Sensors 2018, 18(10), 3343; https://doi.org/10.3390/s18103343
BATS: Adaptive Ultra Low Power Sensor Network for Animal Tracking
Niklas Duda 1,*
, Thorsten Nowak 2, Markus Hartmann 2, Michael Schadhauser 2, Björn Cassens 3, Peter Wägemann 4, Muhammad Nabeel 5, Simon Ripperger 6,7, Sebastian Herbst 8, Klaus Meyer-Wegener 8, Frieder Mayer 6,9, Falko Dressler 5, Wolfgang Schröder-Preikschat 4, Rüdiger Kapitza 3, Jörg Robert 2, Jörn Thielecke 2, Robert Weigel 1 and Alexander Kölpin 10
, Thorsten Nowak 2, Markus Hartmann 2, Michael Schadhauser 2, Björn Cassens 3, Peter Wägemann 4, Muhammad Nabeel 5, Simon Ripperger 6,7, Sebastian Herbst 8, Klaus Meyer-Wegener 8, Frieder Mayer 6,9, Falko Dressler 5, Wolfgang Schröder-Preikschat 4, Rüdiger Kapitza 3, Jörg Robert 2, Jörn Thielecke 2, Robert Weigel 1 and Alexander Kölpin 10
1
Institute for Electronics Engineering, Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
2
Institute of Information Technology, Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
3
Institute of Operating Systems and Computer Networks, TU Braunschweig, 38106 Brunswick, Germany
4
Chair of Computer Science 4—Distributed Systems and Operating Systems, Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
5
Heinz Nixdorf Institute and Department of Computer Science, Paderborn University, 33098 Paderborn, Germany
6
Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, 10115 Berlin, Germany
7
Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Panama
8
Chair of Computer Science 6—Data Management, Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
9
Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany
10
Chair for Electronics and Sensor Systems, Brandenburg University of Technology, 03046 Cottbus, Germany
*
Author to whom correspondence should be addressed.
Received: 11 September 2018 / Revised: 25 September 2018 / Accepted: 28 September 2018 / Published: 7 October 2018
(This article belongs to the Special Issue Low Energy Wireless Sensor Networks: Protocols, Architectures and Solutions)
Abstract
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. View Full-Text
Figure 1
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MDPI and ACS Style
Duda, N.; Nowak, T.; Hartmann, M.; Schadhauser, M.; Cassens, B.; Wägemann, P.; Nabeel, M.; Ripperger, S.; Herbst, S.; Meyer-Wegener, K.; Mayer, F.; Dressler, F.; Schröder-Preikschat, W.; Kapitza, R.; Robert, J.; Thielecke, J.; Weigel, R.; Kölpin, A. BATS: Adaptive Ultra Low Power Sensor Network for Animal Tracking. Sensors 2018, 18, 3343.
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