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نتیجه جستجو - data management and analytics

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 MagLoc : A magnetic induction based localization scheme for fresh food logistics
MagLoc: یک طرح محلی سازی مبتنی بر القای مغناطیسی برای تدارکات مواد غذایی تازه-2022
An IoT infrastructure to continuously monitor the fresh food supply chain can quickly detect food quality and contamination issues and thereby reduce costs and food wastage. This, in turn, involves several challenges including the development of inexpensive quality/contamination sensors to be deployed in a fine grain manner in the food boxes, technologies for sensor level communications, online data management and analytics, and logistics driven by such analytics. In this paper, we study the issues related to the communication among sensing modules deployed in the fresh food boxes and thereby an automated localization of the boxes that may have quality/contamination issues. In this context we study the near-field magnetic induction (NFMI) based communication and localization, as the ubiquitous RF communications suffer high attenuation through the water/mineral rich tissue media. An accurate localization of the sensors inside boxes within the food pallets is very challenging in this environment. In this paper we propose a novel magnetic induction based localization scheme, and show that with a small number of anchor nodes, the localization can be done without any errors for boxes as small as 0.5 meter on the side, and with small errors even for boxes half as big.
Keywords: Smart sensing | Industrial sensors | Food supply chain | Physical Internet | Magnetic communication | Localization
مقاله انگلیسی
2 A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective
یک سایه دیجیتال مبتنی بر دانش برای صنعت ماشینکاری در یک چشم انداز دیجیتال دوتایی-2021
This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision of Digital Shadow (DS) which would be considered as the core component of a future Digital Twin. Knowledge generated by experts and artificial intelligence, is transformed into formal business rules and integrated into the DS to enable the characterization of the real behavior of the physical system throughout its operation stage. This behavior model is continuously enriched by direct or derived learning, in order to improve the digital twin. The proposed DS relies on data analytics (based on unsupervised learning) and on a knowledge inference engine. It enables the incidents to be detected and it is also able to decipher its operational context. An example of this application in the aeronautic machining industry is provided to stress both the feasibility of the proposition and its potential impact on shop floor performance.
keywords: سایه دیجیتال | دوقلو | داده ها و مدیریت دانش | ماشینکاری | Digital shadow | Digital twin | Data and knowledge management | Machining
مقاله انگلیسی
3 Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
محاسبات متن آگاه، یادگیری و داده های بزرگ در اینترنت اشیا: یک مرور-2018
Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers
Index Terms: Big data in Internet of Things (IoT), context awareness, data management and analytics, machine learning in IoT
مقاله انگلیسی
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