# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 1 Introduction
Therefore, we design a new topology control mechanism to maintain the desired density of UAVs around a route. This improves the availability of high-quality (i.e., mobility, congestion, and energy-aware) routes and enables the routing protocol to choose the best route from the available options. We say that a routing protocol is \"mobility-aware\" if it selects long-lasting routes based on the trajectories of nodes [10; 11]. \"Congestion-aware\" routing protocols avoid congested nodes (such as hotspot nodes) [12; 13; 14; 15]. Finally, \"energy-aware\" routing involves selecting nodes above a minimum energy level required to provide stable and long-lasting routes. Figure 2: Challenges in a decentralized and autonomous UAV network, where low SWaP UAVs are tasked to provide fast area coverage while maintaining strong network connectivity, and assist in reliably forwarding sensed data of multiple target UAVs to the BS within the latency constraints. Although strong network connectivity is desired in our application scenario (see Figure 1), not every UAV needs to establish a path to the BS. Therefore, reactive routing protocols (such as [16]) are better suited, which discover a new route on demand and thus incur low control and computation overhead. However, these schemes can trigger frequent route discoveries in a dynamic network topology. In contrast, proactive routing schemes (such as [12]) introduce a large overhead because each node periodically discovers and maintains routes to all other nodes in the network, which is not required in the application scenario considered in Figure 1. Therefore, a hybrid routing protocol should be considered that can select alternate routes (as in the proactive routing protocol) while incurring low overhead (as in the reactive routing protocol). Such state-of-the-art hybrid routing protocol has recently been proposed in [17] for autonomous and decentralized UAV networks; it discovers routes to the BS on demand (like AODV) and also maintains alternate routes within a limited region around current routes. When the quality of a current route degrades, a high-quality route is proactively selected from these alternate routes without discovering a new route. The performance of [17] is much superior to existing reactive routing protocols and closer to proactive routing schemes. However, [17] does not consider the practical challenges that occur in our proposed scenario: (i) It considers a smooth-turn mobility model [18] where each node selects and changes its trajectory without coordinating with its neighbors. Moreover, it is not suited to provide quick area coverage while maintaining strong connectivity both within the network and to the BS; (ii) It does not employ any mechanism to proactively prevent the formation of hotspot nodes when routes serving flows from multiple target UAVs to the BS converge; (iii) The route selection scheme does not consider the residual energy of the UAVs, which is an important metric in battery-constrained UAV networks, nor does it assess the impact of node failure on routing, coverage, and connectivity. We first build our approach on the hybrid routing protocol [17], and design a new mobility-, congestion-, and energy-aware routing protocol called Pipe routing. Then a novel pheromone-based topology control scheme is designed; this works in conjunction with the Pipe routing scheme and is called TC-Pipe routing. Major contributions of the proposed routing protocols include:  
1. The proposed routing protocols are designed to adapt to highly dynamic UAV networks; they also proactively switch to alternate high-quality routes within a limited region around the current route (called the pipe) when needed. 2. Congestion awareness reduces the congestion at hotspot nodes when multiple flows to the BS are present in the network (especially at high data rates). 3. Energy awareness prevents the selection of routes with low-energy UAVs; this avoids disruption of data flows when these low-energy UAVs fail. 4.