# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 2 Related Work  
As discussed in Section 1, finding high-quality routes in a decentralized and autonomous UAV network is challenging due to the highly dynamic network topology, the lack of network connectivity in some areas, fluctuating traffic, limited battery, and potential node failures [5; 8; 9]. These issues can result in frequent route breaks and increase the route discoveries, incurring high overhead and delay. In addition, most existing routing schemes cannot update the routing table or discover a new route before the current route is broken, otherwise they incur a high overhead in predicting such route breaks [12; 17]. For example, proactive routing schemes (such as optimized link state routing (OLSR) and its variants [12; 19]) introduce a large overhead because each node periodically transmits control messages to maintain routes in the network [4; 5; 12]. Since only the target UAVs establish routes to the BS in our application scenario, reactive routing schemes that discover a new route only on demand and thus incur low control and computation overhead are better suited [9; 10; 16; 20; 21; 22; 23; 24; 25; 26], and are reviewed below. Reactive Routing SchemesReactive routing schemes introduce longer route discovery delays when routes break frequently in a dynamic UAV network. Many reactive routing schemes [5; 16] suffer from the route request (RREQ) control packet flooding problem, known as \"broadcast storms\" [9]. Although some schemes [9; 10; 22] explicitly try to reduce the overhead and delay caused by frequent route discoveries, this often comes at the cost of using the best information. For example, in [22], when a route breaks, the intermediate nodes search for an alternate route only in their respective k-hop neighborhood; this reduces control overhead by avoiding network-wide route discovery by the source node. In [10], RREQ flooding is limited by only forwarding RREQ messages to certain nodes identified in different zones based on the location and connectivity of neighboring nodes. However, since the full network topology is not used for route discovery, these schemes can result in the selection of poor routes. Many applications are time-sensitive (e.g., video streaming) and demand a certain bandwidth level from the network. QoS (quality of service)-aware routing schemes [17; 23; 24; 27] use network statistics such as node mobility, link stability, hop count, signal strength, node energy level, congestion, and traffic load to select the best route. In one such scheme [23], RREQ is rebroadcast by only those nodes that meet specific QoS thresholds such as time to live (TTL), bandwidth, and flow jitter. However, this protocol does not estimate or track the node mobility and link stability, leading to frequent route breaks. In [24], an intermediate node in the route can adapt to changes in network topology and traffic conditions by selecting a new downstream node (toward the destination). This selection is based on neighbor distance, latency, traffic load, and reliability. However, this scheme can sometimes result in longer and less efficient routes. An energy-aware variant of AODV [27] uses the link stability and residual energy of the nodes to select stable routes. Multipath reactive routing protocols [21; 25] find multiple node-disjoint or link-disjoint paths during route discovery and cache them as backup routes. When the current route quality degrades, the backup routes from the cache are used instead of performing a new route discovery. However, like the current route, the quality of the backup routes also typically declines over time. To avoid selecting bad routes, the scheme in [26] periodically monitors the quality of the backup routes. Whereas, the scheme in [9] performs local route repair using route recovery messages and uses fuzzy logic route selection based on node mobility, residual energy, and link quality. The link stability-based reactive routing protocol [20] uses a semi-proactive route switching mechanism, which reduces the route discovery overhead incurred in [9; 26].