Recent advances in animal tracking technology have ushered in a new era in biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, whereas older techniques often still represent the state-of-the-art for studying small vertebrates. In industrial applications, low-power wireless sensor networks (WSNs) fulfill requirements similar to those needed to monitor animal behavior at high resolution and at low tag mass. We developed a wireless biologging network (WBN), which enables simultaneous direct proximity sensing, high-resolution tracking, and long-range remote data download at tag masses of 1 to 2 g. Deployments to study wild bats created social networks and flight trajectories of unprecedented quality. Our developments highlight the vast capabilities of WBNs and their potential to close an important gap in biologging: fully automated tracking and proximity sensing of small animals, even in closed habitats, at high spatial and temporal resolution.

Sensor networks have successfully been used for wildlife monitoring and tracking of different species. When it comes to small animals such as smaller birds, mammals, or even insects, the current approach is to use extremely lightweight RF tags located using radio telemetry. A new quantum leap in technology is needed to overcome this limitation and enable new ways to observe larger numbers of small animals. In an interdisciplinary team, we are working on the different aspects of such a new technology. In particular, we report on our findings on a sensor- network-based tracking solution for bats. Our system is based on integrated localization and wireless communication protocols for ultra-low-power systems. This requires coding techniques for improved reliability as well as ranging solutions for tracking hunting bats. We address the technological and methodical problems related to system design, software support, and protocol design. First field experiments have been conducted that showcase the capabilities of our system.

In this paper, the BATS project is presented, which aims to track the behavior of bats via an ultra-low power wireless sensor network. An overview about the whole project and its parts like sensor node design, tracking grid and software infrastructure is given and the evaluation of the project is shown. The BATS project includes a lightweight sensor node that is attached to bats and combines multiple features. Communication among sensor nodes allows tracking of bat encounters. Flight trajectories of individual tagged bats can be recorded at high spatial and temporal resolution by a ground node grid. To increase the communication range, the BATS project implemented a long-range telemetry system to still receive sensor data outside the standard ground node network. The whole system is designed with the common goal of ultra-low energy consumption while still maintaining optimal measurement results. To this end, the system is designed in a flexible way and is able to adapt its functionality according to the current situation. In this way, it uses the energy available on the sensor node as efficient as possible.

We present a novel concept for a wake-up system based ultra-low power communication protocol for sensor networks. The main application field is monitoring contacts and even tracks of bats in the wild. Our sensor nodes can weigh at most 2 g out of which 1 g remains for the battery. We investigate a novel communication protocol design applicable to these systems and also showing great potentials for other ultra-low power sensor networks. In particular, we investigate the bat to ground communication by combining duty cycling with a multi-stage wake-up receiver. We employ Binary Offset Carrier (BOC) modulated signals that allow to accurately localize and track the bats while transmitting data in parallel. In a first step, we evaluated the conceptual design using a software-defined radio system to demonstrate the feasibility of the protocol design.

Location-awareness is a prerequisite for many applications, today. Demanding applications, e.g. wildlife monitoring, rely on precise location information. A prospective approach tracking tiny lightweight animals is to capture field-strength measurements utilizing a wireless sensor network. Apriori knowledge of propagation channel characteristics is essential for precise localization. Hence, in this paper a channel model for forested areas is proposed. Therefore, a reference set of field-strength measurements has been evaluated and a channel model has been derived from these measurements. Besides path loss and fading, also cross fading between two directional antennas has been characterized. Cross-fading between these antennas is crucial for RSSI-based direction finding by directional antennas. Furthermore, the impact of incorrect modeling measurement uncertainties on the position estimation error is assessed.

Kalman filters (KFs) are popular methods to estimate position information from a set of time-of-flight (ToF) values in radio frequency (RF)-based locating systems. Such filters are proven to be optimal under zero-mean Gaussian error distributions. In presence of multipath propagation ToF measurement errors drift due to small-scale motion. This results in changing phases of the multipath components (MPCs) which cause a drift on the ToF measurements. Thus, on a short-term scale the ToF measurements have a non-constant bias that changes while moving. KFs cannot distinguish between the drifting measurement errors and the true motion of the tracked object. Hence, very rigid motion models have to be used for the KF which commonly causes the filters to diverge. Therefore, the KF cannot resolve the short-term errors of consecutive measurements and the long-term motion of the tracked object. This paper presents a data-driven approach that uses training sequences to derive a near-optimal position estimator. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) learns to interpret drifting errors in ToF measurements of a tracked dynamic object directly from raw ToF data. Our evaluation shows that our approach outperforms state-of-the-art KFs on both synthetically generated and real-world dynamic motion trajectories that include drifting ToF measurement errors.