DOI: 10.5061/dryad.0gb5mkm8d
title: FlyPaw: Optimized route planning for scientific UAV missions

description: 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 dynamically
 and process and deliver mission-critical data and analyzed
 results. In this paper, we develop and evaluate a dynamic
 scheduling approach that considers complex tradeoffs between
 real-time constraints, network availability, and latency
 sensitivity of the mission. We devise an optimized route planning
 and data transmission schedule for drone flights. The scheduling
 algorithm is encapsulated in a novel end-to-end architecture
 (FlyPaw) and an associated adaptive drone mission control system,
 which enables deployment and management of an integrated
 cyberphysical system (CPS) – from real drone testbed to base
 stations to edge-to-cloud resources. The planning algorithm takes
 into account measured network communication
 characteristics, estimated uncertainties of future data link
 connectivity, and data timeliness requirements of the mission to
 prioritize candidate decision tree solutions based on a risk
 metric derived from Sharpe’s ratio. Our results show that for
 given task sets, Net Time to Retrieve, our metric describing the
 time required to perform end-to-end collection and downstream
 processing of data, can be significantly reduced compared to
 other naive approaches. The theoretical improvement provided
 by our algorithm over other naive approaches is dependent on
 several factors — task locations, network connectivity,
 processing times, and available resources, and is bounded by the duration
 of the drone flight.
description: 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.
description: # 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 drone 3\) srsRAN logs describing raw radio performance 4\) state information showing the transition of flight through various phases Full description of the data and experiments best described in the paper below, with images: [https://doi.org/10.5061/dryad.0gb5mkm8d](https://doi.org/10.5061/dryad.0gb5mkm8d) Code and results are also available in the GitHub repository: [https://github.com/FlyNet-NSF/flypaw](https://github.com/FlyNet-NSF/flypaw) ## Data Format Data is shared as JSON from iperf results, from drone telemetry results, and from dynamic planning algorithms described in the paper. Radio raw results shared from [srsRAN ](https://docs.srsran.com/projects/4g/en/latest/usermanuals/source/srsenb/source/2_enb_getstarted.html#observing-results)and described in the link. ### Files and variables #### File: flypawState\_20220311-120218.json **Description:**  Detailed state information as the automated flight transitions #### File: 2022-01-13\_135723\_radio\_log.txt #### File: 2022-01-13\_135715\_radio\_epc\_log.txt #### File: 2022-01-13\_135715\_radio\_enb\_log.txt #### File: 2022-03-11\_12\_02\_18\_radio\_log.txt #### File: 2022-03-11\_12\_01\_50\_radio\_epc\_log.txt #### File: 2022-03-11\_12\_01\_50\_radio\_enb\_log.txt **Description:**  srsRAN logs for the SDN suite #### File: iperf3\_20220113-135727.json #### File: iperf3\_20220311-120218.json #### File: 2022-01-13\_135715\_iperfserver\_log.txt #### File: 2022-03-11\_12\_01\_50\_iperfserver\_log.txt **Description:**  TCP and UDP-based iperf data describing air-to-ground UDP throughput at various points in the test area as estimated by the vehicle and also at the ground #### File: 2022-01-13\_135723\_vehicle\_log.txt #### File: 2022-01-13\_135723\_vehicleOut.txt **Description:**  Default AERPAW vehicle logs #### File: telemetry\_20220311-120218.json **Description:**  Telemetry from the vehicle during the flight