A study on storage mechanism for heterogeneous sensor data on big data paradigm

Dinakar, J. Ruby; Vagdevi, S

doi:10.1109/iceeccot.2017.8284525

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The Big Data Grapes EU project 5 provides data semantic, analytics, integration and exploitation solutions as well as a software stack for grapes agriculture. As additional ongoing research issues, we identified tools and technology development [21] [22], analytic methods and services [23] [24], clustering methods [25], transformation algorithms [26] [22] performance and data processing technics [27], data source and storage management [28] [29], etc.…”

Section: -Big Data Technologiesmentioning

confidence: 99%

Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

Lezoche

Hernández

Ruiz

et al. 2020

Computers in Industry

529

339

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income.pdf 4.0 should help meeting sustainable challenges by increasing the agri-food supply chain stakeholders revenues as well as decreasing their pressure for handling complex and external factors they cannot control, such as weather, market behaviours and policies, but also to react on time by visualising current trends in needs. This paper focus principally on the factory farm model issued from the European and north American style. In those typologies of farms the Industry 4.0 elements can be introduced and merged with the agricultural domain to create the Agri-Food 4.0. The focus is more specialised on the industrial farming although the Agri-Food 4.0 is started to be adapted also to the organic and silvo-pasture paradigms. Thus, this paper, considering a review over than two hundred papers, provides a contribution to knowledge by establishing the linkages between new 4.0 trends in technologies and Agri-Food Supply Chain Challenges, which opens the 4.0 research field for a multidisciplinary work. The choice of the literature review methodology focused firstly on key words related to the Agri-food technology, then the second approach was to focus on selected journals and at the end we looked for the data-based repository and the linked publications. To accomplish this, the structure in this paper is as follows: in the first place, a compressive state of the art of Agri-Food 4.0 related technologies is covered. In the second place, a review and contribution coming from digital technologies to the new supply chain methods in Agri-Food shows linkages with next trends and technologies. Next, and linked to the agri-food supply chain challenges, the fourth section presents new trends and models in agri-food supply chains, especially in the domain of risk management, collaboration, governance, cold chain management, globalization, information and communication technologies, Logistics, supply chain structures and sustainable agri-food supply chains. Finally, the fifth section covers the main conclusions, visions and perspectives for this novel research. 2-Agri-food 4.0 and Technologies a State of the Art The agricultural sector has been active in digital innovation for decades already. Especially the advances in Precision Agriculture, remote sensing, robots, farm management information

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Section: -Big Data Technologiesmentioning

confidence: 99%

Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

Lezoche

Hernández

Ruiz

et al. 2020

Computers in Industry

529

339

View full text Add to dashboard Cite

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“…Sensors constitute a fundamental component of data extraction in the food industry, enabling the implementation of Big Data concepts by facilitating efficient management of large volumes of data (Dinakar & Vagdevi, 2017). The information obtained through sensors is integrated into all stages of production, processing, distribution, transport and marketing.…”

Section: Introductionmentioning

confidence: 99%

Filtering Strategies for Smartphone Emitted Digital Signals

OBRETIN,

CORNEA

2024

Appl. Comput. Sci.

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In today's digitalized and technology-driven society, where the number of IoT devices and the volume of collected data is exponentially increasing, the use of sensor data has become a necessity in certain fields of activity. This paper presents a concise history of sensor evolution and specialization, with a focus on the sensors used for localization, particularly those present in microelectromechanical systems (MEMS) that make up inertial measurement units. The study starts with a general overview and progresses towards a more specific analysis of data sets collected from an accelerometer. In the materials and methods section, it emphasizes the imperfect nature of sensor measurements and the need to filter digital signals. Three different digital signal filtering techniques belonging to distinct filter categories are comparatively analyzed, with each technique having its own particularities, advantages and disadvantages. The analysis considers the effectiveness in reducing measurement errors, the impact of the filtering process on the original signal, the ability to highlight the underlying phenomenon, as well as the performance of the analyzed algorithms. The ultimate purpose of this article is to determine which filtration method is best suited for the signal at hand in the context of an indoor localization application.

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“…In recent years, the growth of the IoT (Internet of Things) required to face the challenges of "Big Data" [6][7][8][9][10]. The cost of sensors is decreasing, while their use is expanding.…”

Section: Introductionmentioning

confidence: 99%