Um Doosik, Park Hyung-Seok, Ryu Hyunho, Park Kyung-Joon
Department of Interdisciplinary Studies (Artificial Intelligence Major), Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea.
Department of Electrical Engineering & Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea.
Sensors (Basel). 2024 Dec 30;25(1):155. doi: 10.3390/s25010155.
In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.
在工业和军事等关键任务环境中，无人驾驶车辆的使用正在增加。这些场景通常涉及地面控制系统（GCS）以及诸如无人地面车辆（UGV）和无人机（UAV）等节点。GCS和节点交换不同类型的信息，包括指挥无人驾驶车辆移动的控制数据以及捕获现实世界环境状况的传感器数据。GCS和节点通过无线方式进行通信，这会导致控制和传感器数据的丢失或延迟。将这些问题最小化对于确保节点通过无线链路按预期运行至关重要。在动态网络中，分布式路径计算方法会导致网络流量增加，因为每个节点都会独立交换控制消息以发现新路由。这种流量增加会导致内部干扰，从而造成通信延迟和数据丢失。相比之下，软件定义网络（SDN）通过从单个点为所有节点计算路径提供了一种集中式方法，从而减少了网络流量。然而，从分布式方法转向SDN的集中式方法并不能必然保证更快地创建路由。生成新路由的速度仍然独立于该方法是否为集中式，因此SDN并不总是能带来更快的结果。因此，一个关键挑战仍然存在：确定如何即使在SDN框架内也能尽快创建新路由。本文介绍了一种基于SDN中预测链路状态的转发规则缓存技术，该技术被命名为CRIMSON（移动SDN网络中的缓存路由信息）算法。CRIMSON算法检测由节点移动性引起的网络链路状态变化，并基于预测的拓扑变化缓存新的转发规则。我们验证了与传统的反应式和主动式模式相比，CRIMSON算法分别持续将端到端延迟平均降低了88.96%和59.49%。
