A Panorama of Cloud Platforms for IoT Applications Across Industries

Yangui, Sami

doi:10.3390/s20092701

Cited by 14 publications

(9 citation statements)

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“…Yangui [22] surveys IoT services such as Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) to the users. They analyze several service solutions with different IoT applications and platforms as the criteria.…”

Section: Iot and Fog Applications And Architecturesmentioning

confidence: 99%

“…Gil et al [12] IoT applications smart city Naha et al [14] IoT applications and architectures Fog layer taxonomy Mukherjee et al [16] IoT applications ecosystem architecture Perera et al [13] IoT applications ecosystem protocols Al-Fuqaha et al [17] IoT communication network protocols Yassein et al [18] IoT communication MQTT protocol Maag et al [15] air pollution application IoT sensor layer Maheswari et al [19] IoT data process security Hathaliya and Tanwar [20] IoT healthcare application security Markus and Kertesz [21] IoT simulation software simulator Yangui [22] IoT application IoT services Varshney and Simmhan [23] IoT resource allocation scheduling…”

Section: Topic Key Aspectmentioning

confidence: 99%

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Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Chegini

Naha

Mahanti

et al. 2021

IoT

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The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.

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Section: Iot and Fog Applications And Architecturesmentioning

confidence: 99%

Section: Topic Key Aspectmentioning

confidence: 99%

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Chegini

Naha

Mahanti

et al. 2021

IoT

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“…IoT applications assist enterprises and organizations to realize the potential of the Internet of Things. Businesses are relying on IoT applications to optimize workflows and enhance the control of operations at each step of the supply chain [10]. In the particular case of surveillance systems, IoT turns security surveillance into smart safety and security management [11].…”

Section: A Iot For Surveillance Systemsmentioning

confidence: 99%

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

Yahyaoui

Abdellatif²,

Yangui³

et al. 2021

IEEE Access

Self Cite

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Internet of Things (IoT) enables a myriad of applications by interconnecting software to physical objects. The objects range from wireless sensors to robots and include surveillance cameras. The applications are often critical (e.g. physical intrusion detection, fire fighting) and latency-sensitive. On the one hand, such applications rely on specific protocols (e.g. MQTT, COAP) and the network to communicate with the objects under very tight timeframe. On the other hand, anomalies (e.g. communication noise, sensors' failures, security attacks) are likely to occur in open IoT systems and can result by sending false alerts or the failure to properly detect critical events. To address that, IoT systems have to be equipped with anomaly detection processing in addition to the required event detection capability. This is a key feature that enables reliability and efficiency in IoT. However, anomaly detection systems can be themselves object of failures and attacks, and then can easily fall short to accomplish their mission. This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short). The designed framework integrates events and anomalies detection into a single and common system that centralizes the management of both concepts. To enforce its reliability, the system relies on a reputationaware provisioning of detection capabilities that takes into account the vulnerability of the deployment hosts. As for validation, READ-IoT was implemented and evaluated using two real life applications, i.e. a fire detection and an unauthorized person detection applications. Several scenarios of anomalies and events were conducted using NSL-KDD public dataset, as well as, generated data to simulate routing attacks. The obtained results and performance measurements show the efficiency of READ-IoT in terms of event detection accuracy and real-time processing.

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“…The qualitative leap that accompanies the fourth industrial revolution results in humans and machines co-creating a production system [ 15 ]. In this system, various components can be distinguished: Incremental manufacturing (3D printing)—used to produce complete products [ 16 , 17 , 18 ], systems integration—the flow of data and control signals between machines, information systems, people and management systems [ 19 , 20 , 21 , 22 ], Industrial Internet of Things (IIoT)—the unified integration of statuary and management systems [ 23 , 24 ], Cloud computing—making external hardware and software resources available to process production data and perform complex calculations for the production process itself as well as for management systems [ 25 , 26 , 27 ], big data analysis (Big Data)—i.e., the use of large computing powers to collect, store and analyze large amounts of data [ 28 , 29 ], augmented and virtual reality (AR and VR) [ 30 , 31 , 32 ]—technologies that are proposing to move industry to virtual and augmented reality, Cyber security—which plays a huge role in cloud computing and analyzing large data sets on external servers [ 33 , 34 ]. …”

Section: Introductionmentioning

confidence: 99%

“…Industrial Internet of Things (IIoT)—the unified integration of statuary and management systems [ 23 , 24 ],…”

Section: Introductionmentioning

confidence: 99%

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

Kaczmarek

Lotys²,

Borys

et al. 2021

Sensors

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The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation.

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A Panorama of Cloud Platforms for IoT Applications Across Industries

Cited by 14 publications

References 50 publications

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

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