We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks . We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times , are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences.

We propose a novel service-based interference resolution framework for drones operating in a shared skyway network. This network consists of interconnected line-of-sight segments between designated building rooftops that serve as charging and delivery stations for drones. We propose an approach that effectively and efficiently mitigates delays in service delivery caused by interference between drones operating in close proximity within skyway segments. We use a range of constraints to determine the likelihood of impactful interferences to map out proximity distances for the safe and efficient delivery of drone services. Experimental results conducted on real-world data validate the effectiveness of the proposed approach.

We propose a novel service-based framework for drone service resilience. Our framework monitors inter-drone interference that may lead to drone service failure. We present a novel drone service interference taxonomy to formally identify different interference types in a skyway network. We then propose a heuristic-based approach that leverages spatio-temporal proximity analysis to detect the occurrence of inter-drone interference. In addition, we present an interference severity assessment to quantify their impact on drone services' efficiency. We conduct a set of experiments using real-world datasets to evaluate the effectiveness and efficiency of our proposed approach. The results indicate that the proposed heuristic-based approach detects the occurrence of inter-drone interferences with an accuracy of 95%. In addition, the proposed method is$\approx$70% more efficient than the baseline exhaustive approach and$\approx$48% faster than the K-means approach.