7,921 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
A new wireless underground network system for continuous monitoring of soil water contents
A new stand-alone wireless embedded network system has been developed recently for continuous monitoring of soil water contents at multiple depths. This paper presents information on the technical aspects of the system, including the applied sensor technology, the wireless communication protocols, the gateway station for data collection, and data transfer to an end user Web page for disseminating results to targeted audiences. Results from the first test of the network system are presented and discussed, including lessons learned so far and actions to be undertaken in the near future to improve and enhance the operability of this innovative measurement approac
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Monitoring wild animal communities with arrays of motion sensitive camera traps
Studying animal movement and distribution is of critical importance to
addressing environmental challenges including invasive species, infectious
diseases, climate and land-use change. Motion sensitive camera traps offer a
visual sensor to record the presence of a broad range of species providing
location -specific information on movement and behavior. Modern digital camera
traps that record video present new analytical opportunities, but also new data
management challenges. This paper describes our experience with a terrestrial
animal monitoring system at Barro Colorado Island, Panama. Our camera network
captured the spatio-temporal dynamics of terrestrial bird and mammal activity
at the site - data relevant to immediate science questions, and long-term
conservation issues. We believe that the experience gained and lessons learned
during our year long deployment and testing of the camera traps as well as the
developed solutions are applicable to broader sensor network applications and
are valuable for the advancement of the sensor network research. We suggest
that the continued development of these hardware, software, and analytical
tools, in concert, offer an exciting sensor-network solution to monitoring of
animal populations which could realistically scale over larger areas and time
spans
Pheromone-based In-Network Processing for wireless sensor network monitoring systems
Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.Fil: Riva, Guillermo Gaston. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas, FĂsicas y Naturales; Argentina. Universidad TecnolĂłgica Nacional; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba; ArgentinaFil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba. Instituto de Estudios Avanzados en IngenierĂa y TecnologĂa. Universidad Nacional de CĂłrdoba. Facultad de Ciencias Exactas FĂsicas y Naturales. Instituto de Estudios Avanzados en IngenierĂa y TecnologĂa; Argentin
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Living IoT: A Flying Wireless Platform on Live Insects
Sensor networks with devices capable of moving could enable applications
ranging from precision irrigation to environmental sensing. Using mechanical
drones to move sensors, however, severely limits operation time since flight
time is limited by the energy density of current battery technology. We explore
an alternative, biology-based solution: integrate sensing, computing and
communication functionalities onto live flying insects to create a mobile IoT
platform.
Such an approach takes advantage of these tiny, highly efficient biological
insects which are ubiquitous in many outdoor ecosystems, to essentially provide
mobility for free. Doing so however requires addressing key technical
challenges of power, size, weight and self-localization in order for the
insects to perform location-dependent sensing operations as they carry our IoT
payload through the environment. We develop and deploy our platform on
bumblebees which includes backscatter communication, low-power
self-localization hardware, sensors, and a power source. We show that our
platform is capable of sensing, backscattering data at 1 kbps when the insects
are back at the hive, and localizing itself up to distances of 80 m from the
access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang,
In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201
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