A Low-power, Reachable, Wearable and Intelligent IoT Device for

Animal Activity Monitoring

L. Duran-Lopez, D. Gutierrez-Galan, J. P. Dominguez-Morales, A. Rios-Navarro,

R. Tapiador-Morales, A. Jimenez-Fernandez, D. Cascado-Caballero and A. Linares-Barranco

Robotics and Technology of Computers Lab., University of Seville, Seville 41012, Spain

Keywords:

Low-power, Wearable, Artiﬁcial Intelligence, IoT, Activity Monitoring.

Abstract:

Along with the proliferation of mobile devices and wireless signal coverage, IoT devices, such as smart wrist-

bands for monitoring its owner’s activity or sleep patterns, get great popularity. Wearable technology in human

life has become quite useful due to the information given (sleep hours, heart rate, etc). However, wearables

for animals does not give information about behaviour directly: they collect raw data that is sent to a server

where, after a post-processing step, the behaviour is known. In this work, we present a smart IoT device that

classiﬁes different animal behaviours from the information obtained from on-board sensors using an embed-

ded neural network running in the device. This information is uploaded to a server through a wireless sensor

network based on Zigbee communication. The architecture of the device allows an easy assembly in a re-

duced dimension wearable case. The ﬁrmware allows a modular functionality by activating or deactivating

modules independently, which improve the power efﬁciency of the device. The power consumption has been

analyzed, allowing the 1Ah battery to work the system during several days. A novel localization and distance

estimation technique (for 802.15.4 networks) is presented to recover a lost device in Do

nana National Park

with unidirectional antennas and log-normalization distance estimation over RSSI.

1 INTRODUCTION

The tendency of ”Internet of Things” is to connect

everything to the Internet, including people and ani-

mals. New wearable devices appeared for health care

and activity monitoring. In recent years, the tendency

is also to be able to get information from pets and

animals which live in farms, as dairy cows (Nadimi

et al., 2008a; Nadimi et al., 2008b; Nadimi et al.,

2012). There are many application under the concept

of IoT in different ﬁelds (Miorandi et al., 2012) that

are currently hot topics, such as: Smart Buildings

(focused on reducing the consumption of resources

related to the building (Wei and Li, 2011) and im-

prove the satisfaction of the human populating it),

Smart Cities (create a cyberphisycal eco-system of

interconnected elements that are able to optimize the

usage of physical city infrastructures and the quality

of life for its citizens (Zanella et al., 2014)), Environ-

mental Monitoring (real-time and on device process-

ing along with the interconnection of several devices

for detecting and monitoring anomalies that can lead

to endangering human and animal life (Bellini and

Amaud, 2017; Memon et al., 2016)), Smart Business

(IoT technologies for monitoring product availability

in real-time and maintaining a precise stock inven-

tory (Xu et al., 2014), (Lindsay and Reade, 2006)),

Health-care (Patients carrying sensors that monitor

parameters such as body temperature, blood pressure,

breathing activity along with other wearable sensors

(accelerometer or gyroscopes) (Rahmani et al., 2015;

He and Zeadally, 2015)).

The IoT solution presented in this paper is under

the topic of environmental and wildlife monitoring.

Although in the literature it can be found some appli-

cations for farm animals, this work presents a solution

for wild and semi-wild animals living in an environ-

ment where there is not a Wide Area Network, e.g.

LoRaWAN (Low Power Wide Area Network) (Ade-

lantado et al., 2017).

An intelligent and wearable collar to monitor the

animal activity is presented, along with the com-

munication infrastructure for setting up the Wireless

Sensor Network (WSN) for animals monitoring. It

consists of an embedded implementation of a neu-

ral network for microcontroller that classiﬁes the ani-

mal patterns and uploads the information to a remote

database that can be accessed from the Internet. Sec-

516

Duran-Lopez, L., Gutierrez-Galan, D., Dominguez-Morales, J., Rios-Navarro, A., Tapiador-Morales, R., Jimenez-Fernandez, A., Cascado-Caballero, D. and Linares-Barranco, A.

A Low-power, Reachable, Wearable and Intelligent IoT Device for Animal Activity Monitoring.

DOI: 10.5220/0008493505160521

In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019), pages 516-521

ISBN: 978-989-758-384-1

tion 2 presents the collar as an IoT device, describ-

ing its features, its main hardware components and

its functionality. Section 3 details the communica-

tion infrastructure topology. Section 4 describes the

experiments carried out with horses in Do

nana Na-

tional Park. Section 5 presents the results of the ex-

periments. Finally, the conclusions of this work are

presented in Section 6.

2 COLLAR AS AN IoT DEVICE

The presented collar has been designed taking into

account the main IoT devices requirements: low-

power consumption, small size and reconﬁgurability

along with artiﬁcial intelligence (AI) integration. It

represents a manufacturing oriented design from the

previous prototype version (Gutierrez-Galan et al.,

2017b). New features have been added to improve

the prototype that directly affect how well the system

works. The collar features, hardware functionalities

and ﬁrmware are described below:

2.1 Features

Main novelties over the most common ones in other

IoT devices for monitoring are:

• Ultra-low-power Consumption: if a conserva-

tive policy of data transmission is taken (collect-

ing raw data, processing it into the collar, and

sending only the results) the battery life of the de-

vice will be extended (Dominguez-Morales et al.,

2016).

• Modular: the user is able to activate/deactivate

collar functionalities on-the-ﬂy if they are needed

or not. It can be done in real time sending basic

commands from Internet to the communication

infrastructure. So, the collar is user-customizable.

• Conﬁgurable: parameters of ﬁrmware modules

can also be updated in order to support different

operating options (e.g. time between data trans-

missions, sleep mode parameters, etc).

• Intelligence: raw data can be processed by the

embedded Artiﬁcial Neural Network (ANN) that

is implemented on the collar (Gutierrez-Galan

et al., 2017b; Gutierrez-Galan et al., 2017a), be-

fore their transmission. Thus, thanks to conﬁg-

urable novelty, an user could change from one

trained NN parameters to another one deployed in

the collar on-the-ﬂy. Therefore, valid processed

information is transmitted, instead of raw data

for a later processing step, what considerably de-

creases the power consumption.

(a) Collar PCB & Antenna (b) Collar device dur-

ing the experiments.

Figure 1: (a) From left to right: 1) Antenova Asper

2.4G/GNSS Antenna, 2) PCB back view, showing the SD

card slot.

2.2 Hardware Description

The collar dimensions are 25.10 x 78.05mm. It

is based on an ARM Cortex M0+ microcon-

troller unit (MCU), which belongs to an ultra-

low power consumption family. In particular, the

STM32L072RZT6. This family allows to change

the MCU running mode between full, low-power and

ultra-low-power modes. This MCU has a power con-

sumption of 0.86 µA and 0.29 µA in low-power mode,

and in ultra-low-power mode, respectively.

To transmit the information, a XBee PRO S2B

module is included, since it is able to be conﬁgured

either as a point-to-point network or as a mesh net-

work to establish a WSN. Although this module is not

a low-power module, power consumption can be re-

duced by sending a sleep request.

A full inertial measurement unit (IMU)

with a

3-axis accelerometer, a 3-axis gyroscope, and 3-axis

magnetometer is included and connected through I2C

bus. This unit obtains the animal activity information

(as behavioral patterns or physical activity) from raw

data.

Furthermore, to complement the movement data,

a high performance, ultra-low-power GPS module

(Quectel L70-RL) is used. Finally, a Hall-Effect cur-

rent sensor is also included on the collar to have bat-

tery state information.

As in every network, communication problems

may occur if the device is out of range, missing in-

formation about collars. For this purpose, the device

carries an SD card for storing data when the collar is

either inside or outside of the network. Data can be

read from the SD card and transmitted throw the net-

work when the collar returns to the network coverage

area. In case of collar loss, ie. the animal has lost the

collar in the countryside, it can be recovered while the

See MinIMU-9 v5 (LSM6DS33 and LIS3MDL)

A Low-power, Reachable, Wearable and Intelligent IoT Device for Animal Activity Monitoring

517

battery has power. The collar could detect that there

is no animal attached and enter in a low power mode

that allows radio coverage detection. Using the unidi-

rectional designed XBee antenna attached to a laptop,

the distance from the laptop to the lost collar can be

estimated, together with the direction, in order to lo-

calize the device.

The battery selected to power the collar is a LiPo

of 1 A h. It is connected to DC-DC regulator, which

allows us to charge and also turn on/off the device.

A magnetic on/off switch allow all the components

(PCBs, SDcard, antenna, battery, ...) to be isolated

into a special epoxy resin to make the collar robust

enough to be used in semi-wild animals.

2.3 Firmware

The intelligence of this device resides in the NN im-

plemented in the microcontroller and its capability to

be reconﬁgured.

The low power consumption is achieved by us-

ing a timer, which throws a periodic interruption ev-

ery 1,5 seconds (user-conﬁgurable). When an inter-

ruption occurs, a state machine is executed. This

state machine has several states, and some of these

states can be disabled if they are not needed to save

power. There are mandatory states, such as INIT,

for device initialization; or WAIT, where the system

is waiting for GPS valid signal and to be joined to

a WSN. But there are also optional states which es-

tablish how the device works: BEHAVIOR(1), CAP-

TURE(2), SEND(3), SLEEP(4), EMERGENCY(5)

and RECOVERY(6) modes.

In (1), data is collected from the IMU, processed

by an NN every 1.5 seconds (user-conﬁgurable) and

stored in the SD memory. Mode (2) is used when new

raw data is required in order to train the NN, thus in

this mode the NN is disabled. User only could select

one of these two running modes. When the collar has

behavioral information, mode (3) could be enabled to

transmit the behavior pattern along with the time, or

disabled to save battery in any special situation. After

monitoring, the device goes to mode (4) to enter the

MCU in ultra-low-power consumption state. Mode

(5) is automatically activated when collar remains a

speciﬁc number of days out of range, in order to try

to be found by the mobile node using the distance es-

timator algorithm. In that mode, the collar transmits

simple packets periodically during a speciﬁc time in-

terval. Finally, (6) is used only when the collar must

be recovered for extracting the information stored in

the SD card, eg. the animal has lost the collar. This

mode can be activated remotely or automatically if the

collar is not detecting any activity for a conﬁgurable

period of time.

The embedded version of the ANN implemented

in the device is able to obtain a 90% of hit average

using a simple three layers architecture (Gutierrez-

Galan et al., 2017b).

3 IoT SYSTEM: NETWORK

TOPOLOGY

In some IoT based systems, there exist a central node

or master, which coordinates the communications be-

tween slave devices and the Internet. And it also

has the goal of creating the network that allows to

get the collars distributed without data. In this work,

we have used a complete infrastructure to deploy a

WSN which is able to have a wide network that cover

the land extension needed (Dominguez-Morales et al.,

2016).

Figure 2: Block diagram of network’s communication sys-

tem.

3.1 Subsystems Description

The described collar IoT device information is com-

municated to the Internet through a communication

infrastructure composed of the following items:

• Base Station: it serves as the WSN coordina-

tor. It receives packets from collars through the

mote network and upload them to a remote web

server using WiFi or wired connection. It is

also provided with a set on sensors for measur-

ing the environmental conditions. It is composed

of a BridgeBoard PCB (Gutierrez-Galan et al.,

2017b); an Intel NUC as main host; a 60A battery;

two antennas (wiﬁ and zigbee) and a solar panel

(1476 mm × 659 mm × 35 mm). All components,

but the solar panel, are enclosed in a hermetic box.

• 802.15.4 Sniffer and Mobile Device: it allows

to track animals using the GPS information from

NCTA 2019 - 11th International Conference on Neural Computation Theory and Applications

518

collars or using the RSSI information obtained

from a directional antenna. For this purpose, a

802.15.4 sniffer has been developed, which cap-

tures the data packets sent by all nodes of the net-

work and is capable of analyze and obtain infor-

mation about signal strength and location. The

sniffer is a NesC implementation of a platform-

independent IEEE 802.15.4 MAC protocol (Uni-

versity of California, 2009) called TKN15.4 run-

ning on a TelosB CM5000-SMA mote. It is

connected to a portable device through an USB

port, where a desktop application collects and pro-

cesses the information. It serves as a deployment

assistant, when is used to sense the information

received from other stations and assessing the ef-

fective range of coverage of each ﬁxed node and

collar.

• Mote Network: it is a set of XBee devices con-

ﬁgured as routers, which are connected in a mesh-

fashion and placed in speciﬁc spots of the park

to increase the coverage area. It includes a Li-Po

battery and a small solar cell. Its goal is to route

the packets received from the collars to the base

station.

4 CASE OF USE: DO

NANA

NATIONAL PARK

The IoT system has been tested in Do

nana National

Park. The area where the tests were performed is

shown in Fig. 3, a wooded zone with a high density

of vegetation. Base station and motes are signalized.

Firstly, the base station was established in a WiFi-

accessible area in order to be connected to the remote

web server. Next, the mote network was deployed

around the base station at speciﬁc spots to obtain the

maximum coverage (Fig. 3), using the mobile device

with a non-directional antenna (with a gain equals

to the collar’s antenna). In this way, we can deter-

mine the range by observing the maximum distance at

which packets (coming from one mote) are received

from the sniffer (which is equivalent to the collar)

with the signal strength above the sensitivity thresh-

old of the collar. The maximum distance between two

consecutive nodes was 127m, while the other two did

not exceed more than 90m between them because of

being in a high-density vegetation area.

The collars were placed to horses from different

breeds in order to test the robustness of the classi-

ﬁer. At the same time, the SD card on each collar

was logging the sensors data. This data will be used

to increase the size of the training dataset of the neural

Figure 3: Satellite view of working area at Do

nana. Blue

area (BS) represents the base-station range. Green zones

represent motes range (M1-M3).

network.

Finally, we tested the capability of the mobile

node to ﬁnd an out of range collar. The mobile node

uses a directional antenna, an emergency network co-

ordinator (that works in a different frequency than the

rest of the motes), and a software that is capable of

painting the position of the collar in a map, depend-

ing on the GPS data, or the signal strength received

from the collar, using a log-normal distance estima-

tor (Ahmed et al., 2011). Besides, the software emits

a beep whose pitch depends on the received signal

strength. In order to realize this test, the network of

motes and the GPS device of the collar were disabled.

The collar was placed at maximum distance of 90m,

in a unknown place to the user of the mobile node.

Figure 4: Mobile app screenshot while searching a lost col-

lar. The possible collar location is the red cross. At left side,

the distance estimation, the relative orientation respect the

mobile node and the estimated collar localization.

A Low-power, Reachable, Wearable and Intelligent IoT Device for Animal Activity Monitoring

519

5 RESULTS AND DISCUSSION

In the deployment of the network we observed con-

nection issues between the motes M2 and M3 of Fig.

3 due to the closeness of trees and the high variability

of the signal strength. The RSSI received at the dis-

tance between motes was 10dBi over the sensibility

threshold of the motes, but considering the connec-

tion losses, this margin must be larger.

Regarding the location testing, several tests were

done in the area, being easy to ﬁnd the collar with the

mobile node in no more than 4 minutes. The acous-

tic clues and the distance estimation were enough to

drive the mobile node towards the collar. The direc-

tionality of the antenna was good enough, as can be

seen in Fig. 5. However, the distance estimation is

very dependent on the signal conditions, which de-

pends on the scenario (trees, grass, humidity, build-

ings) and for this reason, no precise information about

location could be obtained.

Figure 5: RSSI values received from a directional antenna,

depending on the angle of orientation.

6 CONCLUSION

In this paper, we have presented an intelligent, low-

power and reconﬁgurable device powered by a low-

power MCU that is capable of acquiring behavioral

information from its owner by classifying the data ob-

tained from several sensors using an embedded NN

implementation. It sends this output to a coordinator

(base station) which uploads this to a database that

can be accessed through a web portal. The commu-

nication is done using XBee through a WSN where

solar-powered motes route the information from col-

lars to the base station, increasing the coverage area

of the network. The collar is reconﬁgurable and can

be adapted to the requirements of the scenario by ac-

tivating/deactivating different software functionalities

of the device.

The whole network has been deployed and tested

in Do

nana National Park, where the collar was placed

on horses to monitor their activity and classify be-

tween different gait patterns. Other WSN implemen-

tations like LoRa requires each of the devices to be

connected to the Internet (Adelantado et al., 2017),

which would be difﬁcult in Do

nana National Park

and other environments with reduced WAN connec-

tion. Our system is also scalable and the coverage

area could be expanded by adding more motes to the

network.

Each of the devices that are part of the WSN has

been tested individually and in the setup where the

experiments were done, showing the expected behav-

ior. The information about the gaits that the collar

detected from the horse movement patterns were cor-

rectly sent to the base station and uploaded to the

database, showing an accuracy of 90%.

ACKNOWLEDGEMENTS

This work is supported by the excellence project

from Andalusia Council MINERVA (P12-TIC-1300)

and also by the Spanish government grant (with sup-

port from the European Regional Development Fund)

COFNET (TEC2016-77785-P). The authors would

like to thank R. Soriguer, F. Carro and the EBD-CSIC

for their support in Do

nana National Park. L. Du-

ran is supported by the Empleo Juvenil with support

from EU. The work of J.P. Dominguez-Morales was

supported by a Formaci

on de Personal Universitario

Scholarship from the Spanish Ministry of Education,

Culture and Sport.

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