SmartHerd management: A microservices‐based fog computing–assisted IoT platform towards data‐driven smart dairy farming

Taneja, Mohit; Jalodia, Nikita; Byabazaire, John; Davy, Alan; Olariu, Cristian

doi:10.1002/spe.2704

Cited by 84 publications

(40 citation statements)

References 47 publications

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“…In the results, the researchers have defined feed efficiency ontology as the feeding procedure which ultimately leads to better milk production. For increased milk productivity, SmartHerd management [21] for analyzing animal behavior and health monitoring has been prospered. In this research, micro service oriented design has been presented to assist distributed computing.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT for Development of Smart Dairy Farming

Akbar

Khan

Ali

et al. 2020

Journal of Food Quality

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Internet of things (IoT) and data-driven techniques are creating greater opportunities for smart dairy farming. The demand for milk is continuously increasing due to increasing population of the world. The consumption of the dairy products is more in developed countries as compared to developing countries. To meet this increased demand for milk products, better technological techniques for improving milk yield are required. It is expected that the use of IoT and different AI techniques can assist a farmer to overcome different traditional farming challenges and increase the milk production. In this research, the authors address different challenges that a dairy farmer has to face in daily life. Brief introduction of smart dairy farming (SDF) is presented with respect to the innovation in production and the processes of smart dairy farming. This review focuses on different aspects of smart dairy farming, and finally a state-of-the-art framework that can assist the farmers to increase the milk yield by using different latest technologies has been proposed. These technological methods can decrease the factors negatively affecting milk production and increase those positively affecting production with minimal resources.

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Section: Literature Reviewmentioning

confidence: 99%

IoT for Development of Smart Dairy Farming

Akbar

Khan

Ali

et al. 2020

Journal of Food Quality

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“…• Smart Farms: It is a system that is rolled out in the farms to help farmers to gain time and improve the quality of farm services. Author in [50] described a solution that is destinated to the agriculture domain and is based on Microservices. They deployed their solution on three-level: Fog, Edge, Cloud.…”

Section: Microservice-based Iot Platformsmentioning

confidence: 99%

A Comparative Study of Microservices-based IoT Platforms

Khalyly¹,

Belangour²,

Banane³

et al. 2020

IJACSA

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Internet of Things (IoT) is a set of technologies that aim at fitting together smart devices and applications to build an IoT ecosystem. The target of this kind of ecosystem is to enhance interaction between machines and humans through hardware to software binding while reducing cost and resource consumption. On the application level, IoT ecosystems were implemented by various technologies that all seek better interconnection, monitoring, and controlling of IoT smart devices. Among recent technologies, Microservices, which are a variant of the serviceoriented architecture, are subject to great excitement. In fact, Microservices are an emerging technology built around Microservice paradigm which goal is to offer services with a small granularity, which exactly meets the distributed nature of IoT devices while maintaining a loosely coupled architecture between IoT components among other advantages. Efforts to build Microservice-based IoT platforms sooner emerged to take advantage of the numerous benefits of the Microservice paradigm to build scalable, interoperable, and dynamic ecosystems. The goal of this paper is to list these approaches, classify them and compare them using a Weighted Scoring Model (WSM) method. This involves, studying these platforms, establishing relevant criteria for comparison, assigning weights for each criterion, and finally calculating scores. The obtained results reveal the weaknesses and strengths of each of the studied platforms.

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“…Such platforms may be based on software frameworks such as FIWARE [14,16], [22], proprietary or custom-made software frameworks following a multitude of software architectures [23] or microservices-based architecture [24], [25], [26], [27]. FIWARE makes interoperability between applications called enablers and has the context broker ORION in its core.…”

Section: Iot Platforms For Agricultural Servicesmentioning

confidence: 99%

IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

Maia¹,

Ballester²,

Baniya³

et al. 2020

Preprint

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Research has shown the multitude of applications that IoT, cloud computing and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop and atmosphere parameters, interact with third party cloud services, planning and scheduling irrigation as well as control of irrigation water control devices. The proposed IoT platform was evaluated during one growing season at four commercial scale farms on two different broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS in rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure and security. The platform addressed all these requirements. The results showed that the microservice approach followed in the platform is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused for any reason at one farm did not affect the performance of the platform regarding the other monitored farms. This paper also discusses how the microservice approach can address the data heterogeneity issue when crops with different management requirements are monitored. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation maintaining data confidentiality for each user, which is relevant in a commercial farm scenario.

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SmartHerd management: A microservices‐based fog computing–assisted IoT platform towards data‐driven smart dairy farming

Cited by 84 publications

References 47 publications

IoT for Development of Smart Dairy Farming

IoT for Development of Smart Dairy Farming

A Comparative Study of Microservices-based IoT Platforms

IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

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