IoT in Smart Farming Analytics, Big Data Based Architecture

Ouafiq, El Mehdi; Elrharras, Abdessamad; Mehdary, A.; Chehri, Abdellah; Saadane, Rachid; Wahbi, Mohamed

doi:10.1007/978-981-15-5784-2_22

Cited by 25 publications

(10 citation statements)

References 14 publications

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“…During the past 60 years, terabytes of unstructured data in the database have been produced from the processes involved in gathering the various agricultural data [17]. Conversely, large-scale data cleansing and preprocessing have been tedious due to unstructured data types and a single database instance [18].…”

Section: Agricultural Information Managementmentioning

confidence: 99%

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Pal¹,

VijayKumar²,

Akila³

et al. 2023

Computers, Materials &Amp; Continua

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Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

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Section: Agricultural Information Managementmentioning

confidence: 99%

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Pal¹,

VijayKumar²,

Akila³

et al. 2023

Computers, Materials &Amp; Continua

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“…These analyses are related to (1) spatial distribution, (2) water management, and (3) mechanical system maintenance. It also allows agronomists and bioinformatics scientists to store and effectively handle their data, based on a data-migration strategy on top of a refined data architecture dedicated to intelligent farming analytics, the SFOBA [6].…”

Section: Managing Admissionmentioning

confidence: 99%

“…In addition, it will determine the ideal time for harvest based on the weather, agricultural market prices, and farm characteristics, thus optimizing the selling prices. Furthermore, with agriculture 4.0, a variety of sensors can be found in the barn, e.g., for animal identification, animal location, heat detection, or barn climate [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

2022

Self Cite

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Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights.

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“…Smart farming is another component of smart environments, which was enabled by IoT, Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, as well as Big data analytics [44]. Big data analytic plays an important role in bringing real-time decision-making capabilities into smart farming environments by obtaining valuable information from the collected data [45]. For example, machine learning can automate decision-making in smart farming environments by predicting soil drought and crop productivity [46].…”

Section: Smart Environmentsmentioning

confidence: 99%

Blockchain as privacy and security solution for smart environments: A Survey

Ebrahim¹,

Hafid²,

Elie³

2022

Preprint

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Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, the benefits of Blockchain are far beyond that. It has been recently used to support and augment many other technologies, including the Internet-of-Things (IoT). IoT, with the help of Blockchain, paves the way for futuristic smart environments, like smart homes, smart transportation, smart energy trading, smart industries, smart supply chains, and more. To enable these smart environments, IoT devices, machines, appliances, and vehicles, will need to intercommunicate without the need for a centralized trusted party. Blockchain can replace third trusted parties by providing secure means of decentralization in such trustless environments. They also provide security enforcement, privacy assurance, authentication, and other important features to IoT ecosystems. Besides the benefits of Blockchain-IoT integration for smart environments, other technologies also have important features and benefits that attracted the research community. Software-Defined Networking (SDN), Fog, Edge, and Cloud Computing technologies, for example, play an important role in enabling realistic IoT applications. Moreover, the integration of Machine Learning and Artificial Intelligence (AI) algorithms provides smart, dynamic, and autonomous decisionmaking capabilities for IoT devices in smart environments. To push the research further in this domain, we provide in this paper a comprehensive survey that includes state-of-the-art technological integration, challenges, and solutions for smart environments, and the role of Blockchain and IoT technologies as the building blocks of such smart environments. We also demonstrate how the level of integration between these technologies has increased over the years, which brings us closer to the futuristic view of smart environments. We further discuss the current need to provide general-purpose Blockchain platforms that can adapt to different design requirements of different applications and solutions. Finally, we provide a simplified architecture of futuristic smart environments that integrates all these technologies, showing the advantage of such integration.

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IoT in Smart Farming Analytics, Big Data Based Architecture

Cited by 25 publications

References 14 publications

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

Blockchain as privacy and security solution for smart environments: A Survey

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