Abstract Background The increasing importance of wildlife movement data in ecology and conservation has fueled the development of Automated Radiotelemetry Systems (ARTS) using very-high-frequency (VHF) transmitters. To make optimal use of this data, highly precise analysis methods are needed to detect even small-scale movement changes and thus provide high data quality. While various approaches have successfully minimized position errors in ARTS, they mostly rely on a single mean error estimate. Methods We present two contributions. First, an antenna geometry-based position finding method (antenna beams) for small scale movements that reduces position errors (PE) and increases the number of position estimates. Second, a model for per-position error estimation, predicting error as a function of signal and position characteristics, applicable for data without ground-truth information and across various position finding methods. Using ground-truth data from VHF transmitters recorded simultaneously with the ARTS trackIT and GPS, we validated and compared yield, position errors and predictive performance of our approach with the common angulation and multilateration methods. Results Our antenna beam-based method provided a substantial alternative to angulation for directional set-ups, achieving comparable mean PEs (41 m vs. 44 m) and especially higher number of localizations (up to 99% vs. 30 to 66%). The per-position error estimation model demonstrated a strong predictive performance (mean absolute deviation from true error down to 21 m) utilizing parameters such as the number of participating stations and antennas, maximum signal strength, normalized summed up signal strengths and positioning within the study area. Conclusions Our results indicate that (i) the antenna beam-based position-finding method outperforms common methods in both accuracy and yield, (ii) the novel introduced per-position error estimation model reliably reflects measured PE from ground-truth data, and (iii) the resulting setup provides a robust foundation for high-resolution wildlife movement analyses.

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

We present a novel multi-hop data dissemination protocol for wireless networks that minimizes the total energy consumption across an entire network by minimizing the transmission power at each hop. It is based on a game-theoretic model, constructs a spanning tree topology in a decentralized manner, and is usable in practice. We evaluate the protocol via simulation and a pratical implementation on a testbed of 75 Raspberry Pis, demonstrating that a total energy reduction of up to 90% can be achieved compared to a simple broadcast protocol.

Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium's broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments.

The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states. A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98). The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method. Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.

We present Bird@Edge, an Edge AI system for recognizing bird species in audio recordings to support real-time biodiversity monitoring. Bird@Edge is based on embedded edge devices operating in a distributed system to enable efficient, continuous evaluation of sound-scapes recorded in forests. Multiple ESP32-based microphones (called Bird@Edge Mics) stream audio to a local Bird@Edge Station, on which bird species recognition is performed. The results of several Bird@Edge Stations are transmitted to a backend cloud for further analysis, e.g., by biodiversity researchers. To recognize bird species in soundscapes, a deep neural network based on the EfficientNet-B3 architecture is trained and optimized for execution on embedded edge devices and deployed on a NVIDIA Jetson Nano board using the DeepStream SDK. Our experiments show that our deep neural network outperforms the state-of-the-art BirdNET neural network on several data sets and achieves a recognition quality of up to 95.2% mean average precision on soundscape recordings in the Marburg Open Forest, a research and teaching forest of the University of Marburg, Germany. Measurements of the power consumption of the Bird@Edge components highlight the real-world applicability of the approach. All software and firmware components of Bird@Edge are available under open source licenses.

Existing routing algorithms for disruption-tolerant networking (DTN) have two main limitations: (a) a particular DTN routing algorithm is typically designed to achieve very good performance in a specific scenario, but has limited performance in other scenarios, and (b) DTN routing algorithms do not take advantage of network programmability to profit from its benefits. We present ProgDTN, a novel approach to support programmable disruption-tolerant networking by allowing network operators to implement and adapt routing algorithms without knowledge of a router's interior workings using the popular JavaScript language. To consider the specific properties of a particular application scenario, network operators can incorporate context information of DTN bundles and nodes in their routing algorithms. ProgDTN is based on DTN7, a flexible and efficient open-source, platform-independent implementation of the Bundle Protocol version 7. Our experimental evaluation demonstrates that using ProgDTN to tailor a routing algorithm to a particular scenario achieves excellent results of up to 99.9% delivery ratio while reducing unnecessary transmissions by 92.9%. ProgDTN's implementation, our tailored scenario-specific routing algorithm, and code/data fragments for our experiments are released under permissive open-source licenses.

A network for environmental monitoring typically requires a large number of sensors. If a longer service life is intended, it is essential that the deployed sensor systems can be upgraded without modifying hardware. Often, these networks rely on proprietary hardware/software components tailored to the desired functionality, but these could technically also be used for other applications. We present a demo of mechanism interception, a novel approach to unobtrusively add or modify the functionality of an existing networked system, in our case a TreeTalker, without touching any proprietary components. We demonstrate how a cloud infrastructure can be unobtrusively replaced by an edge infrastructure in a wireless sensor network. Our results indicate that mechanism interception is a compelling approach for our scenario to provide previously unavailable functionality without modifying existing components.

Networked systems and applications are often based on proprietary hardware/software components that manufacturers might not be willing to adapt or update if new requirements arise. We present mechanism interception, a novel approach to unobtrusively add or modify functionality to/of an existing networked system or application without touching any proprietary components. Behavioral changes are achieved by functionality-enhancing yet unobtrusive interceptors, i.e., components introduced between systems and their environments adding or updating mechanisms. We illustrate our approach by unobtrusively adding a vertical handover mechanism between Wi-Fi and LTE to a mobile end device without disconnecting TCP sessions. Our results indicate that mechanism interception is a compelling approach to achieve improved service quality and provide previously unavailable functionality.

Abstract Bats represent a highly diverse group of mammals and are essential for ecosystem functioning. However, knowledge about their behaviour, ecology and conservation status is limited. Direct observation of marked individuals (commonly applied to birds) is not possible for bats due to their small size, rapid movement and nocturnal lifestyle, while neither popular observation methods such as camera traps nor conventional tracking technologies sufficiently capture the behaviour of individuals. The combination and networking of different sensors in a single system can overcome these limitations, but this potential has been explored only to a limited extent. We present BatRack, a multi‐sensor device that combines ultrasonic audio recordings, automatic radio telemetry and video camera recordings in a single modular unit. BatRack facilitates the individual or combined scheduling of sensors and includes a mutual triggering mode. It consists of off‐the‐shelf hardware and both its hardware blueprints and the required software have been published under an open license to allow scientists and practitioners to replicate the system. We tested the suitability of radio telemetry and audio sensors as camera triggers and evaluated the detection of individuals in video recordings compared to radio telemetry signals. Specifically, BatRack was used to monitor the individual swarming behaviour of six members of a maternity colony of Bechstein's bat. Preliminary anecdotal results indicate that swarming intensity is related to reproductive state and roost switching. BatRack allows researchers to recognize individual bats and monitor their behavioural patterns using an easily deployed and scalable system. BatRack is thus a promising approach to obtaining detailed insights into the behavioural ecology of bats.

: We present tRackIT OS , open-source software for reliable VHF radio tracking of (small) animals in their wildlife habitat. tRackIT OS is an operating system distribution for tRackIT stations that receive signals emitted by VHF tags mounted on animals and are built from low-cost commodity-off-the-shelf hardware. tRackIT OS provides software components for VHF signal processing, system monitoring, configuration management, and user access. In particular, it records, stores, analyzes, and transmits detected VHF signals and their descriptive features, e.g., to calculate bearings of signals emitted by VHF radio tags mounted on animals or to perform animal activity classification. Furthermore, we provide results of an experimental evaluation carried out in the Marburg Open Forest , the research and teaching forest of the University of Marburg, Germany. All components of tRackIT OS are available under a GNU GPL 3.0 open source license at https://github.com/nature40/tRackIT-OS .

The worldwide shortage of medical-grade ventilators is a well-known issue, that has become one of the central topics during the COVID-19 pandemic. Given that these machines are expensive and have long lead times, one approach is to vacate them for patients in critical conditions while patients with mild to moderate symptoms are treated with stripped-down ventilators. We propose a mass-producible solution that can create such ventilators with minimum effort. The central part is a module that can be attached to CPAP machines and repurpose them as low-pressure ventilators. Here, we describe the concept and first measurements which underline the potential of our solution. Our approach may serve as a starting point for open-access ventilator technologies.

Computer systems used in the field of humanitarian technology are often based on general-purpose single-board computers, such as Raspberry Pis. While these systems offer great flexibility for developers and users, configuration and deployment either introduces overhead by executing scripts on multiple devices or requires deeper technical understanding when building operating system images for such small computers from scratch. In this paper, we present PIMOD, a software tool for configuring operating system images for single-board computer systems. We propose a simple yet comprehensive configuration language. In a configuration profile, called Pifile, a small set of commands is used to describe the configuration of an operating system image. Virtualization techniques are used during the execution of the profile in order to be distribution and platform independent. Commands can be issued in the guest operating system, providing access to the distribution specific tools, e.g., to configure hardware parameters. The implementation of PIMOD is made public under a free and open source license. PIMOD is evaluated in terms of user benefits, performance compared to on-system configuration, and applicability across different hardware platforms and operating systems.

Information-centric Networking (ICN) is an emerging Internet architecture that offers promising features, such as in-network caching and named data addressing, to support the edge computing paradigm, in particular Internet-of-Things (IoT) applications. ICN can benefit from Complex Event Processing (CEP), which is an in-network processing paradigm to specify and perform efficient query operations on data streams. However, integrating CEP into ICN is a challenging task due to the following reasons: (1) typical ICN architectures do not provide support for forwarding and processing continuous data streams; (2) IoT applications often need short response times and require robust event detection, which both are hard to accomplish using existing CEP systems. In this article, we present a novel network architecture, called INetCEP, for efficient CEP-based in-network processing as part of ICN. INetCEP enables efficient data processing in ICN by means of (1) a unified communication model that supports continuous data streams, (2) a meta query language for CEP to specify data processing operations in the data plane, and (3) query processing algorithms to resolve the specified operations. Our experimental results for two IoT use cases and datasets show that INetCEP offers very short response times of up to 73 μs under high workload and is more than 15X faster in terms of forwarding events than the state-of-the-art CEP system Flink. Furthermore, the delivery and processing of complex queries is around 32X faster than Flink and more than 100X faster than a naive pull-based reference approach, while maintaining 100

Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.

In this paper, we present an approach to facilitate long-range device-to-device communication via smartphones in crisis scenarios. Through a custom firmware for low-cost LoRa capable micro-controller boards, called rf95modem, common devices for end users can be enabled to use LoRa through a Bluetooth, Wi-Fi, or serial connection. We present two applications utilizing the flexibility provided by the proposed firmware. First, we introduce a novel device-to-device LoRa chat application that works a) on the two major mobile platforms Android and iOS and b) on traditional computers like notebooks using a console-based interface. Second, we demonstrate how other infrastructure-less technology can benefit from our approach by integrating it into the DTN7 delay-tolerant networking software. The firmware, the device-to-device chat application, the integration into DTN7, as well as the experimental evaluation code fragments are available under permissive open-source licenses.

The worldwide shortage of medical-grade ventilators is a well-known issue, that has become one of the central topics during the COVID-19 pandemic. Given that these machines are expensive and have long lead times, one approach is to vacate them for patients in critical conditions while patients with mild to moderate symptoms are treated with stripped-down ventilators. We propose a mass-producible solution that can create such ventilators with minimum effort. The central part is a module that can be attached to CPAP machines and repurpose them as low-pressure ventilators. Here, we describe the concept and first measurements which underline the potential of our solution. Our approach may serve as a starting point for open-access ventilator technologies.

Computation offloading is often used in mobile cloud, edge, and/or fog computing to cope with resource limitations of mobile devices in terms of computational power, storage, and energy. Computation offloading is particularly challenging in situations where network connectivity is intermittent or error-prone. In this paper, we present OPPLOAD, a novel framework for offloading computational workflows in opportunistic networks. The individual tasks forming a workflow can be assigned to particular remote execution platforms (workers) either preselected ahead of time or decided just in time where a matching worker will automatically be assigned for the next task. Tasks are only assigned to capable workers that announce their capabilities. Furthermore, tasks of a workflow can be executed on multiple workers that are automatically selected to balance the load. Our Python implementation of OPPLOAD is publicly available as open source software. The results of our experimental evaluation demonstrate the feasibility of our approach.

We present a novel data-driven approach to perform smooth Wi-Fi/cellular handovers on smartphones. Our approach relies on data provided by multiple smartphone sensors (e.g., Wi-Fi RSSI, acceleration, compass, step counter, air pressure) to predict Wi-Fi connection loss and uses Multipath TCP to dynamically switch between different connectivity modes. We train a random forest classifier and an artificial neural network on real-world sensor data collected by five smartphone users over a period of three months. The trained models are executed on smartphones to reliably predict Wi-Fi connection loss 15 seconds ahead of time, with a precision of up to 0.97 and a recall of up to 0.98. Furthermore, we present results for four DASH video streaming experiments that run on a Nexus 5 smartphone using available Wi-Fi/cellular networks. The neural network predictions for Wi-Fi connection loss are used to establish MPTCP subflows on the cellular link. The experiments show that our approach provides seamless wireless connectivity, improves quality of experience of DASH video streaming, and requires less cellular data compared to handover approaches without Wi-Fi connection loss predictions.