Abstract
A swarm of unmanned aerial vehicles (UAVs) requires the transmission of mission-related data across the network. The resource constraints and dynamic nature of the swarm bring critical challenges to the design of UAV routing protocols. Most of the conventional ad hoc routing schemes are not intelligent and cannot adapt to the dynamic nature of UAV swarming networks. On the other hand, some artificial intelligence (AI)-based routing schemes may consume significant computational resources in the UAVs. In this article, a low-cost, adaptive routing protocol, namely skeleton-based swarm routing (SSR) , is proposed, which exploits an intelligent online learning algorithm and the topology features of the mission-driven UAV swarm to distribute the traffic over optimal routes. Here, the skeleton represents the most stable parts of the swarm formation. SSR architecture consists of three modules: 1) A geometric addressing module, which assigns geometric coordinates to each node based on the swarm skeleton structure; 2) A leaf-like routing pipe which allows the selection of multiple candidate routes around the shortest path; 3) An intelligent low-complexity learning model which determines how to distribute the packets inside the routing pipe to achieve load-balanced, high-throughput transmissions. The proposed skeleton-based scheme can also facilitate the UAV formation construction and morphing. The simulation results show that the proposed SSR protocol can noticeably improve the network performance (up to 100% throughput improvement) compared to the single path routing schemes, such as the ad-hoc on-demand distance vector (AODV) and link-quality and traffic-load aware optimized link state routing (LTA-OLSR) protocols.
Highlights
The airborne networks composed of unmanned aerial vehicles (UAVs), as illustrated in Fig. 1, have been deployed in different civilian, commercial and military applications, such as disaster management, border surveillance, search and rescue opertions, goods delivery, etc. [1], [2]
A low-cost, adaptive routing protocol, namely skeleton-based swarm routing (SSR), is proposed, which exploits an intelligent online learning algorithm and the topology features of the mission-driven UAV swarm to distribute the traffic over optimal routes
SSR architecture consists of three modules: 1) A geometric addressing module, which assigns geometric coordinates to each node based on the swarm skeleton structure; 2) A leaf-like routing pipe which allows the selection of multiple candidate routes around the shortest path; 3) An intelligent low-complexity learning model which determines how to distribute the packets inside the routing pipe to achieve load-balanced, high-throughput transmissions
Summary
The airborne networks composed of unmanned aerial vehicles (UAVs), as illustrated in Fig. 1, have been deployed in different civilian, commercial and military applications, such as disaster management, border surveillance, search and rescue opertions, goods delivery, etc. [1], [2]. They do not need the periodic update of the routing tables and the information about the entire network link states Instead, they depend on the geographic location of the nodes for greedy (distance-based) forwarding. Similar to [25], [26], it is a load-balanced routing scheme, but does not require frequent update of the link state information required by proactive approaches It requires the distribution of the updated geometric address table, which is not very frequent in the formation-based UAV network, considered in this article. At the very bottom layer, geometric addressing system provides the approximate location of each node The formation information, such as the length and the angle of the bones, is distributed through the skeleton by the leader via mission command messages, helping to construct and morph the formation. Mi+1 can be found by using the equations below:
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