Controlled sensing: A myopic fisher information sensor selection algorithm

Zois, Daphney–Stavroula; Mitra, Urbashi

doi:10.1109/glocom.2014.7037333

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Along with the proposed systems and solutions, there has also been research in integrating technologies to increase productivity and sustainable growth in agriculture, ranging from improving wireless network connectivity in economically underprivileged areas [15][16][17] to data mining and analytics for agricultural applications [18][19][20] to providing decision control in variable environments under constraints. [21][22][23] A review by Wolfert et al 24 shows that predictive insights in farming operations drive real-time operational decisions, and redesigns business processes for the benefit of various stakeholders in a farming landscape, and that the influence of such systems goes beyond primary production, to the entire supply chain. However, the survey by Rutten et al 25 identifies a serious lack of analytics and intelligence in these systems, thus leading to gaps between the desired requirement of the system and proposed solutions.…”

Section: Literature Review Background and Motivationmentioning

confidence: 99%

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

et al. 2019

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Summary Internet of Things (IoT), fog computing, cloud computing, and data‐driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing–assisted end‐to‐end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices‐oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6‐month mature real‐world deployment, wherein the data from wearables on cows is sent to a fog‐based platform for data classification and analysis, which includes decision‐making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog‐based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud‐based approach.

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