D-SLATS: Distributed Simultaneous Localization and Time Synchronization

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an ineffcient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The First two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error

WATLAS: high-throughput and real-time tracking of many small birds in the Dutch Wadden Sea

Tracking animal movement is important for understanding how animals interact with their (changing) environment, and crucial for predicting and explaining how animals are affected by anthropogenic activities. The Wadden Sea is a UNESCO World Heritage Site and a region of global importance for millions of shorebirds. Due to climate change and anthropogenic activity, understanding and predicting movement and space-use in areas like the Wadden Sea is increasingly important. Monitoring and predicting animal movement, however, requires high-resolution tracking of many individuals. While high-resolution tracking has been made possible through GPS, trade-offs between tag weight and battery life limit its use to larger species. Here, we introduce WATLAS (the Wadden Sea deployment of the ATLAS tracking system) capable of monitoring the movements of hundreds of (small) birds simultaneously in the Dutch Wadden Sea. WATLAS employs an array of receiver stations that can detect and localize small, low-cost tags at fine spatial (metres) and temporal resolution (seconds). From 2017 to 2021, we tracked red knots, sanderlings, bar-tailed godwits, and common terns. We use parts of these data to give four use-cases revealing its performance and demonstrating how WATLAS can be used to study numerous aspects of animal behaviour, such as, space-use (both intra- and inter-specific), among-individual variation, and social networks across levels of organization: from individuals, to species, to populations, and even communities. After describing the WATLAS system, we first illustrate space-use of red knots across the study area and how the tidal environment affects their movement. Secondly, we show large among-individual differences in distances travelled per day, and thirdly illustrate how high-throughput WATLAS data allows calculating a proximity-based social network. Finally, we demonstrate that using WATLAS to monitor multiple species can reveal differential space use. For example, despite sanderlings and red knots roosting together, they foraged in different areas of the mudflats. The high-resolution tracking data collected by WATLAS offers many possibilities for research into the drivers of bird movement in the Wadden Sea. WATLAS could provide a tool for impact assessment, and thus aid nature conservation and management of the globally important Wadden Sea ecosystem

A guide to pre-processing high-throughput animal tracking data

1. Modern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animal’s step size, leading to mis-estimation of key movement metrics such as speed. ‘Cleaning’ the data to reduce location errors prior to analyses is one of the main ways movement ecologists deal with noisy data, and has the advantage of being more scalable to massive datasets than more complex methods. Though data cleaning is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth, inclusive uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is still rather scarce. 2. A pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficient signal vs. noise screening, in which location errors are rejected without discarding valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods, while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time consuming, and to boost reproducibility, a robust pre-processing pipeline must be automated. 3. In this article we provide guidance on building pipelines for pre-processing high-throughput animal tracking data in order to prepare it for subsequent analysis. Our recommended pipeline, consisting of removing outliers, smoothing the filtered result, and thinning it to a uniform sampling interval, is applicable to many massive tracking datasets. We apply this pipeline to simulated movement data with location errors, and also show a case study of how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for quick biological inference on animal space use. We use calibration data to illustrate how pre-processing improves its quality, and to verify that the residence patch synthesis accurately captures animal space use. Finally, turning to tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. 4. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals’ movement capacity can be used to reduce location errors. The atlastools function is easy to use for beginners, while providing a template for further development. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and leads to better inferences from data

Monitoring Movement Patterns in Choughs

During soft-release reintroductions, biotelemetry devices are often used to track the movement patterns of released individuals. Very high frequency (VHF) and Global Positioning System (GPS) are commonly used telemetry methods, providing accurate locations. An alternative is dead-reckoning, providing high-resolution movement paths from heading and speed measurements, showing fine-scale changes that VHF may not identify. Errors in speed estimation can accumulate, however, producing wide error margins in flight distances and locations. I assess the utility of both techniques in relation to the release of red-billed choughs (Pyrrhocorax pyrrhocorax) on Jersey, UK. First, I use VHF locations to examine dispersal and habitat selection. I then go on to consider the potential of dead-reckoning for future monitoring, by examining the main determinants of error in flight distance and bearing in a similar-sized bird. The reintroduced choughs undertook small movements close to the release site, with individuals travelling as a flock, and dispersal distance showing no clear increase through time. Coastal grassland was the most used habitat, despite low availability, raising the possibility that dispersal may be limited by a lack of suitable habitat. The chough’s relatively short flight distances and tendency to return to a verifiable location, mean that dead-reckoning could potentially work well as a method to reconstruct their movement paths. However, drift was influenced by flight height, tailwind support and tortuosity. The effect of even low wind speeds on drift shown here suggests this would likely have an even greater influence in locations with higher wind speeds, such as Jersey. Ultimately, the use of multiple low-power telemetry systems could prove powerful, with corrected dead-reckoning providing new insight on the movement frequency, distances and paths as well as habitat selection, that could better inform conservation policy

Ergodicity breaking and lack of a typical waiting time in area-restricted search of avian predators

Movement tracks of wild animals frequently fit models of anomalous rather than simple diffusion, mostly reported as ergodic superdiffusive motion combining area-restricted search within a local patch and larger-scale commuting between patches, as highlighted by the L\'evy walk paradigm. Since L\'evy walks are scale invariant, superdiffusive motion is also expected within patches, yet investigation of such local movements has been precluded by the lack of accurate high-resolution data at this scale. Here, using rich high-resolution movement datasets ($>\! 7 \times 10^7$ localizations) from 70 individuals and continuous-time random walk modeling, we found subdiffusive behavior and ergodicity breaking in the localized movement of three species of avian predators. Small-scale, within-patch movement was qualitatively different, not inferrable and separated from large-scale inter-patch movement via a clear phase transition. Local search is characterized by long power-law-distributed waiting times with diverging mean, giving rise to ergodicity breaking in the form of considerable variability uniquely observed at this scale. This implies that wild animal movement is scale specific rather than scale free, with no typical waiting time at the local scale. Placing these findings in the context of the static-ambush to mobile-cruise foraging continuum, we verify predictions based on the hunting behavior of the study species and the constraints imposed by their prey.Comment: 27 pages, 8 figure

Validating ATLAS: A regional-scale high-throughput tracking system

Fine-scale tracking of animal movement is important to understand the proximate mechanisms of animal behaviour. The reverse-GPS system—ATLAS—uses inexpensive (~€25), lightweight (90% at 15 of 16 stationary sites. Tags on birds (1/6 Hz) on the Griend mudflat had a mean fix rate of 51%, yielding an average sampling rate of 0.085 Hz. Fix rates were higher in more central parts of the receiver array. ATLAS provides accurate, regional-scale tracking with which hundreds of relatively small-bodied species can be tracked simultaneously for long periods of time. Future ATLAS users should consider the height of receivers, their spatial arrangement, density and the movement modes of their study species (e.g. ground-dwelling or flying)

Long-term tracking and monitoring of mobile entities in the outdoors using wireless sensors

There is an emerging class of applications that require long-term tracking and monitoring of mobile entities for characterising their contexts and behaviours using data from wireless sensors. Examples include monitoring animals in their natural habitat over the annual cycle; tracking shipping containers and their handling during transit; and monitoring air quality using sensors attached to bicycles used in public sharing schemes. All applications within this class require the acquisition of sensor data tagged with spatio-temporal information and uploaded wirelessly. Currently there is no solution targeting the entire class of applications, only point solutions focused on specific scenarios. This thesis presents a complete solution (firmware and hardware) for applications within this class that consists of attaching mobile sensor nodes to the entities for tracking and monitoring their behaviour, and deploying an infrastructure of base-stations for collecting the data wirelessly. The proposed solution is more energy efficient compared to the existing solutions that target specific scenarios, offering a longer deployment lifetime with a reduced size and weight of the devices. This is achieved mainly by using the VB-TDMA low-power data upload protocol proposed in this thesis. The mobile sensor nodes, consisting of the GPS and radio modules among others, and the base-stations are powered by batteries, and the optimisation of their energy usage is of primary concern. The presence of the GPS module, in particular its acquisition of accurate time, is used by the VB-TDMA protocol to synchronise the communication between nodes at no additional energy costs, resulting in an energy-efficient data upload protocol for sparse networks of mobile nodes, that can potentially be out of range of base-stations for extended periods of time. The VB-TDMA and an asynchronous data upload protocol were implemented on the custom-designed Prospeckz-5-based wireless sensor nodes. The protocols’ performances were simulated in the SpeckSim simulator and validated in real-world deployments of tracking and monitoring thirty-two Retuerta wild horses in the Doñana National Park in Spain, and a herd of domesticated horses in Edinburgh. The chosen test scenario of long-term wildlife tracking and monitoring is representative for the targeted class of applications. The VB-TDMA protocol showed a significantly lower power consumption than other comparable MAC protocols, effectively doubling the battery lifetime. The main contributions of the thesis are the development of the VB-TDMA data upload protocol and its performance evaluation, along with the development of simulation models for performance analysis of wireless sensor networks, validated using data from the two real-world deployments