## 2 Research Background and Motivation  
Currently, in practical emergency rescue scenarios, three methods are predominantly used to establish dedicated networks: mesh self-organizing networks [6], cluster communications, and specialized network devices. First, biological infrared detection devices,acoustic sensors, and other IoT sensing devices are connected via a mesh self-organizing network. Then, a small number of aggregation nodes are deployed within the network, through which each IoT sub-node transmits the detected data to the upper-level network. Second, in scenarios where communication and power supply are interrupted, mobile communication networks usually cannot operate normally; therefore, cluster base stations are often set up on-site to handle single calls, group calls, and cluster communications for all networked walkie-talkies. Third, terminals carried by rescue personnel can effectively monitor the location, vital signs, and behavioral posture of the rescue personnel, connecting through the lightweight MQTT (Message Queuing Telemetry Transport) network. Although various devices and systems can provide good services, the requirements of rescue missions increase, the quantity and variety of required equipment grow, and the network standards vary, making it difficult for different subsystems to effectively integrate within the same rescue scenario. This poses serious challenges to disaster data fusion, efficient information dissemination, and disaster situation analysis. In response to these challenges, the existing research mainly follows three technical approaches: first, optimizing at the self-organizing network protocol level to improve network compatibility, reliability, and security [7; 8]; second, focusing on the algorithmic level for drone payload functions to enrich system capabilities and broaden mission scenarios [9; 10]; third, working on the network architecture level to enhance the efficiency of drone mission execution as a whole [11; 12]. While these efforts largely meet the needs of emergency rescue missions, the protocol-level work tends to overlook the latency sensitivity of rescue tasks, and the algorithmic-level work is somewhat specialized, catering to specific domains such as visual or radar signals, neglecting the common needs of data fusion. Network architecture-level work has a certain degree of generality but is often based on certain limitations, whereas real-world scenarios are multifaceted. Motivated by this, we design a general system architecture that is network-simple, flexibly modular, and elastically powerful, propose the GLSOAG, and conduct joint optimization of energy consumption and latency, two key factors constraining rescue missions. Simulation experiments show that, under different task loads, the GLSOAG-based method can achieve certain advantages compared to other methods. Although various types of equipment and systems can better provide the corresponding services, with the rescue mission needs, the number and types of equipment and devices required are greater and greater, and the network system is also different; with the various components of the system in the same rescue scenarios, it is difficult to be effectively compatible with disaster data fusion, disaster situation analysis and prediction, and other challenges. For this reason, based on the full study of the 5G and edge computing system architecture, a system architecture with a simple network, flexible splicing, and elastic arithmetic is designed. As shown in Figure 1, 5G mobile communication networks and edge computing can be integrated in the User Plane Function (UPF). Based on this theoretical foundation, in the system architecture designed in this paper, MEC is uniformly deployed based on macro base stations, micro base stations, or vehicle-mounted base stations. This avoids transmission latency between servers and network access, enhancing the efficiency of task processing.