面向高难度高山道路、隧道和桥梁环境中更可靠卡车追踪的自主车辆网络
Autonomous Vehicle Networks for More Reliable Truck Tracking in Challenged High Mountain Roads, Tunnels and Bridges Environments
AI总结：
针对山路、隧道等复杂环境中快递追踪不稳定问题，本文提出雅安场景并验证机会性DTN感知协议可实现卡车高消息投递率和低延迟的稳定通信。
摘要：
网络购物的普及对现有快递追踪提出了挑战。如何为客户提供可靠且稳定的快递追踪已成为快递公司当前需要解决的重要问题之一。现阶段，在山路、隧道和城市中心等具有挑战性的环境中，快递追踪效果并不理想。因此，本项目旨在克服具有挑战性的环境并实现稳定的快递追踪，提出了雅安场景并进行了多次实验。我们表明，机会性DTN感知协议是卡车在具有挑战性环境中保持稳定通信的可行解决方案，节点保持极高的消息投递率和能够维持通信的平均延迟。
英文摘要：
The popularity of online shopping has challenged the existing express tracking. How to provide customers with reliable and stable express tracking has become one of the important issues that express companies need to solve now. The current stage of courier tracking is not ideal in challenging environments such as mountain roads, tunnels and city centres. Therefore, the project aims to overcome the challenging environment and achieve stable express tracking, and proposes the Ya'an scenario and conducted multiple experiments. We show that opportunistic DTN-aware protocols are feasible solution for trucks to maintain stable communication in challenging environments, and nodes maintain extremely high message delivery rates and average delays that can maintain communication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
