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

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Cooperative strategies for the detection and localization of odorants with robots and artificial noses

En este trabajo de investigación se aborda el diseño de una plataforma robótica orientada a la implementación de estrategias de búsqueda cooperativa bioinspiradas. En particular, tanto el proceso de diseño de la parte electrónica como hardware se han enfocado hacia la validación en entornos reales de algoritmos capaces de afrontar problemas de búsqueda con incertidumbre, como lo es la búsqueda de fuentes de olor que presentan variación espacial y temporal. Este tipo de problemas pueden ser resueltos de forma más eficiente con el empleo de enjambres con una cantidad razonable de robots, y por tanto la plataforma ha sido desarrollada utilizando componentes de bajo coste. Esto ha sido posible por la combinación de elementos estandarizados -como la placa controladora Arduino y otros sensores integrados- con piezas que pueden ser fabricadas mediante una impresora 3D atendiendo a la filosofía del hardware libre (open-source). Entre los requisitos de diseño se encuentran además la eficiencia energética -para maximizar el tiempo de funcionamiento de los robots-, su capacidad de posicionamiento en el entorno de búsqueda, y la integración multisensorial -con la inclusión de una nariz electrónica, sensores de luminosidad, distancia, humedad y temperatura, así como una brújula digital-. También se aborda el uso de una estrategia de comunicación adecuada basada en ZigBee. El sistema desarrollado, denominado GNBot, se ha validado tanto en los aspectos de eficiencia energética como en sus capacidades combinadas de posicionamiento espacial y de detección de fuentes de olor basadas en disoluciones de etanol. La plataforma presentada -formada por el GNBot, su placa electrónica GNBoard y la capa de abstracción software realizada en Python- simplificará por tanto el proceso de implementación y evaluación de diversas estrategias de detección, búsqueda y monitorización de odorantes, con la estandarización de enjambres de robots provistos de narices artificiales y otros sensores multimodales.This research work addresses the design of a robotic platform oriented towards the implementation of bio-inspired cooperative search strategies. In particular, the design processes of both the electronics and hardware have been focused towards the real-world validation of algorithms that are capable of tackling search problems that have uncertainty, such as the search of odor sources that have spatio-temporal variability. These kind of problems can be solved more efficiently with the use of swarms formed by a considerable amount of robots, and thus the proposed platform makes use of low cost components. This has been possible with the combination of standardized elements -as the Arduino controller board and other integrated sensors- with custom parts that can be manufactured with a 3D printer attending to the open-source hardware philosophy. Among the design requirements is the energy efficiency -in order to maximize the working range of the robots-, their positioning capability within the search environment, and multiple sensor integration -with the incorporation of an artificial nose, luminosity, distance, humidity and temperature sensors, as well as an electronic compass-. Another subject that is tackled is the use of an efficient wireless communication strategy based on ZigBee. The developed system, named GNBot, has also been validated in the aspects of energy efficiency and for its combined capabilities for autonomous spatial positioning and detection of ethanol-based odor sources. The presented platform -formed by the GNBot, the GNBoard electronics and the abstraction layer built in Python- will thus simplify the processes of implementation and evaluation of various strategies for the detection, search and monitoring of odorants with conveniently standardized robot swarms provided with artificial noses and other multimodal sensors

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Secure services integration and edge computing for effective beekeeping

Many of the issues that require resolution are not easy to mitigate just from the technology perspective. The ancestral learned logic of processes, the people traditions, and many other variants define inner contexts that make the adhesion and efficient use of information technologies a delicate process. The enormous geographical dispersion of the beekeeping economic activity, the mostly amateur profile of beekeepers, and the specificity in the traditional way as the activity is managed, compromises the applicability of integrative measures based on ICTE. Efficient and integrated management of a no-professionalized economic activity depends on two basic principles: i) the existence of effective tools capable of managing that activity and its synergies with other related activities, and ii) an infrastructure (technological, procedural, legal) that supports services properly profiled for any actor in that activity. This paper describes the work-in-process sBee - Smart Beekeeping, an applied research project that sought to integrate emerging technologies on the innovative management of critical issues that beekeeping needs to overcome. Electronic devices, Internet-of-things, advanced management algorithms, and innovative visualization services were explored. The global system architecture, its supporting services, and the communication infrastructure are here described. The integration of both internet-of-things and communications services, with the common beekeeping?s management tasks, levered a proposal for improving this activity to become more effective. Furthermore, an advanced technological supporting platform was created and experimented, prepared for further developments, on mitigating emergent challenges that the digitization promotes, namely the security and traceability on food and related agriculture value-chains, as well as on the predictive and intelligent perception of current and future scenarios.911A-2C18-106F | Carlos Jorge Enes Capit?o de AbreuN/