Drifters, devices that naturally float and follow the surface flow lines in a moving water body, are a method to measure important parameters, such as surface water velocity distributions, breakthrough curves, and mixing parameters. In drifter research it is important to have as many drifters as possible to optimally measure flow patterns . Drifters measure their location almost exclusively using GPS technology and store it on the device or transmit it using a network connection. Currently, drifters are made for specific research questions and are either expensive to purchase or are built in-house, requiring detailed knowledge of electrical engineering and embedded software . In general, drifters consist of a location device (often GPS), a logger to store the data, and/or a communication unit. Recent developments, most notably the rise of the “maker movement” and its ecosystem based on Arduino, Raspberry Pi, and other open-hardware development boards , have made the individual components of a GPS drifter commercially available as plug-and-play components for hobbyists and developers. This has led scientists to build GPS loggers geared to their own research need at lower costs. gave an early overview of the possibilities of the Arduino ecosystem as a low-cost alternative for expensive scientific instrumentation. developed the ALOG, a low-power Arduino-inspired logger system specifically designed with geoscientific fieldwork in mind.

Relating to using open hardware to track GPS positions, made a low-power GPS logger to track eastern box turtles. Their device stores the data in the local memory of the Arduino microcontroller, requiring retrieval after the experiment and providing no real-time view on where the turtles are. and both connected a GPS receiver and a cellular modem to an Arduino to track locations of vehicles. In vehicle tracking sufficient power is usually available through the battery of the car. GPS drifters are often used in near-shore applications. Drifters are often designed to store information locally, either by using a completely off-the-shelf GPS logger such as that of or by building one from open-hardware components such as those of , and . For applications where drifters can not be tracked visually for pick-up, wireless solutions are sometimes used. added the ability to communicate with the drifters over local radio to keep track of their location for easy pick-up. custom-built a suite of near-shore buoys that together act as a wireless sensor network (WSN). gives an overview of work on drifters (not focusing on low-cost examples) in their introduction and subsequently present their high-end, custom-designed floating sensor network. For open-ocean tracking adds an iridium satellite modem to their drifter.

For fieldwork on the Ayeyarwady and Chindwin rivers in Myanmar, we were in need of a low-cost, low-power GPS drifter that stores its data locally and at the same time transmits its location in real time for retrieval of the device. Given the length scales involved (tens of kilometers of sampling along the river per day) drifters would float out of range of base stations. However, since we are conducting our fieldwork on a stretch of river with ample cellular coverage, using a satellite modem would be costly overkill. In this article we present, to the best of our knowledge, the first complete open-hardware GPS drifter design that adds cellular communication to relay its location in real time. It features an (open-source) online back-end for both real-time tracking and data collection.

All of the projects cited above, while built using low-cost open-hardware components, were either too expensive to purchase for our project or required extensive engineering. In this article we furthermore set out to test the following question: has the open-hardware revolution progressed to the point that a low-power GPS drifter that wirelessly transmits its position can be made from open-hardware components by most geoscientists? The drifter presented in this paper was deliberately designed from easily connected off the shelf components, allowing for easy construction.

This article will explain how we build such a device, detail the software designed, and present the raw measurement results from the fieldwork on the Ayeyarwady river. The detailed analyses of the data falls beyond the scope of this article, which focuses on the development of the data collection devices, but is presented in the MSc thesis of . We conclude by reflecting on the primary research question about the ”build-ability” of our device by geoscientists in the discussion and conclusions.

The drifter design can be separated in two parts. Firstly the drifter itself, i.e. the material that makes the drifter float optimally given the local flow conditions of the field site. Secondly the electronics that do the GPS localization and communication. We describe the electronics first and the drifter design afterwards.

Experiments were conducted both upstream of the confluence in the Ayeyarwady River and at the Chindwin–Ayeyarwady confluence. The Ayeyarwady river system is a very dynamic and naturally unregulated river, one of the largest rivers in the world, and a vital vein for Myanmar . It is not only one of the main transport corridors but is also a source of fish, irrigation, and drinking water and an important aquatic ecosystem . It is a shallow, dynamic river system, with a typical depth range of only 1–10 m, whereas it has a width of several 100 m. The migrating sandbanks, especially upstream and around the confluence with the Chindwin, are a very pressing problem for river management. Insight into hydraulic behavior of the river is therefore a continuous demand. First of all, groups of five drifters were released at the same time in a line across the river. The purpose of this experiment was to validate a numerical study on the surface water flow velocity of the river. These analyses fall outside of the scope of this article and are presented in , who conclude that “the floater paths around the confluence have been useful for validating the Delft3D model, as the floater speeds and paths observed in the field were compared to those of the Delft3D model”.

The drifters were followed both online and with a trailing boat. For logistical reasons, when all drifters became too dispersed along the river length (not width), they were taken out of the water and brought together to restart the experiment. If a single drifter started significantly lagging behind with respect to the rest of the group, it was retrieved.

The upstream Ayeyarwady experiment was executed with a single group of five drifters. The confluence experiment was executed with two groups of five drifters upstream of the Chindwin–Ayeyarwady confluence, with one group in each river. The drifters were tracked along the confluence. At the end of the experiment, drifters were collected and data were read out from both the SD cards of the OpenLog and from the website to which the data had been uploaded.

The raw data collected from the sensors are plotted on a map of the experiment area; see Fig. . No post-processing has been done on these results: they show the raw GPS locations as recorded by the sensors and downloaded from the online database. The figure has been made by loading these measurements in Google Earth. The different flow paths that the sensors followed can be clearly identified. These data being directly available online allowed the measurement team to identify trackers that had beached or were stuck in near-shore eddies. The full raw dataset is provided in the Supplement. These raw data will be used in follow-up research. Figure  furthermore shows a closeup of the upstream Ayeyarwady experimental results. In this experiment the floaters were in the water for 16 h and 49 min and traveled 50 km in that time, with a distance of 4.6 km between the fastest and slowest drifter. Mean floater velocity over this stretch was 0.8 m s-1 . The location of the drifters clearly shows the different flow paths in the river. Unfortunately, we do spot some gaps in the data. These are caused by lack of cell coverage. In these instances, the particle electron spent a long time attempting to connect, before deciding the switch to offline, leading to a loss of data. Based on the time between signals that were received, we estimate that around 10 % of the data were lost in this manner, although differences between different trackers were significant. Future versions of the software code running on the particle electron should switch to offline sooner when cell coverage drops to prevent loss of data. Finally, although clearly labeled as experimental equipment, some sensors were picked out of the water by citizens. This was easily identified in the online visualization (i.e., sensors moving upstream) and allowed the research team to chase and recover those sensors. These instances were manually removed from the dataset.

The intention of this research was to test if currently available plug-and-play sensors and communication solutions are such that most members of the geoscientific community can use them to build their own measurement equipment suited to the specifics of the measurement campaign. Our experience shows that many geoscientifically trained persons should be able to connect the different pieces together and build the basic drifters. However, to have the entire setup operate at the required low power levels requires changing the standard programs that run on the device. The level of understanding of the electronic functioning of the device is not something that in general can be expected from a typical geoscientist, even one with extensive experience in using various types of electronic measurement equipment. Furthermore, developing the online back-end that visualizes the location of the GPS drifters in real time required back up from colleagues (see the Acknowledgements) with web development expertise. Aside from the design of the drifter, the construction of the trackers in Myanmar also required the creativity to use locally available materials as well as the capacity for the design to fine-tune and debug the tracker in the field. In our case, the code was changed such that if no cell signal was found, the logger would check again in 5 min instead of 1 min to minimize the data gaps (see above). Also, revitalizing a drowned tracker (due to collision with a commercial shipping vessel) needed engineering expertise. These type of experiences taught us that the real-world application of self-designed open-hardware equipment is not as straightforward as is sometimes envisioned. Here we establish that multi-disciplinary collaboration is still required when using low-cost self-designed equipment. Making the design more robust will lead to higher expenses.

Being able to trace the low-cost GPS drifters in real time proved invaluable during the fieldwork, for example in cases when curious locals picked up our drifters, as explained above. The real-time visualization allowed us to recover equipment.

We presented a low-cost GPS drifter that communicates its data in real time over the cellular network. We presented our design such that fellow geoscientists can (re)build or modify our GPS drifters using this article. Our device was tested in extensive fieldwork in the Chindwin and Ayeyarwady rivers in Myanmar. During development we tested if such a device can be completely developed by a geoscientist from available plug-and-play components. Although creating an online-logging GPS drifter turned out to be doable without an electrical engineering background, it required considerable understanding of embedded programming to develop a version that was low enough power to be useful in our fieldwork environment. It would be extremely useful for the geoscientific community if low-power versions of the electronics used in this research were available “out of the box”. Several companies are building lower-power logging equipment for geoscientists , but these are usually not yet usable without having to program them first.

The online environment that visualizes the fieldwork data in real time as they come in was developed by the main author and hosted on his personal website. It would be a great asset to geoscientists doing fieldwork if there was a platform that both stores and visualizes real-time data coming in. Part of this functionality is built into the CUAHSI Hydroshare project , although this focuses more on storing data for posterity than on real-time checking of data as they come in.

The advance of plug-and-play electronics has opened the opportunity for geoscientists to patch together the functionality they need in a sensor–logger communication device. We showed that for a GPS drifter with cellular connectivity and local storage this is indeed possible. However, getting plug-and-play devices to be low enough power for fieldwork conditions still requires considerable electrical engineering knowledge. We hope that this research provides a step in the right direction to help geoscientists use plug-and-play devices in general and our GPS drifters in particular, and we hope to encourage the companies producing these devices to bring low-power versions to market that work out of the box.