This paper presents RAPID-TSP, a distributed coordination algorithm for multi-drone scanning missions in dynamic environments. It enables drones to balance area coverage efficiency with network connectivity by leveraging a tunable parameter λ. This parameter allows each drone to adaptively choose its next scanning target based on proximity and the positions of neighboring drones, while aiming to maintain connectivity with a base station for mission updates. RAPID-TSP incorporates conflict resolution and supports two swarming strategies to minimize redundant scanning and ensure efficient collaboration. A realistic scenario involving the scanning of container stacks in a logistics hub serves as evaluation scenario. Extensive simulations show how RAPID-TSP performs robustly under varying conditions and can reduce mission completion time by up to 40% compared to traditional mTSP coordination methods. A key design insight is that mission time is significantly reduced by adding more drones only when communication range is sufficient; otherwise, sparse connectivity limits their effectiveness.

We propose two application-layer coding schemes for delay-constrained point-to-point packet communications with restrictions on the transmitter's maximum duty-cycle. The schemes operate over GF(2) and utilize intermittently available receiver feedback for erasure correction. Applications that will benefit from the proposed schemes include wireless sensor networks in which energy constrained sensors must deliver readings to a gateway within a deadline. Simulation results for independent Bernoulli erasure channels, Gilbert-Elliott channels, and Long Range (LoRa) communications demonstrate orders-of-magnitude reductions in the delivery failure rate as compared to feedback-assisted repetition redundancy and a blind coding scheme that does not utilize feedback.