Research based on real time monitoring system of digitized agricultural water supply

Liu, Yunchen; Zhang, Yunpeng; Chen, Yu; Tong, Jun

doi:10.1109/iccse.2011.6028659

Cited by 3 publications

(1 citation statement)

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“…IoT-based solutions are investigated in order to help drive decisions in many agricultural areas, mainly water management and agriculture monitoring. These solutions can collect data from the farm's environment using networking protocols and standards, such as RFID, 4G/5G, LoWPAN, IEEE 802.15.x, and the future 6G [45][46][47][48][49][50][51][52].…”

Section: Data Generated By Machine (Dgm)mentioning

confidence: 99%

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

2022

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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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