The study of DDPG based spatiotemporal dynamic deployment optimization of Air-Ground ad hoc network for disaster emergency response

Abstract

In situations where natural disasters damage public communication networks, self-organized emergency communication networks play a vital role as important resources for disaster monitoring and emergency response. Geographical conditions, communication capacity, power availability, terminals’ position changes during disasters, and data volume, on the other hand, have a direct impact on emergency terminals’ effectiveness in disaster monitoring and data processing. As a result, the ability to detect calamities quickly and give real-time reactions suffers. To ensure effective emergency communication coverage when public networks are disrupted, intelligent, rapid and reliable optimization of the spatiotemporal dynamic deployment of Ad Hoc Network (ANET) is crucial for disaster monitoring and emergency response. The Deep Deterministic Policy Gradient (DDPG) method is used in this study to assist the spatiotemporal dynamic deployment of air-ground ANET (AGANET) nodes, including unmanned aerial vehicle (UAV) ANET nodes and ground-based ANET nodes. Its goal is to develop a reliable model for ensuring the deployment of AGANET nodes for multi-terminal disaster monitoring. The paper describes the spatiotemporal dynamic deployment of AGANET nodes and develops a reinforcement learning model. Subsequently, it describes a DDPG reinforcement-based optimization method for the spatiotemporal dynamic deployment of AGANET nodes. Specifically, this method includes a greedy matching strategy based on real-time environmental information of AGANET nodes and ground-based sensing terminals, a DDPG AGANET node three-dimensional initialization algorithm, and a DDPG dynamic solution algorithm for spatiotemporal dynamic deployment reinforcement learning model of AGANET. AGANET nodes and ground-based sensing terminals can all work together in this way to provide high-quality emergency AGANET services. When compared to traditional methods, it offers better communication dependability, enhanced efficiency, and cheaper costs associated with building emergency networks. It is also crucial for emergency response in instances where the public network fails.

Keywords

• Deep Reinforcement Learning
• Ad Hoc Network
• Spatiotemporal Optimization
• Artificial Intelligence
• Disaster Monitoring
• Emergency Response