Data management and internet of things : A methodological review in smart farming

Debauche, Olivier; Trani, Jean-Philippe; Mahmoudi, Saïd; Manneback, Pierre; Bindelle, Jérôme; Mahmoudi, Sidi Ahmed; Guttadauria, Adriano; Lebeau, Frédéric

doi:10.1016/j.iot.2021.100378

Cited by 36 publications

(29 citation statements)

References 44 publications

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“…Finally, we argue that is important to collect massively farms animals' behavior data of various species evolving in different environment to establish more robust models usable at a large scale [75]. Nowadays, experimentations are generally achieved on reduced datasets of animals which allows obtaining high accuracy because of the homogeneity of the training data.…”

Section: Discussionmentioning

confidence: 99%

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Debauche¹,

Elmoulat²,

Mahmoudi³

et al. 2021

RIA

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Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

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Section: Discussionmentioning

confidence: 99%

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Debauche¹,

Elmoulat²,

Mahmoudi³

et al. 2021

RIA

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“…On the right side, different approaches based on IoT solutions through digitalization of a "thing" [32] are shown. Each of these solutions can generate a huge amount of data, called big data [33,34], which is collected in real-time and processed by optimization AI-based algorithms for data driven decisions and other technologies [16,17], aiming at monitoring, measuring, tracking or controlling, allowing to improve production results [10,11,13,15,18,19,24,25,[35][36][37][38][39][40][41][42][43][44]. Block chain is an emerging technology and is con-sidered a solution for the cybersecurity of IoT devices [22].…”

Section: Iot In Agriculturementioning

confidence: 99%

Development of Technological Capabilities through the Internet of Things (IoT): Survey of Opportunities and Barriers for IoT Implementation in Portugal’s Agro-Industry

et al. 2021

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The agro-industrial sector consumes a significant amount of natural resources for farming and meat production. By 2050, population growth is expected, generating more demand and, consequently, more consumption of scarce resources. This challenging scenario is a concern of the European Commission, revealed in the Green Deal commitment and by the United Nations’ 12th goal of sustainable development. Thus, organizations must increase productivity and be more sustainable as soon as possible. Internet of Things (IoT) is introduced as a solution to facilitate agro-food companies to be more eco-efficient, mainly facing difficulties on farms, such as food loss and waste, best efficiency in management of resources, and production. The deployment of this technology depends on the stage of maturity and potential of implementation. To assess and characterize companies, with respect of IoT implementation, a survey was applied in 21 micro, small and medium agro-food companies, belonging to milk, honey, olive oil, jams, fruticulture, bakery and pastry, meat, coffee, and wine sectors, in the central region of Portugal. As results, this paper reveals the stage of maturity, level of sophistication, potential, opportunities, solutions, and barriers for implementation of IoT. Additionally, suggestions and recommendations to improve practices are discussed.

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“…These types of data are characterized by large volumes and/or continuous flows of data. The processing of these types of data is generally carried out in the cloud out of convenience [1]. However, with the concomitant development of the Internet of Things (IoT), virtual reality, augmented reality, big data, and social networks have led to network congestion and an increase in processing times and the limits of the Central Cloud Computing (CCC) paradigm.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the increase in processing capacities at the level of routers and network equipment (Fog Computing) made it possible to offer new processing capacities closer to users. At the same time, the memory and processor capacities of the sensors have also evolved and it has been possible to offer processing capacities (Edge Computing) as close as possible to the sensors to verify, clean, encrypt, compress the data and lightly preprocess data in order to limit the amount of data that is sent to the cloud [1]. Processing and storage capacities and latency decrease the closer you get to the end user.…”

Section: Introductionmentioning

confidence: 99%

“…Osmotic Computing (OC) uses micro virtual machines (MVM) that can be moved between the local level and the cloud depending on available resources. This paradigm is inspired by osmotic pressure to distribute tasks between Edge and Cloud level [1,2]. However, for the moment, security aspects in terms of privacy or data confidentiality are lowly or not managed.…”

Section: Introductionmentioning

confidence: 99%

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A New Edge Computing Architecture for IoT and Multimedia Data Management

2022

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The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT).

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Data management and internet of things : A methodological review in smart farming

Cited by 36 publications

References 44 publications

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Development of Technological Capabilities through the Internet of Things (IoT): Survey of Opportunities and Barriers for IoT Implementation in Portugal’s Agro-Industry

A New Edge Computing Architecture for IoT and Multimedia Data Management

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