The continuous evolution of commercial Unmanned Aerial Systems (UAS) is fuelling a rapid advancement in the fields of network edge-communication applications for smart agriculture, smart traffic management, and border security. A common problem in UAS (a.k.a. drone systems) research and development is the cost related to deploying and running realistic testbeds. Due to the constraints in safe operation, handling limited energy resources, and government regulation restrictions, UAS testbed building is time-consuming and not easily configurable for high-scale experiments. In addition, experimenters have a hard time creating repeatable and reproducible experiments to test major hypotheses. In this paper, we present a design for performing tracebased NS-3 simulations that can be helpful for realistic UAS simulation experiments. We run experiments with real-world UAS traces including various mobility models, geospatial link information and video analytics measurements. Our experiments assume a hierarchical UAS platform with low-cost/high-cost drones co-operating using a geo-location service in order to provide a ‘common operating picture’ for decision makers. We implement a synergized drone and network simulator that features three main modules: (i) learning-based optimal scheme selection module, (ii) application environment monitoring module, and (iii) trace-based simulation and visualization module. Simulations generated from our implementation have the ability to integrate di↵erent drone configurations, wireless communication links (air-to-air; air-to-ground), as well as mobility routing protocols. Our approach is beneficial to evaluate network-edge orchestration algorithms pertaining to e.g., management of energy consumption, video analytics performance, and networking protocols configuration.
Chengyi Qu, Alicia Esquivel Morel, Drew Dahlquist, and Prasad Calyam, "Design of trace-based NS-3 simulations for UAS video analytics with geospatial mobility," Proc. SPIE 11398, Geospatial Informatics X, 1139809 (Presented at SPIE Defense + Commercial Sensing: April 28, 2020; Published: 27 April 2020); https://doi.org/10.1117/12.2563018.
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