The evolution of network densification, from terrestrial ultradense networks (UDNs) to aerial deployments of uncrewed aerial vehicles (UAVs), intensifies interference from line-of-sight (LoS) links while imposing stringent energy constraints, making resource optimization critical. The joint power and admission control (JPAC) problem is fundamental to achieving efficient network services, but traditional algorithms face performance limitations in the high-dimensional sparse scenarios created by these large-scale networks. To address this, this article proposes a novel algorithm, adaptive sieving with gradual removals (ASGRs), inspired by the adaptive sieving (AS) method. While AS focuses solely on admitting links, ASGR enhances this process by incorporating a gradual removal mechanism. This key difference allows ASGR to proactively prune unpromising links, leading to higher quality feasible solutions. Both theoretical analysis, which establishes a complexity of $O(U \log ^{3} U)$ in sparse scenarios, and experimental results confirm that ASGR achieves a significant computational speedup over mainstream algorithms. The proposed algorithm attains this acceleration while achieving comparable results for total transmission power and the number of admitted links, and its comprehensive performance remains competitive in general scenarios.

In our work, the Robust Multiple-robot Orienteering Problem with Workload Balancing is constructed for the first time. Our primary contribution lies in the rigorous formulation of this problem as a three-stage optimization task. It leverages the Robust Multiple-robot Orienteering Problem (RMOP) as the initial stage. The Path Replanning stage and the workload balancing stage are introduced to minimize walk redundancy and achieve workload equilibrium. The resultant solution upholds the optimality inherent to the original RMOP. Additionally, we craft a suite of heuristic strategies to mitigate redundancy and employ Monte Carlo sampling to tackle the problem. Our algorithm analysis indicates that the method has asymptotic convergence properties and a feasible time complexity under certain conditions. Local parallelization of the algorithm can further improve its performance. Our simulation studies demonstrate that our approach can efficaciously attain a balance between robustness and workload without compromising performance in the presence of adversarial challenges.

Active area protection against unauthorized UAV swarms requires coordinated target assignment strategies that account for both low-level physical capabilities and the stochastic, consumptive nature of physical interceptions. This paper presents a decentralized target assignment framework based on probabilistic flow optimization. By utilizing an aerodynamics-aware flight model, we derive a probabilistic prior to capture the geometric dependency of terminal interception success under high-velocity maneuvers. Modeling the defense process as a probabilistic consumption flow couples initial tactical assignments with conditional transition flows, allowing surviving defensive assets to be proactively redistributed to secondary unauthorized intrusions. To resolve this problem under practical communication and sensing constraints, we develop the Distributed Flow-regularized Market-based Consensus (DFMC) algorithm. The proposed algorithm decomposes the global optimization into localized subproblems and employs a water-filling projection to plan secondary paths. Simulation results demonstrate that the proposed framework yields improved interception rates and better spatial resource dispersion compared to conventional auction-based baselines, while maintaining stable scalability in dense interception scenarios.