FlyPaw: Optimized route planning for scientific UAV missions
Data files
Jan 17, 2025 version files 672.58 KB
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2022-01-13_135715_iperfserver_log.txt
153.53 KB
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2022-01-13_135715_radio_enb_log.txt
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2022-01-13_135715_radio_epc_log.txt
25.40 KB
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2022-01-13_135723_radio_log.txt
141.40 KB
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2022-01-13_135723_vehicle_log.txt
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2022-01-13_135723_vehicleOut.txt
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2022-03-11_12_01_50_iperfserver_log.txt
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2022-03-11_12_01_50_radio_enb_log.txt
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2022-03-11_12_01_50_radio_epc_log.txt
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2022-03-11_12_02_18_radio_log.txt
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flypawState_20220311-120218.json
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iperf3_20220113-135727.json
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iperf3_20220311-120218.json
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README.md
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telemetry_20220311-120218.json
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Abstract
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.
README: FlyPaw: Optimized route planning for scientific UAV missions
https://doi.org/10.5061/dryad.0gb5mkm8d
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
Code and results are also available in the GitHub repository:
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 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
Methods
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.