fusion of multi-modal data IntelliSense and data-generation enhancement technology. (2) Data acquisition includes necessary processes of sensor fusion, such as data cleaning, data filtering, data integration, data conversion, and data pre-processing [45].