Footstone of Metaverse: A Timely and Secure Crowdsensing

Abstract

Recently, the metaverse has been a hot research topic that fuses multiple cutting-edge techniques, including virtual reality (VR), augmented reality (AR), artificial intelligence (AI), Internet of Things (IoT), and 5th generation (5G) networks. Supported by these powerful cornerstones, people can establish a hyper spatiotemporal virtual world that seamlessly links to the real world and create personal activities such as art creation, concert display, and stock trading. Virtual-reality mapping demands enormous information transmission between two worlds without sensible latency since delayed data could severely impact user experiences. Besides, intrinsic flaws in fundamental technologies also aggravate the vulnerabilities in the metaverse due to real-time interaction. Hence, this paper first proposes a timely and secure data collection framework based on crowdsensing for metaverse modeling. In our proposed Data encryption, Transmission and Perception optimization (DTP) model, the metaverse servers can recruit workers to gather environmental parameters and user information through five layers architecture (i.e., perception layer, link layer, transmission layer, operation layer, and application layer). Each layer takes responsibility for data computation and encryption, which can stimulate data dissemination in a fast and reliable manner for the metaverse. Furthermore, we present a healthcare use case that introduces a bidirectional mapping of patient data between physical and virtual space. This use case illustrates the prospects of the DTP model. Finally, we conclude this paper and summarize several significant future research directions.