Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks
Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks
复制标题
DOI：
10.1109/tnse.2021.3085514
复制
发表时间：
2021-07-01
影响因子：
6.6
通讯作者：
Razi, Abolfazl
中科院分区：
文献类型：
--
作者：
Rovira-Sugranes, Arnau;Afghah, Fatemeh;Razi, Abolfazl
目前的网络协议在解决无人机网络的两个关键挑战方面效率低下，即网络连接丢失和能量限制。解决这些问题的一种方法是使用基于学习的路由协议来由网络节点做出接近最优的本地决策，Q路由是这种协议的一个大胆的例子。然而，目前实现的Q路由算法的性能还不令人满意，主要是由于缺乏适应性，不断的拓扑变化。在本文中，我们提出了一个全回波Q路由算法与自适应学习率，利用模拟退火（SA）优化控制的探索率的算法，通过温度下降率，这反过来又是调节的经验变化率的Q值。我们的研究结果表明，我们的方法适应网络的动态性，而不需要手动重新初始化的过渡点（突然的网络拓扑变化）。我们的方法表现出减少的能量消耗范围从7%到82%，以及成功的数据包传输率的2.6倍的增益，相比最先进的Q-路由协议。
Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-to-optimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a full-echo Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate, compared to the state of the art Q-routing protocols.