Reliability- and Connectivity-Constrained Age-of-Information Optimization for UAV Swarm IoT Data Collection

Uncrewed aerial vehicle~(UAV) swarms provide a flexible platform for Internet of Things~(IoT) data collection by gathering delay-sensitive measurements from distributed sensor clusters and relaying them to a fusion center. Such missions require jointly optimizing data freshness, short-packet communication reliability, and swarm connectivity, yet existing approaches typically prioritize freshness while neglecting its impact on connectivity. This paper proposes a topology-coupled urgency~(TCU) scheduler that weights each cluster's age-of-information urgency by a per-UAV connectivity score, prioritizing stale clusters while dispersing collection across the swarm, with connectivity preserved by an explicit probabilistic constraint. For each time slot, the resulting problem is a mixed-integer nonconvex program, which we solve by successive convex approximation: the binary assignments are relaxed and the finite-blocklength rate, the inter-UAV link reliability and the connectivity score are linearized together into one convex subproblem, so their coupling is retained rather than broken across alternating blocks. Integer assignments are recovered by Hungarian matching and verified against the original nonconvex constraints, while a deficit queue sustains long-run coverage without explicit long-horizon planning. Simulation results demonstrate that the proposed scheduler consistently achieves the lowest mean AoI and the highest service reliability among all baselines, reducing service failures by approximately $3.5\times$ under nominal operating conditions. Under increasingly stringent connectivity and coverage requirements, it maintains full service while competing methods either become infeasible or fragment the swarm, demonstrating the effectiveness of jointly optimizing data freshness, communication reliability, and connectivity.