FIWARE Open Source Standard Platform in Smart Farming - A Review

[EN] FIWARE is an open source platform for the deployment of Internet of Things (IoT) applications, driven by European Union and managed by FIWARE Foundation. Recently, FIWARE Foundation has launched his new product Agricolus, which focus on Smart Farming and it uses FIWARE infrastructure. Agricolus manages to bring Hardware and Software together in a decision-making process that support farming activities and offers a "plug and play" interface for precision agriculture. This is encompassed by the phenomenon of Smart Farming, which is a development that take advantage of the use of Information Communication Technologies (ICT) in the daily farm management. This review aims to gain insight into the state-of-the-art of FIWARE in Smart Farming and identify the components of Agricolus in comparison with essential FIWARE architecture.This research has been carried out in the framework of the project "Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector." Ref. GV/2017/025 funded by the Generalitat Valenciana.Rodríguez-Sánchez, MDLÁ.; Cuenca, L.; Ortiz Bas, Á. (2018). FIWARE Open Source Standard Platform in Smart Farming - A Review. IFIP Advances in Information and Communication Technology. 534:581-589. https://doi.org/10.1007/978-3-319-99127-6_50S581589534Robert, P.C.: Precision agriculture: research needs and status in the USA. In: Stafford, J.V. (ed.) Proceedings of the 2nd European Conference on Precision Agriculture, Part 1, pp. 19–33. Academic Press, SCI/Sheffield (1999)Ge, Y., Thomasson, J.A., Sui, R.: Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. 5(3), 229–238 (2011)Sundmaeker, H., Verdouw, C., Wolfert, S., Pérez Freire L.: Internet of food and farm 2020. In: Vermesan, O., Friess, P. (eds.) Digitising the Industry - Internet of Things Connecting Physical, Digital and Virtual Worlds, pp. 129–151. River Publishers, Gistrup/Delft (2016)Lin, J., Liu, C.: Monitoring system based on wireless sensor network and a SocC platform in precision agriculture. In: Proceedings of the International Conference on Communication Technology (ICCT), Hangzhou, pp. 101–104 (2008)Kaewmard, N., Saiyod, S.: Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm. In: Proceedings of the International Conference on Wireless Sensors (ICWiSE), pp. 106–112 (2014)FIWARE. https://www.fiware.org/Future Internet Private Public Partnership (FI-PPP). https://www.fi-ppp.eu/Agricolus. https://www.agricolus.comFIWARE Generic Enablers. http://edu.fiware.org/FIWARE Catalogue. https://catalogue.fiware.org/enablersKamilaris, A., Gao, F., Prenafeta-Boldu, F.X., Ali, M.I.: Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In: IEEE 3rd World Forum on Internet of Things, WF-IoT 2016, pp. 442–447 (2017)López-Riquelme, J.A., Pavón-Pulido, N., Navarro-Hellín, H., Soto-Valles, F., Torres-Sánchez, R.: A software architecture based on FIWARE cloud for precision agriculture. Agric. Water Manag. 183, 123–135 (2017)Martínez, R., Pastor, J.Á., Álvarez, B., Iborra, A.: A testbed to evaluate the FIWARE-based IoT platform in the domain of precision agriculture. Sensors (Switzerland), 16(11) (2016)Pesonen, L.A., et al.: Cropinfra - an internet-based service infrastructure to support crop production in future farms. Biosys. Eng. 120, 92–101 (2014)Barmpounakis, S., et al.: Management and control applications in agriculture domain via a future internet business-to-business platform. Inf. Process. Agric. 2(1), 51–63 (2015)Kaloxylos, A., et al.: Farm management systems and the future internet era. Comput. Electron. Agric. 89, 130–144 (2012)Kaloxylos, A., et al.: A cloud-based farm management system: architecture and implementation. Comput. Electron. Agric. 100, 168–179 (2014)Ryu, M., Yun, J., Miao, T., Ahn, I.Y., Choi, S.C., Kim, J.: Design and implementation of a connected farm for smart farming system. In: 2015 IEEE SENSORS Proceedings, pp. 1–4 (2015)Layton, A.W., Balmos, A.D., Sabpisal, S., Ault, A., Krogmeier, J.V., Buckmaster, D.: ISOBlue: an open source project to bring agricultural machinery data into the cloud, Montreal, 13 July–16 July 2014. American Society of Agricultural and Biological Engineers (2014)SmartAgriFood. http://smartagrifood.com/FIWARE MarketPlace. https://marketplace.fiware.orgFIWARE iHubs. https://www.fiware.org/community/fiware-ihubs/Agricolus in FIWARE MarketPlace. https://marketplace.fiware.org/pages/solutions/2ec3c741ef4dd8f83bab4e83Implementation example of Agricolus. http://www.libelium.com/increasing-tobacco-crops-quality-by-climatic-conditions-control/FIspace. https://www.fispace.eu/whatisfispace.htm

Towards interoperability of entity-based and event-based IoT platforms: The case of NGSI and EPCIS standards

With the advancement of IoT devices and thanks to the unprecedented visibility and transparency they provide, diverse IoT-based applications are being developed. With the proliferation of IoT, both the amount and type of data items captured have increased dramatically. The data generated by IoT devices reside in different organizations and systems, and a major barrier to utilizing the data is the lack of interoperability among the standards used to capture the data. To reduce this barrier, two major standards have emerged: the Global Standards One (GS1) Electronic Product Code Information Service (EPCIS) and the FIWARE Next Generation Services Interface (NGSI). However, the two standards differ not only in the data encoding but also in the underlying philosophy of representing IoT data; namely, EPCIS is event-based, and NGSI is entity-based. Interoperability between FIWARE and EPCIS is essential for system integration. This paper presents OLIOT Mediation Gateway, now one of the incubated generic enablers offered by the FIWARE Foundation, that realizes the required interoperability between NGSI and EPCIS systems. It also demonstrates the applicability and feasibility of the Gateway by applying it to a real-life case study of integrating transparency systems used in a meat supply chain

Development of a Low-Cost Open-Source Platform for Smart Irrigation Systems

Nowadays, smart irrigation is becoming an essential goal in agriculture, where water and energy are increasingly limited resources. Its importance will grow in the coming years in the agricultural sector where the optimal use of resources and environmental sustainability are becoming more important every day. However, implementing smart irrigation is not an easy task for most farmers since it is based on knowledge of the different processes and factors that determine the crop water requirements. Thanks to technological developments, it is possible to design new tools such as sensors or platforms that can be connected to soil-water-plant-atmosphere models to assist in the optimization and automation of irrigation. In this work, a low-cost, open-source IoT system for smart irrigation has been developed that can be easily integrated with other platforms and supports a large number of sensors. The platform uses the FIWARE framework together with customized components and can be deployed using edge computing and/or cloud computing systems. To improve decision-making, the platform integrates an irrigation model that calculates soil water balance and wet bulb dimensions to determine the best irrigation strategy for drip irrigation systems. In addition, an energy efficient open-source datalogger has been designed. The datalogger supports a wide range of communications and is compatible with analog sensors, SDI-12 and RS-485 protocols. The IoT system has been deployed on an olive farm and has been in operation for one irrigation season. Based on the results obtained, advantages of using these technologies over traditional methods are discussed

An Internet of Things Platform for Air Station Remote Sensing and Smart Monitoring

Air pollution is currently receiving more attention by international governments and organizations. Nevertheless, current systems for air quality monitoring lack essential requirements which are key in order to be effective concerning users’ access to the information and efficient regarding real-time monitoring and notification. This paper presents an Internet of Things platform for air station remote sensing and smart monitoring that combines Big Data and Cloud Computing paradigms to process and correlate air pollutant concentrations coming from multiple remote stations, as well as to trigger automatic and personalized alerts when a health risk for their particular context is detected. This platform has been tested by analyzing the results of observing Andalusian, South of Spain, sensor network during a long period of time. The results show that this novel solution can help to reduce the impact of air pollution on human health since citizens are alerted in real time

Exploiting the Internet Resources for Autonomous Robots in Agriculture

Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.info:eu-repo/semantics/publishedVersio

Architecting and deploying IoT smart applications: A performance–oriented approach

open7siLayered internet of things (IoT) architectures have been proposed over the last years as they facilitate understanding the roles of different networking, hardware, and software components of smart applications. These are inherently distributed, spanning from devices installed in the field up to a cloud datacenter and further to a user smartphone, passing by intermediary stages at different levels of fog computing infrastructure. However, IoT architectures provide almost no hints on where components should be deployed. IoT Software Platforms derived from the layered architectures are expected to adapt to scenarios with different characteristics, requirements, and constraints from stakeholders and applications. In such a complex environment, a one-size-fits-all approach does not adapt well to varying demands and may hinder the adoption of IoT Smart Applications. In this paper, we propose a 5-layer IoT Architecture and a 5-stage IoT Computing Continuum, as well as provide insights on the mapping of software components of the former into physical locations of the latter. Also, we conduct a performance analysis study with six configurations where components are deployed into different stages. Our results show that different deployment configurations of layered components into staged locations generate bottlenecks that affect system performance and scalability. Based on that, policies for static deployment and dynamic migration of layered components into staged locations can be identified.openZyrianoff I.; Heideker A.; Silva D.; Kleinschmidt J.; Soininen J.-P.; Cinotti T.S.; Kamienski C.Zyrianoff I.; Heideker A.; Silva D.; Kleinschmidt J.; Soininen J.-P.; Cinotti T.S.; Kamienski C

Thinger.io: an open source platform for deploying data fusion applications in IoT environments

In The Last Two Decades, Data And Information Fusion Has Experienced Significantdevelopment Due Mainly To Advances In Sensor Technology. The Sensors Provide A Continuousflow Of Data About The Environment In Which They Are Deployed, Which Is Received And Processed Tobuild A Dynamic Estimation Of The Situation. With Current Technology, It Is Relatively Simple To Deploya Set Of Sensors In A Specific Geographic Area, In Order To Have Highly Sensorized Spaces. However, Tobe Able To Fusion And Process The Information Coming From The Data Sources Of A Highly Sensorizedspace, It Is Necessary To Solve Certain Problems Inherent To This Type Of Technology. The Challengeis Analogous To What We Can Find In The Field Of The Internet Of Things (Iot). Iot Technology Ischaracterized By Providing The Infrastructure Capacity To Capture, Store, And Process A Huge Amountof Heterogeneous Sensor Data (In Most Cases, From Different Manufacturers), In The Same Way That Itoccurs In Data Fusion Applications. This Work Is Not Simple, Mainly Due To The Fact That There Is Nostandardization Of The Technologies Involved (Especially Within The Communication Protocols Usedby The Connectable Sensors). The Solutions That We Can Find Today Are Proprietary Solutions Thatimply An Important Dependence And A High Cost. The Aim Of This Paper Is To Present A New Opensource Platform With Capabilities For The Collection, Management And Analysis Of A Huge Amount Ofheterogeneous Sensor Data. In Addition, This Platform Allows The Use Of Hardware-Agnostic In A Highlyscalable And Cost-Effective Manner. This Platform Is Called Thinger.Io. One Of The Main Characteristicsof Thinger.Io Is The Ability To Model Sensorized Environments Through A High Level Language Thatallows A Simple And Easy Implementation Of Data Fusion Applications, As We Will Show In This Paper.This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), references TEC2017-88048-C2-2-R, TEC2014-57022-C2-2-RRTC-2016-5595-2, RTC-2016-5191-8 and RTC-2016-5059-8

IoT data processing pipeline in FoF perspective

With the development in the contemporary industry, the concepts of ICT and IoT are gaining more importance, as they are the foundation for the systems of the future. Most of the current solutions converge into transforming the traditional industry in new smart interconnected factories, aware of its context, adaptable to different environments and capable of fully using its resources. However, the full potential for ICT manufacturing has not been achieved, since there is not a universal or standard architecture or model that can be applied to all the existing systems, to tackle the heterogeneity of the existing devices. In a common factory, exists a large amount of information that needs to be processed into the system in order to define event rules accordingly to the related contextual knowledge, to later execute the needed actions. However, this information is sometimes heterogeneous, meaning that it cannot be accessed or understood by the components of the system. This dissertation analyses the existing theories and models that may lead to seamless and homogeneous data exchange and contextual interpretation. A framework based on these theories is proposed in this dissertation, that aims to explore the situational context formalization in order to adequately provide appropriate actions

Proposal of architecture for IoT solution for monitoring and management of plantations

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production This work presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor