Edge Intelligence for Mission Cognitive Wireless Emergency Networks
Li Wang, Jun Zhang, Jianbin Chuan, Ruqiu Ma, Aiguo Fei
Abstract
Emergency communication infrastructures are of critical importance in disaster rescue scenarios, responsible for providing reliable connection services among victims, rescuers, and public safety command centers. Moreover, many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing, and the availability of low-latency computation platforms. In this article, we propose an edge intelligence-based MCWEN to address these challenges, by leveraging edge-based technologies including edge caching, edge computing, and edge learning. In particular, MCWEN showcases a three-layer architecture, consisting of end devices, edge servers, and remote clouds. Intelligent mission cognition involves the understanding of key features of various tasks, as well as the environment and available rescue resources, and it plays an essential role in the MCWEN. We highlight edge-assisted approaches to support the key functionalities of MCWEN, including data collection, information extraction, and decision making. Typical application examples are provided to illustrate the practical significance of the proposed MCWEN framework.