6,978 research outputs found
A framework for smart production-logistics systems based on CPS and industrial IoT
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems
Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform
In this paper, we provide details of implementing a system for managing a
fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse
premise. While the robots are themselves autonomous in its motion and obstacle
avoidance capability, the target destination for each robot is provided by a
global planner. The global planner and the ground vehicles (robots) constitute
a multi agent system (MAS) which communicate with each other over a wireless
network. Three different approaches are explored for implementation. The first
two approaches make use of the distributed computing based Networked Robotics
architecture and communication framework of Robot Operating System (ROS) itself
while the third approach uses Rapyuta Cloud Robotics framework for this
implementation. The comparative performance of these approaches are analyzed
through simulation as well as real world experiment with actual robots. These
analyses provide an in-depth understanding of the inner working of the Cloud
Robotics Platform in contrast to the usual ROS framework. The insight gained
through this exercise will be valuable for students as well as practicing
engineers interested in implementing similar systems else where. In the
process, we also identify few critical limitations of the current Rapyuta
platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Використання хмарних обчислень для розвитку інформаційно-комунікаційної компетентності вчителів
The article deals with the problem for development of techers’ information and communication competence and use of cloud computing for it. The analysis of the modern approaches to the use of cloud technologies and projects for professional development of teachers and development of teachers’ information and communication competence have been presented.There are the main characteristics of software as a service on the Internet for education leading companies Google, Microsoft, IBM. There are described some actions of these companies, which are conducted to help teachers to master cloud technology for improving the professional activities and development of teachers’ information and communication competence. The examples of ways of development of teachers’ information and communication competence and training teachers to use modern ICT in the professional activity are given in the paper. The Cloud based model for development of teachers’ information and communication competence has been proposed.Стаття присвячена проблемі розвитку інформаційно-комунікаційної компетентності вчителів за допомогою використання хмарних обчислень. Здійснюється аналіз сучасних підходів до використання хмарних технологій та проектів для професійного розвитку вчителів та розвитку інформаційно-комунікаційної компетентності вчителів. Пропонуються основні характеристики хмарних обчислень провідних компаній Google, Microsoft, IBM, з точки зори їх необхідності для здійснення навчального процесу в мережі Інтернет. Описуються дії цих компаній та інші навчальні проекти, метою яких є розвиток інформаційно-комунікаційної компетентності вчителів за допомогою хмарних обчислень. Наводяться приклади шляхів рішення проблеми розвитку інформаційно-комунікаційної компетентності вчителів за допомогою використання сучасних інформаційно-комунікаційних технологій. Запропонована модель розвитку інформаційно-комунікаційної компетентності вчителів на базі хмарних обчислень, виділені основні вимоги та елементи цієї моделі
Adaptive Big Data Pipeline
Over the past three decades, data has exponentially evolved from being a simple software by-product to one of the most important companies’ assets used to understand their customers and foresee trends. Deep learning has demonstrated that big volumes of clean data generally provide more flexibility and accuracy when modeling a phenomenon. However, handling ever-increasing data volumes entail new challenges: the lack of expertise to select the appropriate big data tools for the processing pipelines, as well as the speed at which engineers can take such pipelines into production reliably, leveraging the cloud. We introduce a system called Adaptive Big Data Pipelines: a platform to automate data pipelines creation. It provides an interface to capture the data sources, transformations, destinations and execution schedule. The system builds up the cloud infrastructure, schedules and fine-tunes the transformations, and creates the data lineage graph. This system has been tested on data sets of 50 gigabytes, processing them in just a few minutes without user intervention.ITESO, A. C
A Hybrid Approach for Data Analytics for Internet of Things
The vision of the Internet of Things is to allow currently unconnected
physical objects to be connected to the internet. There will be an extremely
large number of internet connected devices that will be much more than the
number of human being in the world all producing data. These data will be
collected and delivered to the cloud for processing, especially with a view of
finding meaningful information to then take action. However, ideally the data
needs to be analysed locally to increase privacy, give quick responses to
people and to reduce use of network and storage resources. To tackle these
problems, distributed data analytics can be proposed to collect and analyse the
data either in the edge or fog devices. In this paper, we explore a hybrid
approach which means that both innetwork level and cloud level processing
should work together to build effective IoT data analytics in order to overcome
their respective weaknesses and use their specific strengths. Specifically, we
collected raw data locally and extracted features by applying data fusion
techniques on the data on resource constrained devices to reduce the data and
then send the extracted features to the cloud for processing. We evaluated the
accuracy and data consumption over network and thus show that it is feasible to
increase privacy and maintain accuracy while reducing data communication
demands.Comment: Accepted to be published in the Proceedings of the 7th ACM
International Conference on the Internet of Things (IoT 2017
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