Bachelor Thesis: Drone Network Measurement System

Dronehub K
Feb 6, 2019 · 3 min read


The field of swarm robotics is a rapidly developing and interesting subject to study, especially with regard to drone swarms. At Alpen-Adria-Universität, multiple research groups are exploring this concept in their work. In such a system, as in any multi-agent setup, communication is an important factor. Unfortunately, there are no popular comprehensive tools designed to monitor and measure moving or flying ad-hoc networks. The goal of this project is to create such a tool that can later be used by the Karl Popper Kolleg on Networked Autonomous Aerial Vehicles (KPK NAV).


  1. Based on studies of the current state of research and technology, propose multiple parameters describing the quality of communication links, covering throughput (data rate), latency, and reliability measures.
  2. Design and implement software that estimates or measures the quality of links among communicating agents in a wireless network.
  3. Specify querying APIs for the communication quality data of (3.1) a single link and (3.2) an entire wireless (ad-hoc) network.
  4. Design and implement software that gathers link quality data from the whole wireless (ad-hoc) network and visualizes it on the user’s computer.


The following split of the project into the milestones is proposed. It should be thought of as an iterative process — each milestone can be revisited and improved multiple times and they do not have to be completed in order.

  • State of the art

Especially at the beginning of the project it is important to gather a shared knowledge of the state of the art tools and industry standards.

Tools used for network monitoring and configuring, data logging and data visualization should be identified and briefly tested. Some examples of such tools include: iperf, wireshark, iwtools, babeld, monitorix, rrdtool.

  • Local link quality estimation

The aim of this milestone is to identify parameters describing a link quality and implement a software to estimate these parameters on a Linux-based system. A set of such parameters could, for example, include:

  1. throughput,
  2. round trip time (RTT),
  3. expected transmission count (ETX), as a measure of link reliability.

During the progress of the project, new measures of the link quality could be identified and they should also be accomodated.

A key aspect of this milestone is a network load vs. measurement precision/reliability tradeoff: in active measurements, one can exchange a huge amount of data to get very precise measurements, but such an overhead is not practical. Achieving a good estimation with just a small amount of exchanged data would be beneficial. Furthermore, passive solutions should also be investigated. In such a case, no additional data should be exchanged among devices, the solution should just monitor current traffic and estimate its parameters.

  • Definition and implementation of querying APIs

After identifying connection parameters and estimating them, the interface to access the gathered data should be defined and implemented. The key factors to consider are:

  1. at which frequency can the results be obtained, and from where, i.e., which node(s),
  2. how to aggregate the data (allow access just to the last sample; maximum, minimum or average over a time interval, for how long an interval, etc.),
  3. how to schedule active measurements so they will not interfere with other agents’ transmissions.
  • Data gathering and visualization

When the data is available locally on each machine and it is possible to query for this data, a method to gather and visualize it on any single node in the network should be implemented. The solution should work both at runtime (visualize current state) and in post-processing (visualize past states, gather statistics).

  • Experiments and evaluation

After the working system is available, experiments should be conducted. Multiple configurations of hardware (WiFi: 802.11b, g, ac, wired connection, different communication devices) should be tested in different scenarios (high/low load, high/low fading, infrastructure networks, mesh/ad-hoc networks, etc.).

Currently two platforms that can be used for the tests are available:

  1. fdrones (“fake drones”) — a swarm of mobile robots based on Raspberry Pi 3,
  2. Intel Aero — a small swarm of Intel Aero Ready to Fly drones.

Period and Contacts

Time period: As soon as possible

Internal supervisors:

Hermann Hellwagner <>

Michał Barciś <>

Originally published at on February 6, 2019.

Dronehub K

A multidisciplinary team at University of Klagenfurt and Lakeside Labs performs research on networked autonomous aerial systems.

Dronehub K

Written by

Dronehub K

A multidisciplinary team at University of Klagenfurt and Lakeside Labs performs research on networked autonomous aerial systems.

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