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Improving aerial image transmission quality using trajectory-aided OLSR in flying ad hoc networks
EURASIP Journal on Wireless Communications and Networking ( IF 2.6 ) Pub Date : 2020-07-02 , DOI: 10.1186/s13638-020-01707-3
Chen Hou , Zhexin Xu , Wen-Kang Jia , Jianyong Cai , Hui Li

UAVs have been widely used in various applications. Auto coordination of multiple UAVs through AI or mission planning software can provide significant improvements in many applications, including battlefield reconnaissance, topographical mapping, and search and rescue missions. Under such circumstances, the trajectory information is known for a set amount of time, and the system’s performance relies on the network between UAVs and their base. Here, a new protocol is proposed that takes the trajectory of UAVs as a known factor and uses it to improve optimized link state routing (OLSR). In this protocol, Q-learning is adopted to find the best route for the system. Additionally, a packet forwarding arrangement is described that addresses the common problem of deteriorating image quality often faced by UAVs. The simulation results show significant improvements over OLSR and GPSR under a sparsely distributed scenario, with the packet delivery ratio improved by over 30% and over 40 s reduction in the end-to-end delay.



中文翻译:

在飞行自组织网络中使用轨迹辅助OLSR提高航空影像传输质量

无人机已广泛用于各种应用中。通过AI或任务计划软件对多个UAV进行自动协调,可以在许多应用中提供重大改进,包括战场侦察,地形图以及搜索和救援任务。在这种情况下,轨迹信息在一定的时间内是已知的,并且系统的性能取决于UAV及其基座之间的网络。在此,提出了一种新协议,该协议将UAV的轨迹作为已知因素,并使用它来改进优化的链接状态路由(OLSR)。在此协议中,Q-learning被用来找到系统的最佳路由。另外,描述了一种分组转发装置,其解决了UAV经常面临的图像质量下降的普遍问题。仿真结果表明,在稀疏分布的情况下,与OLSR和GPSR相比有显着改进,数据包传递率提高了30%以上,端到端延迟减少了40 s以上。

更新日期:2020-07-02
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