FlyPaw: Optimized route planning for scientific UAV missions

Many Internet of Things (IoT) applications require computing resources that cannot be provided by the devices themselves. On the other hand, processing of the data generated by IoT devices and sensors often has to be performed in real- or near real-time, i.e., with stringent latency requirements in constrained environments (e.g., intermittent network connectivity and limited power envelopes). Examples of such scenarios are autonomous vehicles in the form of cars and drones where the processing and analysis of observational data (e.g., video feeds) need to be performed expeditiously to allow for the safe operation of the vehicles and to deliver the results in a timely fashion to the stakeholders of the mission. To support the compute and timeliness requirements of such applications, it is essential to include suitable edge resources to process these workflows, and to develop an end-to-end system that can route the vehicles dynamicall..., Multiple types of data are included:
Firstly, collected using a drone, flying autonomously, making network measurements using a software defined radio (srsRAN). Raw radio and telemetry logs included as unprocessed txt files. Also included are processed logs combined from several independent sources into json files for convenience. Python code for merging and processing included in the results director of the associated github repository.
Secondly, one set of solution data from a recursive planning algorithm described in the FlyPaw publication is used to calculate various task completion estimates with metrics described in the paper., , # FlyPaw: Optimized route planning for scientific UAV missions

[https://doi.org/10.5061/dryad.0gb5mkm8d](https://doi.org/10.5061/dryad.0gb5mkm8d)

## Data Collection

This data was obtained in collaboration with North Carolina State University and the NSF Funded AERPAW Testbed-- Award #1939334

AERPAW (Aerial Experimentation and Research Platform for Advanced Wireless) is funded by the PAWR (Platforms for Advanced Wireless Research) Project Office. The authors thank the NC State team and the National Science Foundation for making these valuable resources available. More information about the AERPAW testbed can be found [here](https://aerpaw.org/)

## Description of the data and file structure

Several types of data are presented here, collected with the NSF-funded AERPAW testbed at North Carolina State University

1\) iperf measurements made from drone to base station using the srsRAN software-defined radio suite at various points around the flying field

2\) telemetry data from the dr...