Convergence of Manufacturing Cloud and Industrial IoT

2019 ◽  
pp. 49-78
Author(s):  
Manoj Himmatrao Devare
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Business Environment ◽  
On Demand ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Key Technologies ◽  
Manufacturing Resources

The manufacturing cloud (CMfg) covers the use of three key technologies including cloud computing, the industrial internet of things (IIoT), and collaborative engineering for achieving the productivity and quality challenges in the big manufacturing, which is enabled due to the communication, mobile, and broadcasting network. It is necessary to establish a flexible and adaptive infrastructure for manufacturing industry to share and use various manufacturing resources and services on-demand under the dynamic, complicated, and large-scale business environment. The CMfg makes the industry more agile, responsive, and reconfigurable for exposure to the industry as a global manufacturing enterprise. The chapter considers the CMfg facets and IIoT, use cases in the manufacturing industry, and explains IIoT and CMfg as a complementary technology.

Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry
Keyword(s):  
Internet Of Things ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
The Internet ◽  
Resource Assignment ◽  
Industrial Internet Of Things ◽  
Assignment Method ◽  
Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

Cloud in Science

2014 ◽  
pp. 165-191
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Computing ◽  
Scientific Discovery ◽  
Software Environment ◽  
Access To Resources ◽  
On Demand ◽  
Service Models ◽  
New Models ◽  
The Impact

Cloud computing technologies and service models are attractive to scientific computing users due to the ability to get on-demand access to resources as well as the ability to control the software environment. Scientific computing researchers and resource providers servicing these users are considering the impact of new models and technologies. SaaS solutions like Globus Online and IaaS solutions such as Nimbus Infrastructure and OpenNebula accelerate the discovery of science by helping scientists to conduct advanced and large-scale science. This chapter describes how cloud is helping researchers to accelerate scientific discovery by transforming manual and difficult tasks into the cloud.

Examining IoT's Applications Using Cloud Services

2018 ◽  
pp. 147-163 ◽  
Author(s):  
Saravanan K ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Distributed Computing ◽  
Knowledge Sharing ◽  
Effective Communication ◽  
Cloud Services ◽  
On Demand ◽  
Computing Paradigm ◽  
Iot Devices ◽  
And Storage

Cloud IoT has evolved from the convergence of Cloud computing with Internet of Things (IoT). The networked devices in the IoT world grow exponentially in the distributed computing paradigm and thus require the power of the Cloud to access and share computing and storage for these devices. Cloud offers scalable on-demand services to the IoT devices for effective communication and knowledge sharing. It alleviates the computational load of IoT, which makes the devices smarter. This chapter explores the different IoT services offered by the Cloud as well as application domains that are benefited by the Cloud IoT. The challenges on offloading the IoT computation into the Cloud are also discussed.

Internet of Things Service Provisioning Platform for Cross-Application Cooperation

2016 ◽  
Vol 13 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Shuai Zhao ◽  
Bo Cheng ◽  
Le Yu ◽  
Shou-lu Hou ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Physical Space ◽  
Service Provisioning ◽  
Problem Domain ◽  
Tight Coupling ◽  
User Requirement ◽  
Loosely Coupled ◽  
Heterogeneous Devices ◽  
On Demand

With the development of Internet of Things (IoT), large-scale of resources and applications atop them emerge. However, most of existing efforts are “silo” solutions, there is a tight-coupling between the device and the application. The paradigm for IoT and its corresponding infrastructure are required to move away from isolated solutions towards cooperative models. Recent works have focused on applying Service Oriented Architecture (SOA) to IoT service provisioning. Other than the traditional services of cyberspace which are oriented to a two-tuple problem domain, IoT services are faced with a three-tuple problem domain of user requirement, cyberspace and physical space. One challenge of existing works is lacking of efficient mechanism to on-demand provisioning the sensing information in a loosely-coupled, decentralized way and then dynamically coordinate the relevant services to rapidly respond to changes in the physical world. Another challenge is how to systematically and effectively access (plug) the heterogeneous devices without intrusive changing. This paper proposes a service provisioning platform which enables to access heterogeneous devices and expose device capabilities as light-weighted service, and presents an event-based message interaction mode to facilitate the asynchronous, on-demand sharing of sensing information in distributed, loosely-coupled IoT environment. It provides the basic infrastructure for IoT application pattern: inner-domain high-degree autonomy and inter-domain dynamic coordination. The practicability of platform is validated by experimental evaluations and a District Heating Control and Information System (DHCIS).

scholarly journals Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

2019 ◽  
Vol 9 (20) ◽  
pp. 4323 ◽  
Author(s):  
López de Lacalle ◽  
Posada
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Research Community ◽  
Manufacturing Processes ◽  
Special Issue ◽  
Industrial Internet Of Things ◽  
Visual Computing ◽  
Industrial Internet ◽  
Industrial Iot ◽  
New Industry

The new advances of IIOT (Industrial Internet of Things), together with the progress in visual computing technologies, are being addressed by the research community with interesting approaches and results in the Industry 4.0 domain[...]

scholarly journals A Polling-Based Transmission Scheme Using a Network Traffic Uniformity Metric for Industrial IoT Applications

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 187 ◽  
Author(s):  
Yuichi Igarashi ◽  
Ryo Nakano ◽  
Naoki Wakamiya
Keyword(s):  
Data Collection ◽  
Network Traffic ◽  
Success Probability ◽  
Transmission Scheme ◽  
On Demand ◽  
Success Ratio ◽  
Periodic Data ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Wireless Network Protocols

The Industrial Internet of Things (IIoT) applications are required to provide precise measurement functions as feedback for controlling devices. The applications traditionally use polling-based communication protocols. However, in polling-based communication over current industrial wireless network protocols such as ISA100.11a, WirelessHART have difficulty in realizing both scheduled periodic data collection at high success ratio and unpredictable on-demand communications with short latency. In this paper, a polling-based transmission scheme using a network traffic uniformity metric is proposed for IIoT applications. In the proposed scheme, a center node controls the transmission timing of all polling-based communication in accordance with a schedule that is determined by a Genetic Algorithm. Communication of both periodic and unpredictable on-demand data collection are uniformly assigned to solve the above difficulties in the schedule. Simulation results show that network traffic is generated uniformly and a center node can collect periodic data from nodes at high success ratio. The average success probability of periodical data collection is 97.4 % and the lowest probability is 95.2 % .

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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