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

Along with the proliferation of mobile devices and wireless signal coverage, IoT devices, such as smart wristbands 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 embedded 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 reduced 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 Donana National Park with unidirectional antennas and log-normalization distance estimation over RSSI.

[1]  Imrich Chlamtac,et al.  Internet of things: Vision, applications and research challenges , 2012, Ad Hoc Networks.

[2]  Hannu Tenhunen,et al.  Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems , 2015, 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC).

[3]  Yongzhen Li,et al.  Design of energy consumption monitoring and energy-saving management system of intelligent building based on the Internet of things , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[4]  H. Søgaard,et al.  ZigBee-based wireless sensor networks for monitoring animal presence and pasture time in a strip of new grass , 2008 .

[5]  Thomas Bak,et al.  ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees , 2008 .

[6]  Muhammad Aamir,et al.  Internet of Things (IoT) enabled smart animal farm , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[7]  Sherali Zeadally,et al.  An Analysis of RFID Authentication Schemes for Internet of Things in Healthcare Environment Using Elliptic Curve Cryptography , 2015, IEEE Internet of Things Journal.

[8]  Esmaeil S. Nadimi,et al.  Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks , 2012 .

[9]  Bruno Bellini,et al.  A 5μΑ wireless platform for cattle heat detection , 2017, 2017 IEEE 8th Latin American Symposium on Circuits & Systems (LASCAS).

[10]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[11]  Daniel Gutierrez-Galan,et al.  Semi-wildlife gait patterns classification using statistical methods and Artificial Neural Networks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[12]  Amr Mohamed,et al.  Characterization of the indoor-outdoor radio propagation channel at 2.4 GHz , 2011, 2011 IEEE GCC Conference and Exhibition (GCC).

[13]  Thomas Watteyne,et al.  Understanding the Limits of LoRaWAN , 2016, IEEE Communications Magazine.

[14]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[15]  Daniel Gutierrez-Galan,et al.  Embedded neural network for real-time animal behavior classification , 2018, Neurocomputing.

[16]  Daniel Gutierrez-Galan,et al.  Wireless Sensor Network for Wildlife Tracking and Behavior Classification of Animals in Doñana , 2016, IEEE Communications Letters.