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

Public Key Encryption With Equality Test for Industrial Internet of Things Based on Near-Ring

2021 ◽  
Vol 17 (3) ◽  
pp. 25-45
Author(s):  
Muthukumaran V. ◽  
Manimozhi I. ◽  
Praveen Sundar P. V. ◽  
Karthikeyan T. ◽  
Magesh Gopu
Keyword(s):  
Internet Of Things ◽  
Quantum Algorithm ◽  
Random Oracle Model ◽  
Public Key ◽  
Smart Devices ◽  
Type I ◽  
Public Key Encryption ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Equality Test

Organizations have moved from the conventional industries to smart industries by embracing the approach of industrial internet of things (IIoT), which has provided an avenue for the integration of smart devices and communication technologies. In this context, this work presents a public key encryption with equality test based on DLP with decomposition problems over near-ring. The proposed method is highly secure, and it solves the problem of quantum algorithm attacks in industrial internet of thing systems. Further, the proposed system is highly secure, and it prevents the chosen-ciphertext attack in type-I adversary and it is indistinguishable against the random oracle model for the type-II adversary. The proposed scheme is highly secure, and the security analysis measures are comparatively stronger than existing techniques.

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.

scholarly journals An Efficient Pairing-Free Certificateless Searchable Public Key Encryption for Cloud-Based IIoT

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mimi Ma ◽  
Min Luo ◽  
Shuqin Fan ◽  
Dengguo Feng
Keyword(s):  
Internet Of Things ◽  
Data Privacy ◽  
Security And Privacy ◽  
Public Key ◽  
Public Key Encryption ◽  
Privacy Concerns ◽  
Industrial Internet ◽  
Encryption Schemes ◽  
Searchable Public Key Encryption ◽  
Industrial Resource

The Industrial Internet of Things (IIoT), as a special form of Internet of Things (IoT), has great potential in realizing intelligent transformation and industrial resource utilization. However, there are security and privacy concerns about industrial data, which is shared on an open channel via sensor devices. To address these issues, many searchable encryption schemes have been presented to provide both data privacy-protection and data searchability. However, due to the use of expensive pairing operations, most previous schemes were inefficient. Recently, a certificateless searchable public-key encryption (CLSPE) scheme was designed by Lu et al. to remove the pairing operation. Unfortunately, we find that Lu et al.’s scheme is vulnerable to user impersonation attacks. To enhance the security, a new pairing-free dual-server CLSPE (DS-CLSPE) scheme for cloud-based IIoT deployment is designed in this paper. In addition, we provide security and efficiency analysis for DS-CLSPE. The analysis results show that DS-CLSPE can resist chosen keyword attacks (CKA) and has better efficiency than other related schemes.

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.

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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