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© 2015 International Society of Zoological Sciences, Institute of Zoology/
Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd
Integrative Zoology 2015; 10: 572–578 doi: 10.1111/1749-4877.12162
SHORT COMMUNICATION
The application of the Internet of Things to animal ecology
Songtao GUO,1 Min QIANG,2 Xiaorui LUAN,1 Pengfei XU,2 Gang HE,1 Xiaoyan YIN,2 Luo XI,1
Xuelin JIN,1 Jianbin SHAO,3 Xiaojiang CHEN,2 Dingyi FANG2 and Baoguo LI1,4
1College of Life Sciences, Northwest University, Xi’an, China, 2 Information Science and Technology School, Northwest University
Xi’an, China,3 Niubeiliang National Nature Reserve, Xi’an, China and 4 Xi’an Branch of Chinese Academy of Sciences, Xi’an,
China
Abstract
For ecologists, understanding the reaction of animals to environmental changes is critical. Using networked sen-
sor technology to measure wildlife and environmental parameters can provide accurate, real-time and compre-
hensive data for monitoring, research and conservation of wildlife. This paper reviews: (i) conventional detec-
tion technology; (ii) concepts and applications of the Internet of Things (IoT) in animal ecology; and (iii) the
advantages and disadvantages of IoT. The current theoretical limits of IoT in animal ecology are also discussed.
Although IoT offers a new direction in animal ecological research, it still needs to be further explored and de-
veloped as a theoretical system and applied to the appropriate scientic frameworks for understanding animal
ecology.
Key words: animal detecting, environmental monitoring, Internet of Things, wireless sensor network
Correspondence: Xiaojiang Chen and Baoguo Li, Northwest
University, North Taibai Road, Xi’an 710069, China.
Email: xjchen@nwu.edu.cn; baoguoli@nwu.edu.cn
INTRODUCTION
Animal ecology is the science of the relationships be-
tween animals and their surrounding environment - both
organic and inorganic. To determine what mechanisms
result in adaptations of animals to the environment, pa-
rameters such as morphology, physiology, behavior, de-
velopment and population dynamics, including interac-
tion with predators and competitors, need to be assessed.
There are many well-established methods and tech-
niques applied in animal ecology, and recent advances
have enabled novel research perspectives and opportuni-
ties for the discipline. We reviews some commonly used
research methods for animal population ecology, and in-
vestigate the current and future application of the “Inter-
net of Things” (IoT) technology in this eld as well as
its advantages and disadvantages.
CONVENTIONAL DETECTION
TECHNOLOGY IN ANIMAL ECOLOGY
The earliest method to measure the behavioral pat-
terns of animals was by direct observation through eld
tracking and observing individuals and groups. This
method is still the main approach to study many spe-
cies, including diurnal vertebrates (Wilson & Delahay
2001). However, this approach may cause subjects to
deviate from their natural behavior due to human distur-
bance, which can compromise the resulting data (Harris
& Burnham 2002).
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© 2015 International Society of Zoological Sciences, Institute of Zoology/
Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd
With the development of radio telemetry, the ecology
and behavior of difficult-to-track vertebrates could be
more reliably recorded, including wide-ranging marine
vertebrates such as turtles (Dizon & Balazs 1982) and
rare, secretive nocturnal predators such as leopard cats
(Grassman et al. 2005). Radio tracking devices incor-
porating global positioning systems (GPS) can record
animal activity patterns at much larger spatial scales:
for example, for migratory birds (Guo et al. 2015) and
mammals. This new technology has revealed in more
detail the population dynamics and reproductive biolo-
gy of several vertebrate species. The use of GPS tech-
nology has been reviewed previously for some mam-
mals and birds (Wu et al. 2008; Miller et al. 2010), with
emphasis on the limitations of such devices. For exam-
ple, data recorders are usually only effective for 1 to 3
years due to battery life, radio signals are sometimes un-
reliable, and units need to be recovered for data access,
which may increase the potential for accidental injury in
some species.
The movements and populations of animals, mainly
vertebrates, can be recorded without intrusion using tra-
ditional tracking methods. For example, the sizes, and
the sex and age make-ups of ungulate populations (Jiang
et al. 2001) and the habitat preferences of wolves (Li et
al. 1999, 2005; Liu et al. 2002; Zhang et al. 2010) can
be estimated by footprints left in snow. Counting dung
patches is an effective method of estimating forest musk
deer numbers (Wei et al. 1995). However, such methods
are unlikely to accurately record individual behavior,
and because quantitative predictions of populations sub-
sequently need to be made by extrapolation, the accura-
cy of the data may be affected.
In recent years, several new methods that avoid di-
rect human contact with animal subjects have been de-
veloped for measuring and recording ecological data for
animals in the eld. One such method, which is being
increasingly applied, is the use of static infra-red camer-
as to record the presence and movement of cryptic, noc-
turnal vertebrates. For example, this method has been
used to record the abundance of large cats such as jag-
uars (Panthera oncas) (Trolle & Kéry 2005; Heilbrun
et al. 2006) and tigers (Panthera tigris) (Carbone et al.
2001), ungulates (Jia et al. 2014) and other species of
mammals (Lu et al. 2005; Rovero & Marshall 2009).
The use of infra-red cameras to monitor and investigate
wildlife has become popular in China (Xiao et al. 2014).
Radio frequency identification (RFID) has been de-
veloped for tracking and marking individual animals
(Huang & Ku 2009). RFID is a wireless communica-
tion technology useful for precisely identifying ob-
jects by using radio frequency waves to transfer identi-
fying information between tagged objects and readers.
Each RFID has a unique identity code, also known as
RFID tag, which can be attached to an animal as its “ID
card.” RFID tags are often classied as being either ac-
tive or passive. Active RFID tags have an internal pow-
er source and emit a radio signal that can be detected at
a distance; they tend to be used for short time periods
before removal. Passive RFID tags do not emit a sig-
nal but require a powered detection device near the sub-
ject animal for monitoring and can be used throughout
an animal’s life. RFID tagging can be applied in track-
ing individuals to study population diffusion, individual
movements in space and over time, and to reveal other
important information on population ecology (Schooley
et al. 1993; Harper & Batzli 1996; Rogers et al. 2002).
This technique has been widely used in captive animals
in the management of their reproduction and communi-
cation, and these new detection technologies are likely
to make the study of wild animals easier.
However, the use of RFID also usually requires direct
observation and monitoring, which is time-consuming
and labor-intensive, with multiple subjects necessary
to answer most scientific questions on animal ecolo-
gy. Other problems with the use of RFIDs have been re-
ported. For example, when RFID tags have been used
on burrowing animals, electromagnetic signals emitted
by RFID units have been weak and difficult to detect.
The electromagnetic waves suffer from severe attenua-
tion due to soil and moisture when the conventional ra-
dio-tagged methods are used to locate the burrowing
animals. To solve this problem, researchers have used
magnetic induction wireless positioning, applying au-
tomatic sensor array equipment, which is unaffected by
water or soil. This equipment can track burrowing mam-
mals, such as the European badger (Meles meles), au-
tomatically to obtain the position of each individual in
a sett at a particular point in time. These data can then
be used to produce a graphic visualization of the move-
ments of all individuals within a sett superimposed on a
diagram of the tunnel network (Markham et al. 2010).
The use of IoT in animal ecology can be based on such
technologies.
THE CONCEPT AND BASIC
FRAMEWORK OF THE INTERNET
OF THINGS APPLIED IN ANIMAL
ECOLOGY
In 2005, the International Telecommunications Union
(ITU) formally presented the concept of the IoT. The
IoT incorporates all kinds of sensing networks, such
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© 2015 International Society of Zoological Sciences, Institute of Zoology/
Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd
as RFID devices, GPS technology and laser scanners,
which are connected to the Internet to form a supernet-
work. People can manage their working and home lives
more efficiently and dynamically through the IoT, in-
crease productivity, and improve the relationship be-
tween humans and nature.
The IoT is a vast network, formed by combining ob-
jects, processes and information with the Internet. The
objects and the processes, which need to be monitored,
connect or interact during real time, and the informa-
tion conveyed includes sound, light, heat, electrical,
mechanical, chemical, biological and location infor-
mation that can be gathered by a variety of sensing de-
vices. These include wireless sensor networks (WSNs),
RFID technology, GPS technology, infrared sensors, la-
ser scanners, gas sensors and other equipment and tech-
nologies (He et al. 2006; Werner-Allen et al. 2006; Liu
et al. 2011; Mao et al. 2012). Typically, IoT architecture
is classified into 4 layers: (i) the perceived identifica-
tion layer; (ii) the network construct layer; (iii) the man-
agement service layer; and (iv) the integrated applica-
tion layer (Thiagarajan 2009). Hence, IoT is a network
formed from a variety of sensors, which are used as ba-
sic detection nodes. The subjects of detection may be a
variety of inanimate objects, those associated with liv-
ing organisms and environmental parameters. The cen-
tral computing system processes the data sent from the
detectors, which are then fed to the effectors or manag-
ers for appropriate processing depending on the applica-
tion (Dargie & Poellabauer 2010).
In some circumstances, IoT has thus been fully ap-
plied to the ecology of certain animal species. Because
of different research goals and various means of detec-
tion, methods to process the data retrieved by sensors,
and interaction between those data, the detectors and the
effectors, will differ. IoT systems involve the simulta-
neous use of multiple information engineering technol-
ogies applied to ecological principles. These complex
networks can be divided into different multi-stage feed-
back paths (as shown in Fig. 1). Briefly, they are pro-
cesses incorporating signal detection, data processing,
and feedback. The physical or chemical data can be ob-
tained by corresponding detectors in the process of de-
tecting signals, and then transferred to the server and
processed. Finally, researchers and managers can make
judgments as to how best to obtain the optimal data to
achieve specic goals. For hardware conguration, the
most important components are the detector nodes and
the network supporting data transmission. Research-
ers need to set up these nodes according to specic en-
vironmental conditions based on the known ecological
information about the animals to be studied. In accor-
dance with the physiology of the animals to be stud-
ied, researchers should use appropriate detectors, such
as acoustic, optical or chemical detectors, to record and/
or track the target animals. Researchers should take into
account the known behavior and/or ecology of the target
species to decide the optimal number and the layout of
detector nodes to be used.
Poter et al. (2005) discuss how to arrange an IoT net-
work, according to specic research goals, data type and
other conditions. The cost of the individual nodes has
fallen with the rapid development of data transmission
and detector technologies. The transmission of much
data over a relatively short time no longer creates a bot-
tleneck within a network deployment due to the devel-
opment of compression and transmission technologies.
Currently, various embeddable detectors can be used as
detection nodes in an IoT network (International Tele-
communication Union ITU 2005). A network can trans-
fer real-time data from each node and continuously re-
cord various environmental parameters.
THE APPLICATION OF THE INTERNET
OF THINGS TO ANIMAL ECOLOGY
Currently, various forms of emerging IoT technolo-
gy have been applied to animal ecology research. Var-
ious species of terrestrial and aquatic vertebrates, for
instance, have been identied through computer-assist-
ed technology. For example, a database of individual
Grevy’s Zebra (Equus grevyi) stripe-patterns has been
established, based on “noisy” images obtained from a
locality favored by Grevy’s Zebras. The images of a
new, previously unknown animal can be automatical-
ly compared with those already in the database to deter-
mine whether an animal is a Grevy’s Zebra or another
species (Lahiri et al. 2011).
The “RatPack” project at Rheinisch-Westfaelische
Technische Hochschule Aachen (RWTH Aachen) aims
to monitor the behavior and movements of underground
rodents using wireless sensor nodes to perform sound
analysis, position calculation and terrain rendering, and
provides a new method for studying rodent behavior in
the natural environment (Bitsch et al. 2010). Research-
ers at Antilles and Guyana University (UAG) have pro-
posed a method to identify the cooperative behavior
of birds by recording the movements and songs of in-
dividuals, using an acoustic sensor array comprised of
a series of wireless microphones. Different bird spe-
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© 2015 International Society of Zoological Sciences, Institute of Zoology/
Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd
cies have been automatically identied using a wireless
acoustic sensor array (Chen et al. 2006).
Acoustic sensors have also aided researchers in the
eld in recording the sound activities of individual mar-
mots, by conrming the position of individuals making
alarm calls via a network of wireless acoustic recorders
(Ali et al. 2009). Wireless sensor network technology
has also been used to monitor large animal populations
over a wide area. This technique has provided high-res-
olution data for monitoring seabirds that nest in the UK
(Naumowicz et al. 2008), and for monitoring and count-
ing the relative numbers of native frogs to invasive cane
toads (Bufo marinus) in northern Australia (Hu et al.
2009). All the work mentioned has used various types of
physical detectors to study activity patterns, and to iden-
tity individual animals after the detected signal was pro-
cessed in an Internet-connected data processing center.
These network nodes can also detect environmental
data simultaneously to data recorded from the target an-
imal. Furthermore, the impact of environmental chang-
es on animal behavior and ecology can be assessed from
these automatically recorded data. For example, un-
derwater sensor network platforms have been used for
long-term monitoring of coral reefs and sheries, using
sensor networks that include both static and underwater
mobile sensor nodes. These are capable of recording im-
ages, water temperature and pressure, and other infor-
mation. Similar systems can also be used for still-water,
river and open-ocean data collection (Vasilescu et al.
2005). IoT can also be used as a wildlife management
tool. For example, in zoos, wireless video nodes can be
installed to form a visual sensor network, so animals’
movements and behavior can be monitored remotely.
Vibrotactile collars connected to a sensory network can
also be used for behavioral management of zoo mam-
mals. These integrated systems provide a new interac-
tive way for humans to manage wildlife (Fahlquist et al.
2010). This zoo example can be considered a full appli-
cation of IoT, in that it covers most IoT basic elements,
including the identication of individuals, a network of
detectors, and several platforms for information feed-
back and processing.
Two recent systematic studies in China exemplify pi-
oneering work in the eld of IoT. The Green Orbs proj-
ect has been conducted at some major universities, with
a collaborative project involving the Hong Kong Uni-
versity of Science and Technology, Tsinghua University,
Xi’an Jiaotong University, Zhejiang Forestry University,
Zhejiang University, University of Illinois and Nanyang
Technological University. This involved wireless sen-
sor networks on the Tianmu Mountains in Lin’an Zheji-
ang to monitor carbon missions, plant life and wildlife.
Much data has been recorded for temperature, humidity,
light and carbon dioxide concentrations, and other pa-
Figure 1 The wireless sensor net-
work in animal and environmen-
tal monitoring.
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© 2015 International Society of Zoological Sciences, Institute of Zoology/
Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd
rameters for forest monitoring, re risk assessment and
wildlife rescue. However, this work only partly mon-
itors the environmental parameters of wildlife habitat
(Liu et al. 2013). Northwest University in Xi’an, Chi-
na has applied IoT in the wildlife reserve inhabited by
endangered golden snub-nosed monkeys (Rhinopithe-
cus roxellana) in the Qinling Mountains. Aimed at iden-
tifying the particular requirements for wildlife protec-
tion, researchers used limited IoT resources to monitor
the activities of monkeys and to collect environmental
data. These data were transmitted to a control center via
Quality of Service, which is a network security mecha-
nism used to address issues such as network latency and
blocking. Finally, this work has added to information
needed for the protection of R. roxellana at the Qinling
Mountains (Liu et al. 2011).
ADVANTAGES AND DISADVANTAGES
OF INTERNET OF THINGS FOR ANIMAL
ECOLOGY RESEARCH
The IoT is considered an emerging industry, and has
considerable potential for development. IoT has many
technological advantages for ecological research and the
monitoring of wild animals. First, IoT can acquire data
continuously, and also adjust the frequency of data col-
lection through remote adjustment of the sensors, which
effectively increase the service time of power supplies.
Second, IoT can remotely monitor animals and their en-
vironment, and, thus, exclude any effects of human in-
terference to record data more objectively. Additionally,
a network can function for a long period of time (as op-
posed to humans) and provide interactive services such
as reminders and alerts for users by setting of thresholds
on the back-end server by the operator. Finally, after in-
stalling the management devices, IoT can implement the
interaction with the user under the control of the net-
work client, and improve the efciency of animal moni-
toring and management.
There are some problems in applying IoT, including
issues with the short life of batteries, incompatible sen-
sor components and transferring data, particularly large
video les (Tan et al. 2010). However, such disadvan-
tages will be improved with the development of the IT
industry: for example, with sensors using new long-life
solar energy drive batteries, developing uniform inte-
grated sensor components, and quality of service tech-
niques making the transfer of data more stable (Amardeo
& Sarma 2009). Further IoT application in animal ecol-
ogy research will require not only the input of IT profes-
sionals, but also the design ideas of animal ecologists.
CONCLUSION AND RECOMMENDATION
The application of IoT is a new direction for ecolog-
ical research. However, except for the general frame-
works mentioned above, we still regard IoT as a theoret-
ical system and a “work in progress” requiring further
development for its full application to research in ani-
mal ecology. Firstly, we recommend that potential users
carefully consider sensor type to be use and overall de-
sign of the frameworks for each species-specific proj-
ect. Animals in different habitats will require different
sensor congurations, power supplies and data transfer
strategies. For example, the optimal type and density of
the sensor will differ for grasslands or forests. Second-
ly, users need to design and optimize the specic strat-
egies for energy output, data storage and battery model
in order to maximise battery life according to their spe-
cic research goals and the funds available for a given
project. Finally, users should design a strategy for data
transfer and storage, and prioritize the most important
parameters to measure. Users need to ensure that their
IoT data can be transferred and saved safely and effec-
tively under a limited network bandwidth. Ecologists
should make full use of the advantages of this advanced
technology. Using IoT in diverse habitats will present
challenges to animal ecologists, however it will provide
novel opportunities to study animals in their natural
habitat and lead to improvements as this exciting new
technology evolves.
ACKNOWLEDGMENTS
We thank Dr Derek Dunn for improving the En-
glish in the present paper. This research was support-
ed by: the National Nature Science Foundation of Chi-
na (grant numbers: 31130061, 31270441, 61170218
and 61272461); the FokYing Tung Education Foun-
dation (grant number: 131105); and the National Sci-
ence Foundation for Fostering Talents in Basic Research
of the National Natural Science Foundation of China
(J1210063).
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