# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 1 Introduction  
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## 1 Introduction  
Unmanned aerial vehicles (UAVs) equipped with self-localization and sensing capabilities have become popular for applications such as area monitoring, surveillance, search and rescue, and target tracking [1, 2, 3, 4, 5]. In this paper, we consider low SWaP (size, weight, and power) fixed-wing UAVs, which offer a balance between portability and area coverage, with higher speeds and longer flight times compared to rotor-based UAVs [4, 5]. However, these UAVs face practical challenges, such as limited battery life and communication range and increased susceptibility to failure. In Figure 1, we consider a network of low SWaP fixed-wing UAVs deployed in an inaccessible area without any fixed communication infrastructure, such as a cellular network. Here, a decentralized and autonomous network can provide scalability and resilience against single point failures. UAVs in this network coordinate with their neighbors to explore the area, perform local sensing, identify targets of interest, and support communication without global knowledge of network topology [5]. Examples of targets could be burning buildings, damaged bridges, or first responders in disaster areas that require continuous monitoring [6]. When a target is found, the UAV responsible for monitoring it (called the target UAV) transmits the target information to the base station (BS) in real time over a multihop route [7]. Meanwhile, the remaining UAVs collaboratively search the area for new targets while maintaining network connectivity and reliable communication routes to the BS. A reliable routing protocol is critical to ensure the seamless transmission of sensed information from the target UAV to the BS in real time. However, maintaining high-quality routes in a decentralized and autonomous UAV network is challenging due to the highly dynamic network topology, sparse network connectivity in some areas, fluctuating traffic, limited battery life, and potential node failures [5, 8, 9]. Moreover, when multiple targets are detected, the routes from the target UAVs to the BS converge; this creates hotspot nodes (as shown in Figure 2), which can become critical points of failure and experience increased congestion. These issues can result in packet drop due to route breaks (caused by frequent changes in network topology and/or node failure) and expir  
Figure 1: Illustration of a decentralized and autonomous UAV network for remote monitoring of an inaccessible area where a communication infrastructure is not available. Here, the UAVs collaborate to provide robust routes for transmitting the sensed information of ground-based targets to a base station while performing fast area coverage. This, in turn, triggers frequent route discoveries, incurring even higher control (signaling) and computational overhead and delay. To address these challenges, a holistic approach is needed that integrates the mobility model, topology control, and routing protocols to establish reliable and high-quality routes to the BS while ensuring fast area coverage. Since pheromone-based mobility models [3] can achieve rapid area coverage autonomously in a decentralized manner, we use our recent pheromone-based connectivity-aware BS-CAP mobility model [7], which provides fast area exploration along with strong network connectivity for target monitoring. Note that UAVs, while searching the area for new targets, should also assist the target UAVs in maintaining stable high-quality route(s) to the BS. Although the BS-CAP mobility model achieves strong network connectivity, situations may arise where a target UAV does not have any alternative high-quality backup route, which could be needed when the current route breaks, or an intermediate node fails, or congestion builds up suddenly. To achieve robust and reliable communication between a target UAV and the BS in such cases, a topology control mechanism is required to alleviate the impact of congestion and route failures.