# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 9 Conclusions  
We studied a decentralized and autonomous UAV network of low SWaP UAVs tasked with monitoring inaccessible areas with no communication infrastructure to find ground-based targets, and maintain high-quality multihop routes for real-time data communication with the BS. These networks face challenges such as a highly dynamic network topology, lack of network connectivity in some areas, fluctuating traffic, limited battery, and node failures. In addition, the convergence of routes from multiple target UAVs to the BS creates hotspot nodes, leading to congestion. To address these issues, we proposed two routing schemes: Pipe routing and TC-Pipe routing. Both are mobility-, congestion-, and energy-aware, discover routes on demand, and proactively switch to alternate high-quality routes within a limited region around the routes (called 'pipe'). However, the Pipe routing scheme lacks a topology control mechanism and, therefore, can face route unavailability. This issue is resolved in the TC-Pipe routing scheme, which incorporates a topology control mechanism. For diverse network and traffic conditions (including UAV density and speed, number of targets, and fault tolerance), the proposed schemes outperformed the AODV and ConCov-Pipe schemes in terms of number of route breaks and PDR. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal. TC-Pipe scheme further improved the routing performance over Pipe routing but showed slightly reduced coverage at low UAV density. Compared to the Relay routing scheme, which uses dedicated UAVs as relays, both proposed schemes achieved a better coverage and connectivity trade-off. In addition, both schemes showed a graceful degradation in PDR and coverage performance as node failures increased. Thus, both the TC-Pipe and Pipe routing schemes are well suited for decentralized and autonomous UAV networks performing target monitoring and area coverage applications. Future extensions of the proposed schemes could include the use of multiple base stations to improve network resiliency, dynamic environments with mobile targets, time-varying external interference, channel- and transmit-power-aware topology control, and different flow priorities. Compressed sensing techniques and sparse Bayesian learning [48] could also be used to construct radio and pheromone environment maps for low-densityUAV settings to improve topology control, UAV distribution within the pipe and route selection for improved routing performance. To achieve fairness in area coverage and route stability, UAV trajectory planning and channel resource allocation frameworks, like those developed using clustered nonorthogonal multiple access (C-NOMA) in [49], could be considered. Conceptualization, S.D., S.G., A.I., E.S.B. and S.K. ; methodology, S.D., S.G., S.K. and A.I. ; software, S.D. and S.G.; validation, S.D. and S.G; formal analysis, S.D. and S.G; investigation, S.D. and S.G; resources, S.D. and S.G; writing--original draft preparation, S.D. and S.G; writing--review and editing, S.D., S.G., S.K. and A.I. ; visualization, S.D. and S.G; supervision, S.K. and A.I. ; project administration, S.K. and E.S.B. ; funding acquisition, S.K. and E.S.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the U.S. Air Force Research Laboratory (AFRL), under contract Nos. FA8750-20-1-1005 and FA8750-23-C-0521. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors acknowledge the U.S. government's support in the publication of this article. This material is based on work funded by the U.S. Air Force Research Laboratory (AFRL), under contract Nos. FA8750-20-1-1005 and FA8750-23-C-0521. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of AFRL or the US government. The authors declare no conflicts of interest.