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Wildlife Research Wildlife Research Society
Ecology, management and conservation in natural and modified habitats
RESEARCH ARTICLE

Testing the feasibility of wireless sensor networks and the use of radio signal strength indicator to track the movements of wild animals

C. R. Krull https://orcid.org/0000-0003-4030-2313 A E , L. F. McMillan B , R. M. Fewster B , R. van der Ree C , R. Pech D , T. Dennis A and M. C. Stanley A
+ Author Affiliations
- Author Affiliations

A School of Biological Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

B Department of Statistics, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

C Ecology and Infratructure International and School of BioSciences, The University of Melbourne, Vic. 3010, Australia.

D Manaaki Whenua Landcare Research, PO Box 69040, Lincoln 7640, New Zealand.

E Corresponding author. Email: cherylrkrull@gmail.com

Wildlife Research 45(8) 659-667 https://doi.org/10.1071/WR18013
Submitted: 25 January 2018  Accepted: 15 August 2018   Published: 5 December 2018

Abstract

Context: Wireless sensor networks (WSNs) are revolutionising areas of animal behaviour research and are advantageous based on their ability to be deployed remotely and unobtrusively, for long time periods in inaccessible areas.

Aims: We aimed to determine the feasibility of using a WSN to track detailed movement paths of small animals, e.g. rats (Rattus spp.) 100–400 g, too small for current GPS technology, by calibrating active Radio Frequency Identification (RFID) tags and loggers using Radio Frequency Signal Strength Indicator (RSSI) as a proxy for distance. Active RFIDs are also called Wireless Identification (WID) tags.

Methods: Calibration tests were conducted using a grid of loggers (n = 16) spaced at 45-m intervals in clear line-of-sight conditions. WID tags (n = 16) were placed between the loggers at 45-m intervals. Eight ‘walks’ were also conducted through the grid using a single WID tag. This involved attaching the tag to a small bottle of water (to simulate the body of an animal), towed around the grid using a 1-m long tow line attached to a volunteer walker. The volunteer also held a GPS device that logged their track. Models were constructed to test the effects of distance, tag movement and individual differences in loggers and tags on the reliability of movement data.

Key results: Loggers were most successful at detecting tags at distances <50 m. However, there was a significant difference in the detection probabilities of individual loggers and also the transmission performance of individual tags. Static tags were less likely to be detected than the mobile tag; and although RSSI was somewhat related to distance, the reliability of this parameter was highly variable.

Implications: We recommend caution in the future use of current radio frequency ID tags in wireless sensor networks to track the movement of small animals, and in the use of RSSI as an indicator of individual distance values, as extensive in situ calibration is required. ‘Off the shelf’ devices may vary in performance, rendering data unreliable. We emphasise the importance of calibrating all equipment in animal tracking studies to reduce data uncertainty and error.

Additional keywords: Radio Frequency Identification, RFID, WSNs, Wireless Identification tags, calibration, testing tracking devices, proximity studies, wildlife sensing.


References

Awad, A., Frunzke, T., and Dressler, F. (2007). Adaptive distance estimation and localization in WSN using RSSI measures. In ‘10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (DSD 2007), 29–31 August 2007, Lubeck, Germany’. pp. 471–478. (IEEE, Piscataway, NJ.) Available at https://ieeexplore.ieee.org/document/4341511/ [Verified September 2018]

Bagree, R., Jain, V. R., Kumar, A., and Ranjan, P. (2010). TigerCENSE: wireless image sensor network to monitor tiger movement. In ‘Real-World Wireless Sensor Networks’. (Eds P. J. Marron, T. Voigt, P. Corke and L. Mottola)pp. 13–24. (Springer: Berlin.)

Barrenetxea, G., Ingelrest, F., Schaefer, G., Vetterli, M., Couach, O., and Parlange, M. (2008). Sensorscope: out-of-the-box environmental monitoring. In ‘2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), 22–24 April 2008, St Louis, MO’. pp. 332–343. (IEEE: Piscataway, NJ.) Available at https://ieeexplore.ieee.org/document/4505485/ [Verified September 2018]

Bates, D., Maechler, M., Bolker, B., and Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. Available at http://CRAN.R-project.org/package=lme4 [Verified September 2018]

Benkic, K., Malajner, M., Planinsic, P., and Cucej, Z. (2008). Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. In ‘15th International Conference on Systems, Signals and Image Processing, 25-28 June 2008, Bratislava, Slovakia’. pp. 303–306. (IEEE: Piscataway, NJ.) Available at https://ieeexplore.ieee.org/document/4604427/ [Verified September 2018]

Boyland, N. K., James, R., Mlynski, D. T., Madden, J. R., and Croft, D. P. (2013). Spatial proximity loggers for recording animal social networks: consequences of inter-logger variation in performance. Behavioral Ecology and Sociobiology 67, 1877–1890.
Spatial proximity loggers for recording animal social networks: consequences of inter-logger variation in performance.Crossref | GoogleScholarGoogle Scholar |

Chandrasekhar, V., Seah, W. K., Choo, Y. S., and Ee, H. V. (2006). Localization in underwater sensor networks: survey and challenges. In ‘Proceedings of the 1st ACM International Workshop on Underwater Networks, WUWNET, 25 September 2006, Los Angeles, CA’. pp. 33–40. (ACM: New York.) 10.1145/1161039.1161047

Creech, T. G., Cross, P. C., Scurlock, B. M., Maichak, E. J., Rogerson, J. D., Henningsen, J. C., and Creel, S. (2012). Effects of low-density feeding on elk-fetus contact rates on Wyoming feedgrounds. The Journal of Wildlife Management 76, 877–886.
Effects of low-density feeding on elk-fetus contact rates on Wyoming feedgrounds.Crossref | GoogleScholarGoogle Scholar |

Cross, P., Creech, T., Ebinger, M., Heisey, D., Irvine, K., and Creel, S. (2012). Wildlife contact analysis: emerging methods, questions, and challenges. Behavioral Ecology and Sociobiology 66, 1437–1447.
Wildlife contact analysis: emerging methods, questions, and challenges.Crossref | GoogleScholarGoogle Scholar |

Dawson, D. K., and Efford, M. G. (2009). Bird population density estimated from acoustic signals. Journal of Applied Ecology 46, 1201–1209.
Bird population density estimated from acoustic signals.Crossref | GoogleScholarGoogle Scholar |

Domdouzis, K., Kumar, B., and Anumba, C. (2007). Radio-Frequency Identification (RFID) applications: a brief introduction. Advanced Engineering Informatics 21, 350–355.
Radio-Frequency Identification (RFID) applications: a brief introduction.Crossref | GoogleScholarGoogle Scholar |

Drewe, J. A., Weber, N., Carter, S. P., Bearhop, S., Harrison, X. A., Dall, S. R., McDonald, R. A., and Delahay, R. J. (2012). Performance of proximity loggers in recording intra-and inter-species interactions: a laboratory and field-based validation study. PLoS One 7, e39068.
Performance of proximity loggers in recording intra-and inter-species interactions: a laboratory and field-based validation study.Crossref | GoogleScholarGoogle Scholar |

Dyo, V., Ellwood, S. A., Macdonald, D. W., Markham, A., Mascolo, C., Pásztor, B., Scellato, S., Trigoni, N., Wohlers, R., and Yousef, K. (2010). Evolution and sustainability of a wildlife monitoring sensor network. In ‘Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys10, 3–5 November 2010, Zurich, Switzerland’. pp. 127–140. (ACM: New York.)

Etherington, T. R., Pietravalle, S., and Cowan, D. P. (2010). Visualising uncertainty in radio-telemetry wildlife-tracking data to aid better study design. Wildlife Research 37, 482–488.
Visualising uncertainty in radio-telemetry wildlife-tracking data to aid better study design.Crossref | GoogleScholarGoogle Scholar |

Gibbons, W. J., and Andrews, K. M. (2004). PIT tagging: simple technology at its best. Bioscience 54, 447–454.
PIT tagging: simple technology at its best.Crossref | GoogleScholarGoogle Scholar |

Gorlick, A. (2007). Turtles to test wireless network. Washington Post Jul, 7. Available at https://www.worldbulletin.net/archive/turtles-to-test-wireless-network-h6248.html [Verified 16 October 2018]

Harris, S., Cresswell, W. J., Forde, P. G., Trewhella, W. J., Woollard, T., and Wray, S. (1990). Home‐range analysis using radio‐tracking data–a review of problems and techniques particularly as applied to the study of mammals. Mammal Review 20, 97–123.
Home‐range analysis using radio‐tracking data–a review of problems and techniques particularly as applied to the study of mammals.Crossref | GoogleScholarGoogle Scholar |

Jones, C., Warburton, B., Carver, J., and Carver, D. (2015). Potential applications of wireless sensor networks for wildlife trapping and monitoring programs. Wildlife Society Bulletin 39, 341–348.
Potential applications of wireless sensor networks for wildlife trapping and monitoring programs.Crossref | GoogleScholarGoogle Scholar |

Joshi, A. VishnuKanth, I. N., Samdaria, N., Bagla, S., and Ranjan, P. (2008). GPS-less animal tracking system. In ‘Fourth International Conference on Wireless Communication and Sensor Networks, 27–29 December 2008, Allahabad, India’. pp. 120–125. (IEEE: Piscataway, NJ.) Available at https://ieeexplore.ieee.org/document/4772694/ [Verified September 2018]

Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., and Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. ACM SIGPLAN Notices 37, 96–107.
Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet.Crossref | GoogleScholarGoogle Scholar |

Kays, R., Kranstauber, B., Jansen, P., Carbone, C., Rowcliffe, M., Fountain, T., and Tilak, S. (2009). Camera traps as sensor networks for monitoring animal communities. In ‘IEEE 34th Conference on Local Computer Networks, 20–23 October 2009, Zurich, Switzerland’. pp. 811–818. (IEEE: Picataway, NJ.) Available at https://ieeexplore.ieee.org/abstract/document/5355046/ [Verified September 2018]

Kays, R, Jansen, P. A., Knecht, E. M., Vohwinkel, R., and Wikelski, M. (2011). The effect of feeding time on dispersal of Virola seeds by toucans determined from GPS tracking and accelerometers. Acta Oecologica 37, 625–631.
The effect of feeding time on dispersal of Virola seeds by toucans determined from GPS tracking and accelerometers.Crossref | GoogleScholarGoogle Scholar |

Markham, A., Trigoni, N., Ellwood, S. A., and Macdonald, D. W. (2010). Revealing the hidden lives of underground animals using magneto-inductive tracking. In ‘Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys2010, 3–5 November 2010, Zurich, Switzerland’. pp. 281–294. (ACM: New York.)

Mennill, D. J., Battiston, M., Wilson, D. R., Foote, J. R., and Doucet, S. M. (2012). Field test of an affordable, portable, wireless microphone array for spatial monitoring of animal ecology and behavior. Methods in Ecology and Evolution 3, 704–712.
Field test of an affordable, portable, wireless microphone array for spatial monitoring of animal ecology and behavior.Crossref | GoogleScholarGoogle Scholar |

Moll, R. J., Millspaugh, J. J., Beringer, J., Sartwell, J., and He, Z. (2007). A new ‘view’of ecology and conservation through animal-borne video systems. Trends in Ecology & Evolution 22, 660–668.
A new ‘view’of ecology and conservation through animal-borne video systems.Crossref | GoogleScholarGoogle Scholar |

Pace, R. M. (2001). Estimating and visualizing movement paths from radio-tracking data. In ‘Radio tracking and animal populations’. pp. 189–206. (Academic Press: San Diego, CA.)

Parameswaran, A. T., Husain, M. I., and Upadhyaya, S. (2009). Is RSSI a reliable parameter in sensor localization algorithms: an experimental study. In ‘Field Failure Data Analysis Workshop (F2DA09), September, 2008, New York, NY’. pp. 471–478. University at Buffalo: New York, NY.)

Pedersen, M. W., Burgess, G., and Weng, K. C. (2014). A quantitative approach to static sensor network design. Methods in Ecology and Evolution 5, 1043–1051.
A quantitative approach to static sensor network design.Crossref | GoogleScholarGoogle Scholar |

R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Available at https://www.R-project.org/ [Verified September 2018]

Roberts, C. M. (2006). Radio frequency identification (RFID). Computers & Security 25, 18–26.
Radio frequency identification (RFID).Crossref | GoogleScholarGoogle Scholar |

Rutz, C., and Hays, G.C. (2009). New frontiers in biologging science. Biology Letters 5, 289–292.
New frontiers in biologging science.Crossref | GoogleScholarGoogle Scholar |

Rutz, C., Morrissey, M. B., Burns, Z. T., Burt, J., Otis, B., St Clair, J. J., and James, R. (2015). Calibrating animal‐borne proximity loggers. Methods in Ecology and Evolution 6, 656–667.
Calibrating animal‐borne proximity loggers.Crossref | GoogleScholarGoogle Scholar |

Shen, X., Wang, Z., Jiang, P., Lin, R., and Sun, Y. (2005). Connectivity and RSSI based localization scheme for wireless sensor networks. In ‘Advances in Intelligent Computing’. pp. 578–587. (Springer: Berlin.)

Szewczyk, R., Polastre, J., Mainwaring, A., and Culler, D. (2004). Lessons from a sensor network expedition. In ‘Wireless Sensor Networks’. (Eds H. Karl, A. Willig, A. Wolisz) pp. 307–322. (Springer: Berlin.)

Wall, J., Wittemyer, G., LeMay, V., Douglas‐Hamilton, I., and Klinkenberg, B. (2014). Elliptical time‐density model to estimate wildlife utilization distributions. Methods in Ecology and Evolution 5, 780–790.
Elliptical time‐density model to estimate wildlife utilization distributions.Crossref | GoogleScholarGoogle Scholar |

Wikelski, M., Kays, R. W., Kasdin, N. J., Thorup, K., Smith, J. A., and Swenson, G. W. (2007). Going wild: what a global small-animal tracking system could do for experimental biologists. The Journal of Experimental Biology 210, 181–186.
Going wild: what a global small-animal tracking system could do for experimental biologists.Crossref | GoogleScholarGoogle Scholar |

Zhang, P., Sadler, C. M., Lyon, S. A., and Martonosi, M. (2004). Hardware design experiences in ZebraNet. In ‘Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, SenSys’04, 3–5 November 2004, Baltimore, MD’. pp. 227–238. (ACM: New York.)