https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1007&context=aii_fac_pub

Machine Learning for Internet of Things Data Analysis: A Survey

Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

https://www.mdpi.com/1424-8220/20/4/1042/pdf

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a s...

https://journals.sagepub.com/doi/pdf/10.1177/0954405417736547

Smart manufacturing: Characteristics, technologies and enabling factors:

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.

Machine learning for Internet of Things data analysis: A survey

Modern Enabling Techniques and Adsorbents based Dye Removal with Sustainability Concerns in Textile Industrial Sector -A comprehensive review

Water is a vital resource for life, and its management is a key issue nowadays. Information and communications technology systems for water control are currently facing interoperability problems due to the lack of support of standardization in monitory and control equipment. This problem affects various processes in water management, such as water consumption, distribution, system identification and equipment maintenance. OPC UA (Object Linking and Embedding for Process Control Unified Architecture) is a platform independent service-oriented architecture for the control of processes in the logistic and manufacturing sectors. Based on this standard we propose a smart water management model combining Internet of Things technologies with business processes coordination and decision support systems. We provide an architecture for sub-system interaction and a detailed description of the physical scenario in which we will test our implementation, allowing specific vendor equipment to be manageable and interoperable in the specific context of water management processes.

/pdf/an-iot-based-reference-architecture-for-smart-water-4xkfg19zrs.pdf

An IoT based reference architecture for smart water management processes.

INSPIRE and Social Empowerment for Environmental Sustainability Results from the HABITATS project

Innovation strategy for rural development based on living labs for human empowerment

El objetivo de este estudio fue comparar, en un alojamiento de cebo de porcino con enrejillado parcial (Laboratorio de Bienestar Porcino), la produccion de residuos con un sistema tradicional de fosa de purines frente a un sistema de separacion in situ con cinta plana. La fosa se vacio cada cinco semanas, y la cinta se vacio dos veces al dia a lo largo del periodo de cebo. Ambos sistemas tuvieron el mismo tipo de ventilacion por extraccion bajo suelo. La cantidad de residuos generados con la separacion in situ fue el 70,50% de la correspondiente al sistema tradicional, que, a su vez, fue sensiblemente menor que la habida en las explotaciones comerciales. El sistema de ventilacion por extraccion bajo suelo fue muy eficiente para la evaporacion de agua de las deyecciones y la consecuente reduccion de la cantidad de residuos generados; asimismo, los resultados obtenidos ponen de manifiesto que la separacion in situ permitio una notable reduccion adicional de dicha cantidad, en comparacion con el sistema tradicional.

/pdf/fosa-de-purines-vs-separacion-in-situ-de-heces-y-orina-en-39o6k5onpc.pdf

Fosa de purines vs separación in situ de heces y orina en porcino de cebo: producción de purín y de estiércol

Water is a vital resource for life, and for the economy. Nowadays, one of the most serious challenges to solve is to manage the water scarcity. Current water management ICT systems are supported by specific vendor equipment, without considering any interoperability standards. The lack of standardization among producer's water ICT equipment hinders proper monitoring and control systems, resulting in low efficiency in water distribution and consumption, system's maintenance and improvement, and failure identification. In this paper we propose a smart water management model integrating Internet of Things technologies for decoupling decision support systems and monitoring from business processes coordination and subsystem implementation. The proposed smart water management model makes specific vendor equipment interoperable and manageable in a water management domain in a homogeneous way.

An Internet of Things-Based Model for Smart Water Management

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