By pushing computation, cache, and network control to the edge, mobile edge computing (MEC) is expected to play a leading role in fifth generation (5G) and future sixth generation (6G). Nevertheless, facing ubiquitous fast-growing computational demands, it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments (UEs). To address this issue, we propose an {a}ir-{g}round {c}ollaborative {MEC} (AGC-MEC) architecture in this article. The proposed AGC-MEC integrates all potentially available MEC servers within air and ground in the envisioned 6G, by a variety of collaborative ways to provide computation services at their best for UEs. Firstly, we introduce the AGC-MEC architecture and elaborate three typical use cases. Then, we discuss four main challenges in the AGC-MEC as well as their potential solutions. Next, we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy. Finally, we highlight several potential research directions of the AGC-MEC.
The increasing dependence on data highlights the need for a detailed understanding of its behavior, encompassing the challenges involved in processing and evaluating it. However, current research lacks a comprehensive structure for measuring the worth of data elements, hindering effective navigation of the changing digital environment. This paper aims to fill this research gap by introducing the innovative concept of "data components." It proposes a graph-theoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods. Additionally, the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value. The paper also assesses the value of information, suggesting a pricing mechanism based on its significance. In conclusion, this paper establishes a robust framework for understanding and quantifying the value of implicit information in data, laying the groundwork for future research and practical applications.
