51,501 research outputs found
Modular and generic IoT management on the cloud
Cloud computing and Internet of Things encompass various physical devices that generate and exchange data with services promoting the integration between the physical world and computer-based systems. This work presents a novel Future Internet cloud service for data collection from Internet of Things devices in an automatic, generalized and modular way. It includes a flexible API for managing devices, users and permissions by mapping data to users, publish and subscribe context data as well as storage capabilities and data processing in the form of NoSQL big data. The contributions of this work include the on the fly data collection from devices that is stored in cloud scalable databases, the vendor agnostic Internet of Things device connectivity (since it is designed to be flexible and to support device heterogeneity), and finally the modularity of the event based publish/subscribe service for context oriented data that could be easily utilized by third party services without worrying about how data are collected, stored and managed
Security and privacy analysis based on Internet of Things in the fourth industrial generation (Industry 4.0)
The connection of smart devices using the Internet has dramatically changed the way people live, and this concept has also been extended to the industrial sector. This practice not only provides more stable, faster, and safer communications but also makes it possible to realize the concept of the smart factory in the fourth industrial revolution. The Internet of Things uses a unique Internet Protocol to identify, control, and transmit data to individuals as well as databases. Data is collected through the Internet of Things, stored in cloud storage, and managed and calculated through analytical tools. Internet of Things security is a field of technology that focuses on protecting connected devices and networks in the Internet of Things (IoT). Ensuring the safety of networks with connected IoT devices is critical. Security in the Internet of Things includes a wide range of techniques, strategies, protocols, and measures aimed at mitigating the ever-increasing vulnerabilities of the Internet of Things in modern businesses. The simultaneous connection of objects also brings privacy concerns. For this reason, in this research, an effort has been made to examine and analyze the most important privacy requirements in the Internet of Things in digital businesses in Industry 4.0. In this regard, by using experts' opinions and literature review, privacy requirements were extracted and evaluated using fuzzy non-linear decision-making methodology. The results showed that acquired and intrinsic information has the highest importance
Low-cost method to measure and remotely monitor water tank level
The rising area of Internet of Things (IoT) intends to unify sensors data all over the world through the web. In this context, new technologies emerge to bring integration between society and those data. This paper proposes a low-cost method to measure water tank level and send it to the Internet for remote monitoring. A search was made on patents and papers index databases to verify similar technologies. Using a cheap microcontroller and wires as switches, the water level was measured, this data was uploaded on the cloud through MQTT protocol, in JSON format, and even a relay for a water pump was actuated. Further, some other ways to improve this work and how it differs from existent technologies were discussed
Internet of Things Cloud: Architecture and Implementation
The Internet of Things (IoT), which enables common objects to be intelligent
and interactive, is considered the next evolution of the Internet. Its
pervasiveness and abilities to collect and analyze data which can be converted
into information have motivated a plethora of IoT applications. For the
successful deployment and management of these applications, cloud computing
techniques are indispensable since they provide high computational capabilities
as well as large storage capacity. This paper aims at providing insights about
the architecture, implementation and performance of the IoT cloud. Several
potential application scenarios of IoT cloud are studied, and an architecture
is discussed regarding the functionality of each component. Moreover, the
implementation details of the IoT cloud are presented along with the services
that it offers. The main contributions of this paper lie in the combination of
the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport
(MQTT) servers to offer IoT services in the architecture of the IoT cloud with
various techniques to guarantee high performance. Finally, experimental results
are given in order to demonstrate the service capabilities of the IoT cloud
under certain conditions.Comment: 19pages, 4figures, IEEE Communications Magazin
Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection
With the current rapid advancement of cloud computing (CC) technology, which enabled the connectivity of many intelligent objects and detectors and created smooth data interchange between systems, there is now a strict need for platforms for data processing, the Internet of Things (IoT), and data management. The field of medicine in CC is receiving a lot of attention from the scientific world, as well as the private and governmental sectors. Thousands of individuals now have a digital system due to these apps where they may regularly obtain helpful medical advice for leading a healthy life. The use of artificial intelligence (AI) in the medical field has several advantages, including the ability to automate processes and analyze large patient databases to offer superior medicine more quickly and effectively. IoT-enabled smart health tools provide both internet solutions and a variety of features. CC infrastructure improves these healthcare solutions by enabling safe storage and accessibility. We suggest a novel Cloud computing and artificial intelligence (CC-AI) premised smart medical solution for surveillance and detecting major illnesses to provide superior solutions to the users. For disease detection, we suggested AI-based whale optimization (WO) and fuzzy neural network (FNN) (WO-FNN). Patients' IoT wearable sensor data is gathered for detection. The accuracy, sensitivity, specificity, and computation time are evaluated and compared with existing techniques
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
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
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