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Experiences, Results, and Reflections from Real-World Experiments with FANETs and UAVs - Extended Version

Core Concepts
This work shares experiences and results obtained during the construction and active use of a testbed for comparing simulations and field tests of drone coordination algorithms in Flying Ad-Hoc Networks (FANETs).
This paper discusses the importance of verification and validation in academic research, particularly in the context of drone coordination and Flying Ad-Hoc Networks (FANETs). It presents the GrADyS framework, which combines simulation, real-world drone coordination, and sensor network deployment to enable comprehensive testing and validation of distributed coordination algorithms. The key highlights and insights from the paper are: Simulation is widely used to verify proposals, but real-world validation is crucial to ensure the reliability and credibility of research results. The GrADyS framework integrates simulation, drone coordination, and sensor network deployment to enable comprehensive testing of distributed coordination algorithms. Experiences from real-world experiments highlight the challenges of logistics, battery management, radio communication, and data synchronization when transitioning from simulations to the real world. Results from experiments using 802.11s mesh networks, 802.15.4 broadcast-based networks, and 802.11 ad-hoc networks demonstrate the importance of carefully selecting communication technologies to match the requirements of the application. The lessons learned from real-world experiments can help researchers better plan and execute field tests, leading to more robust and reliable research outcomes.
Drone flight speeds of 5 m/s and 10 m/s were tested. The testbed covered an area of 10 hectares. 60 ESP32-based sensor nodes were deployed in the field.
"Simulations are important, but beyond them, there is also a need for real-world tests to validate the proposals and enhance results." "Field experiments involving drones and FANETs are not trivial, and this work aims to share experiences and results obtained during the construction of a testbed actively used in comparing simulations and field tests."

Deeper Inquiries

How can the GrADyS framework be extended to support more diverse vehicle types, such as ground robots or underwater vehicles, to enable multi-domain coordination experiments?

The GrADyS framework can be extended to support more diverse vehicle types by incorporating additional communication protocols and hardware interfaces that cater to the specific requirements of ground robots or underwater vehicles. For ground robots, the framework can integrate protocols like ROS (Robot Operating System) for seamless communication and coordination. This would involve developing modules that can interface with the sensors and actuators commonly found in ground robots. For underwater vehicles, specialized communication protocols such as acoustic modems can be implemented to enable communication in underwater environments. The framework would need to include modules for handling the unique challenges of underwater communication, such as high latency and limited bandwidth. Furthermore, the GrADyS framework can be designed to be modular and extensible, allowing researchers to easily integrate new vehicle types by developing custom modules for communication, control, and data exchange. By providing a flexible architecture, researchers can adapt the framework to different vehicle types and experiment scenarios, enabling multi-domain coordination experiments seamlessly.

How can the data synchronization and logging challenges encountered in the real-world experiments be addressed through novel hardware or software solutions to improve the reliability and reproducibility of the results?

To address the data synchronization and logging challenges encountered in real-world experiments, novel hardware and software solutions can be implemented within the GrADyS framework. One approach is to incorporate Real-Time Clock (RTC) modules with battery backup in the companion computers or SBCs used in the experiments. This would ensure accurate timestamping of data, even in the absence of an internet connection for time synchronization. Additionally, developing custom synchronization algorithms that leverage GPS data for time synchronization can enhance the accuracy of data logging across multiple nodes. On the software side, implementing robust logging mechanisms that prioritize data integrity and synchronization can improve the reliability and reproducibility of results. This may involve developing custom logging libraries that handle data storage, retrieval, and synchronization efficiently. Furthermore, utilizing distributed logging systems that centralize data collection from multiple nodes in real-time can streamline the data analysis process and ensure consistency in the recorded data. By combining hardware solutions for accurate timestamping and software solutions for efficient data logging and synchronization, the GrADyS framework can overcome the challenges associated with real-world experiments, enhancing the reliability and reproducibility of the results.

What are the potential challenges and trade-offs in using commercial off-the-shelf drones versus custom-built platforms for real-world FANET experiments?

Using commercial off-the-shelf (COTS) drones for real-world FANET experiments offers advantages in terms of cost-effectiveness, ease of deployment, and availability of technical support. However, there are several potential challenges and trade-offs to consider when comparing COTS drones with custom-built platforms: Customization: COTS drones may have limited customization options compared to custom-built platforms. Custom-built platforms allow researchers to tailor the hardware and software components to specific research requirements, enabling more flexibility in experiment design. Hardware Limitations: COTS drones may have hardware limitations that restrict the integration of additional sensors or communication modules. Custom-built platforms offer the flexibility to incorporate specialized hardware components for advanced functionalities. Software Control: COTS drones often come with proprietary software that may limit the level of control and customization. Custom-built platforms allow researchers to develop and implement custom control algorithms and communication protocols tailored to the research objectives. Reliability: COTS drones are designed for general consumer use and may not offer the same level of reliability and robustness as custom-built platforms built with high-quality components and rigorous testing. Scalability: Custom-built platforms can be more scalable in terms of adding more nodes or expanding the network, whereas COTS drones may have limitations in scalability due to hardware constraints. Cost: While COTS drones are generally more affordable upfront, custom-built platforms may incur higher initial costs but offer long-term cost savings through improved performance and customization. In conclusion, the choice between using COTS drones or custom-built platforms for real-world FANET experiments depends on the specific research requirements, budget constraints, and the level of customization and control needed for the experiments. Researchers should carefully evaluate the trade-offs and challenges associated with each option to determine the most suitable platform for their research objectives.