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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 |