88,621 research outputs found
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
Combining edge and cloud computing for mobility analytics
Mobility analytics using data generated from the Internet of Mobile Things
(IoMT) is facing many challenges which range from the ingestion of data streams
coming from a vast number of fog nodes and IoMT devices to avoiding overflowing
the cloud with useless massive data streams that can trigger bottlenecks [1].
Managing data flow is becoming an important part of the IoMT because it will
dictate in which platform analytical tasks should run in the future. Data flows
are usually a sequence of out-of-order tuples with a high data input rate, and
mobility analytics requires a real-time flow of data in both directions, from
the edge to the cloud, and vice-versa. Before pulling the data streams to the
cloud, edge data stream processing is needed for detecting missing, broken, and
duplicated tuples in addition to recognize tuples whose arrival time is out of
order. Analytical tasks such as data filtering, data cleaning and low-level
data contextualization can be executed at the edge of a network. In contrast,
more complex analytical tasks such as graph processing can be deployed in the
cloud, and the results of ad-hoc queries and streaming graph analytics can be
pushed to the edge as needed by a user application. Graphs are efficient
representations used in mobility analytics because they unify knowledge about
connectivity, proximity and interaction among moving things. This poster
describes the preliminary results from our experimental prototype developed for
supporting transit systems, in which edge and cloud computing are combined to
process transit data streams forwarded from fog nodes into a cloud. The
motivation of this research is to understand how to perform meaningfulness
mobility analytics on transit feeds by combining cloud and fog computing
architectures in order to improve fleet management, mass transit and remote
asset monitoringComment: Edge Computing, Cloud Computing, Mobility Analytics, Internet of
Mobile Things, Edge Fog Fabri
Building Blocks for IoT Analytics Internet-of-Things Analytics
Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)
Challenges of Internet of Things and Big Data Integration
The Internet of Things anticipates the conjunction of physical gadgets to the
In-ternet and their access to wireless sensor data which makes it expedient to
restrain the physical world. Big Data convergence has put multifarious new
opportunities ahead of business ventures to get into a new market or enhance
their operations in the current market. considering the existing techniques and
technologies, it is probably safe to say that the best solution is to use big
data tools to provide an analytical solution to the Internet of Things. Based
on the current technology deployment and adoption trends, it is envisioned that
the Internet of Things is the technology of the future, while to-day's
real-world devices can provide real and valuable analytics, and people in the
real world use many IoT devices. Despite all the advertisements that companies
offer in connection with the Internet of Things, you as a liable consumer, have
the right to be suspicious about IoT advertise-ments. The primary question is:
What is the promise of the Internet of things con-cerning reality and what are
the prospects for the future.Comment: Proceedings of the International Conference on International
Conference on Emerging Technologies in Computing 2018 (iCETiC '18), 23rd
-24th August, 2018, at London Metropolitan University, London, UK, Published
by Springer-Verla
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
Advantages of analytic solutions of sentience cloud platform.
The Internet of Things (IoT), Industry 4.0, and advanced analytics have generated enormous hype for their potential to transform businesses. This paper looks at different types of analytics and presents a pragmatic approach taken by Honeywell Connected Plant in application of analytics to gain insights from data in process industry
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