A prototype animal borne behaviour monitoring system

Le Roux, Solomon Petrus (2016-03)

Thesis (MEng)--Stellenbosch University, 2016.

Thesis

ENGLISH ABSTRACT: Rhinos across the globe are suffering from an immense onslaught from rhino poachers. The latter show no regard for the remaining rhino species. New technologies must be implemented to provide scientists, biologists and nature conservationists with key information regarding the behaviour and well being of animals. This project entailed the design and development of a prototype Animal Borne Behaviour Monitoring System (ABBMS). The system was based on animal borne sensor driven devices known as WildMotes. The latter were used to collect and communicate sensor data to a base station, by means of a multi-hop Wireless Sensor Network (WSN) that exists between WildMotes, repeaters and the base station. The project considered the hardware design of the WildMotes, which included various components such as an ultra-low power microcontroller, GPS, accelerometer, nano-power tilt and vibration sensor, RF communication module, microSD card and FRAM modules. Apart from the hardware design, the project included all software required of the ABBMS. Initially the WildMotes collected data that could be used for the automatic behaviour classification of animals, by means of computer based techniques. The data was utilised with techniques such as linear- and quadratic discriminant analysis and decision trees, to classify the behaviour of rhinos and sheep with high accuracies. This behaviour included running, walking, standing, grazing and laying down. Furthermore, this project successfully implemented an On-animal Behaviour Classification System (OABCS). To the best of our knowledge, this is the first implementation where behaviour classification is performed in real time, on the animal. This technique provides live updates of animal behaviour, as opposed to post processing, computer based techniques. The OABCS was able to accurately distinguish between similar behavioural classes as above mentioned. In addition to the OABCS, a nano-power tilt and vibration sensor was applied, as an ultra-low power alternative, to classify the behaviour of animals. The latter could accurately distinguish between the same behaviour, while consuming very little energy. This technique was further utilised, in combination with the OABCS, to extend the battery life of the WildMotes from roughly 47 days to 270 days. Finally, GPS coordinates were obtained and utilised to reveal repetitive movement patterns of rhinos, by means of a heat map. In future work, the ABBMS can be combined with the OABCS, GPS locations and key stress level indicators, such as pulse rates, to learn more about endangered species and serve as tools in the fight against illegal poaching activities.

AFRIKAANSE OPSOMMING: Renosters regoor die wêreld ly onder 'n hewige aanslag van renosterstropers. Laasgenoemde toon geen agting vir die oorblywende renosterspesies nie. Nuwe tegnologie moet geïmplementeer word om wetenskaplikes, bioloë en natuurbewaarders met belangrike inligting oor die gedrag van diere te voorsien. Hierdie projek behels die ontwikkeling van 'n prototipe Animal Borne Behaviour Monitoring System (ABBMS). Die stelsel is gebaseer op mobile sensorgedrewe toestelle bekend as WildMotes. Laasgenoemde word gebruik om sensor data te kommunikeer na 'n basis stasie, deur middel van 'n Wireless Sensor Network (WSN) wat bestaan tussen Wildmotes, herhalers en die basis stasie. Die hardeware-ontwerp van die WildMotes sluit verskillende komponente in soos: 'n ultralaekrag mikrobeheerder, GPS, versnellingsensor, nanodrywing kantel-en-vibrasie-sensor, RF kommunikasiemodule, microSD-kaart en FRAM-modules. Afgesien van die hardewareontwerp, is alle sagteware soos benodig vir die projek, ook ontwikkel. Aanvanklik is die WildMotes gebruik om data in te samel om d.m.v. rekenaartegnieke dieregedrag outomaties te klassifiseer. Die data is verwerk met tegnieke soos lineêre-en-kwadratiese diskriminante-analise en besluitbome, om die gedrag van renosters en skape met 'n hoë akkuraatheid te klassifiseer. Die gedrag het hardloop, loop, staan, wei en lê ingesluit. Gedurende die projek is 'n On-animal Behaviour Classi cation System (OABCS) suksesvol ontwikkel. Na die beste van ons kennis, is dit die eerste implementering waar gedragsklassi kasie intyds op die dier uitgevoer word. Hierdie tegniek bied oombliklike opdaterings van die dier se gedrag, in teenstelling met die rekenaargebaseerde tegnieke, waar die inligting eers veel later beskikbaar is. Die OABCS was in staat om akkuraat te onderskei tussen soortgelyke gedragspatrone soos hierbo genoem. Bykomend tot die OABCS, is 'n nanodrywing kantel-en-vibrasie-sensor aangewend, as 'n laedrywing alternatief, om die gedrag van diere te klassifiseer. Dit kon akkuraat onderskei tussen die laasgenome gedragspatrone. Hierdie tegniek is verder benut, in kombinasie met die OABCS, om die batterylewe van die WildMotes te verleng vanaf ongeveer 47 dae tot 270 dae. Laastens was GPS-koördinate verkry en aangewend om herhalende bewegingspatrone van renosters te openbaar, deur middel van 'n hittekaart. In toekomstige werk kan die diere se gedrag in kombinasie met GPS posisies en sleutel stresvlak aanwysers, soos polsslag, gebruik word om meer te leer oor bedreigde spesies.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/98463
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