scholarly journals IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Mohammed Mohammed Sadeeq ◽  
Nasiba M. Abdulkareem ◽  
Subhi R. M. Zeebaree ◽  
Dindar Mikaeel Ahmed ◽  
Ahmed Saifullah Sami ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Self Organization ◽  
Industrial Internet Of Things ◽  
Efficient Storage ◽  
Outsourced Data ◽  
Industrial Internet ◽  
Challenges And Opportunities ◽  
Integration Problems ◽  
And Storage

With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

scholarly journals Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

Assessing the implementation feasibility of intelligent production systems based on cloud computing, industrial internet of things and business social networks

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiabao Sun ◽  
Ting Yang ◽  
Zhiying Xu
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Resource Sharing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Industrial Internet Of Things ◽  
Content Type ◽  
Industrial Internet ◽  
The Impact

PurposeThe increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.Design/methodology/approachIntelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R2, T-Value and GOF.FindingsThe study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.Research limitations/implicationsStudy limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.Originality/valueThe research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

Reconceptualizing value innovation for Industry 4.0 and the Industrial Internet of Things

2019 ◽  
Vol 34 (6) ◽  
pp. 1203-1209 ◽  
Author(s):  
Paul Matthyssens
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Internet Of Things ◽  
Value Innovation ◽  
Content Type ◽  
Innovation Research ◽  
Industrial Internet ◽  
Challenges And Opportunities ◽  
New Research

Purpose Starting from the foundations of value innovation, this paper aims to give an idea of the key drivers and barriers – internal and external to the company – and to provide insight into proven capabilities underscoring the ability to create a flow of new value initiatives. These thoughts are then confronted with the present challenges of Industry 4.0 and the Industrial Internet of Things (IIoT). The confrontation leads to the identification of five capabilities for future-proof value innovation. Design/methodology/approach Literature review based upon the work of the author with more than two decades of experience within value innovation research is included. The review is supplemented with recent literature and an overview of the challenges of Industry 4.0/IIoT, which leads into a confrontation of the present status of value innovation with future requirements. Findings Value innovation remains important specifically for established companies facing path-breaking digital disruption of their existing business models provoked by Industry 4.0 and IIoT. Five key capabilities are suggested to rejuvenate value innovation and prepare it for the Industry 4.0 challenge: capabilities for designing, adapting and marketing product service systems; capabilities for blending digital strategy and processes with value offerings; capabilities for designing and mobilizing ecosystems and integrating these into a value-based IIoT platform; capabilities for combining and integrating technological and value innovation approaches; and capabilities for linking value creation to value capturing. Research limitations/implications This paper is more of a “viewpoint” than an empirically based paper presenting new research findings. It is based on expert judgment and confrontation with extant literature. The outlook indicating five key capabilities needs further empirical corroboration. Practical implications The overview of barriers and the “toolkit” for value innovation (Figure 1) and the five capabilities for future value innovation are expected to be managerially relevant. Originality/value The paper highlights the concept of value innovation, as discussed over the past decades, and links it to recent challenges and opportunities imposed by Industry 4.0 and the IIoT. The concept of value or strategic innovation is still valid but needs a re-conceptualization in view of these developments. The paper provides five capabilities business marketers should develop to perform value innovation in an Industry 4.0 environment.

Public key encryption with equality test for Industrial Internet of Things system in cloud computing

2021 ◽  
Author(s):  
Ganesh Gopal Deverajan ◽  
V. Muthukumaran ◽  
Ching‐Hsien Hsu ◽  
Marimuthu Karuppiah ◽  
Yeh‐Ching Chung ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Public Key ◽  
Public Key Encryption ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Equality Test

scholarly journals Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2509 ◽  
Author(s):  
Juan Wang ◽  
Di Li
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
Fog Computing ◽  
Software Defined Network ◽  
Industrial Internet Of Things ◽  
Time Performance ◽  
Industrial Internet ◽  
Task Priority ◽  
Sdn Controller

In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system. A method based on Computing Mode Selection (CMS) and execution sequences based on the task priority (ASTP) is proposed in this paper. First, a CMS module is designed in the SDN controller and then, after operating the CMS algorithm, each task obtains an optimal computing mode. Second, the task priorities can be calculated according to their real-time performance and calculated amount. According to the task priority, the SDN controller sends a flow table to the SDN switch to complete the task transmission. In other words, the higher the task priority is, the earlier the fog computing service is obtained. Finally, a series of experiments and simulations are performed to evaluate the performance of the proposed method. The results show that our method can achieve real-time performance and high reliability in IIoT.

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