422 research outputs found
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
Long-term tracking and monitoring of mobile entities in the outdoors using wireless sensors
There is an emerging class of applications that require long-term tracking and monitoring
of mobile entities for characterising their contexts and behaviours using data
from wireless sensors. Examples include monitoring animals in their natural habitat
over the annual cycle; tracking shipping containers and their handling during transit;
and monitoring air quality using sensors attached to bicycles used in public sharing
schemes. All applications within this class require the acquisition of sensor data
tagged with spatio-temporal information and uploaded wirelessly. Currently there is
no solution targeting the entire class of applications, only point solutions focused on
specific scenarios. This thesis presents a complete solution (firmware and hardware)
for applications within this class that consists of attaching mobile sensor nodes to the
entities for tracking and monitoring their behaviour, and deploying an infrastructure
of base-stations for collecting the data wirelessly. The proposed solution is more energy
efficient compared to the existing solutions that target specific scenarios, offering
a longer deployment lifetime with a reduced size and weight of the devices. This is
achieved mainly by using the VB-TDMA low-power data upload protocol proposed in
this thesis. The mobile sensor nodes, consisting of the GPS and radio modules among
others, and the base-stations are powered by batteries, and the optimisation of their
energy usage is of primary concern. The presence of the GPS module, in particular
its acquisition of accurate time, is used by the VB-TDMA protocol to synchronise the
communication between nodes at no additional energy costs, resulting in an energy-efficient
data upload protocol for sparse networks of mobile nodes, that can potentially
be out of range of base-stations for extended periods of time. The VB-TDMA
and an asynchronous data upload protocol were implemented on the custom-designed
Prospeckz-5-based wireless sensor nodes. The protocols’ performances were simulated
in the SpeckSim simulator and validated in real-world deployments of tracking
and monitoring thirty-two Retuerta wild horses in the Doñana National Park in Spain,
and a herd of domesticated horses in Edinburgh. The chosen test scenario of long-term
wildlife tracking and monitoring is representative for the targeted class of applications.
The VB-TDMA protocol showed a significantly lower power consumption than other
comparable MAC protocols, effectively doubling the battery lifetime. The main contributions
of the thesis are the development of the VB-TDMA data upload protocol
and its performance evaluation, along with the development of simulation models for
performance analysis of wireless sensor networks, validated using data from the two
real-world deployments
Feasibility of wireless horse monitoring using a kinetic energy harvester model
To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 mu W during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current
Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets
Although it is not a novel topic, pattern recognition has
become very popular and relevant in the last years. Different classification
systems like neural networks, support vector machines or even
complex statistical methods have been used for this purpose. Several
works have used these systems to classify animal behavior, mainly in an
offline way. Their main problem is usually the data pre-processing step,
because the better input data are, the higher may be the accuracy of the
classification system. In previous papers by the authors an embedded
implementation of a neural network was deployed on a portable device
that was placed on animals. This approach allows the classification to
be done online and in real time. This is one of the aims of the research
project MINERVA, which is focused on monitoring wildlife in Do˜nana
National Park using low power devices. Many difficulties were faced when
pre-processing methods quality needed to be evaluated. In this work, a
novel pre-processing evaluation system based on self-organizing maps
(SOM) to measure the quality of the neural network training dataset is
presented. The paper is focused on a three different horse gaits classification
study. Preliminary results show that a better SOM output map
matches with the embedded ANN classification hit improvement.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-
IRIS: Efficient Visualization, Data Analysis and Experiment Management for Wireless Sensor Networks
The design of ubiquitous computing environments is challenging, mainly due to the unforeseeable impact of real-world environments on the system performance. A crucial step to validate the behavior of these systems is to perform in-field experiments under various conditions. We introduce IRIS, an experiment management and data processing tool allowing the definition of arbitrary complex data analysis applications. While focusing on Wireless Sensor Networks, IRIS supports the seamless integration of heterogeneous data gathering technologies. The resulting flexibility and extensibility enable the definition of various services, from experiment management and performance evaluation to user-specific applications and visualization. IRIS demonstrated its effectiveness in three real-life use cases, offering a valuable support for in-field experimentation and development of customized applications for interfacing the end user with the system
Forest Animal Detection and Alerting System
The Internet of Things (IoT) is a physical thing with an ecological connection that is reachable online. IoT is used in many different ways, including smart agriculture, smart healthcare, smart retail, smart homes, smart cities, energy commitment, poultry and farming, smart water management, and other contemporary purposes. In the agricultural industry, man-animal conflict poses a serious problem where a huge amount of resources are lost and human life is put in danger. Due to this, farmers lose their crops, livestock, property, and even their lives. Therefore, it is necessary to regularly monitor this area to prevent the introduction of wild animals. This initiative offered a framework to monitor the situation in this regard. This is done by locating the invader in the area of the field by using a sensor, a camerawill then identify the animal, and a text message will be delivered to the farmer through GSM
On a wildlife tracking and telemetry system : a wireless network approach
Includes abstract.Includes bibliographical references (p. 239-261).Motivated by the diversity of animals, a hybrid wildlife tracking system, EcoLocate, is proposed, with lightweight VHF-like tags and high performance GPS enabled tags, bound by a common wireless network design. Tags transfer information amongst one another in a multi-hop store-and-forward fashion, and can also monitor the presence of one another, enabling social behaviour studies to be conducted. Information can be gathered from any sensor variable of interest (such as temperature, water level, activity and so on) and forwarded through the network, thus leading to more effective game reserve monitoring. Six classes of tracking tags are presented, varying in weight and functionality, but derived from a common set of code, which facilitates modular tag design and deployment. The link between the tags means that tags can dynamically choose their class based on their remaining energy, prolonging lifetime in the network at the cost of a reduction in function. Lightweight, low functionality tags (that can be placed on small animals) use the capabilities of heavier, high functionality devices (placed on larger animals) to transfer their information. EcoLocate is a modular approach to animal tracking and sensing and it is shown how the same common technology can be used for diverse studies, from simple VHF-like activity research to full social and behavioural research using wireless networks to relay data to the end user. The network is not restricted to only tracking animals – environmental variables, people and vehicles can all be monitored, allowing for rich wildlife tracking studies
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