Toward Cloud-Assisted Industrial IoT Platform for Large-Scale Continuous Condition Monitoring

2019 ◽  
Vol 107 (6) ◽  
pp. 1193-1205 ◽  
Author(s):  
Gang Wang ◽  
Mark Nixon ◽  
Mike Boudreaux
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Iot Platform ◽  
Industrial Iot

On the use of jerk and snap in condition monitoring of machinery – review and case studies

2021 ◽  
Vol 63 (8) ◽  
pp. 457-464
Author(s):  
S Lahdelma
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Early Stage ◽  
Time Derivative ◽  
Additional Insight ◽  
Rolling Bearings ◽  
Monitoring Applications ◽  
Derivatives Of ◽  
Insight Into ◽  
Peak Value

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cut-off frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

scholarly journals An Open Source Framework Approach to Support Condition Monitoring and Maintenance

2020 ◽  
Vol 10 (18) ◽  
pp. 6360
Author(s):  
Jaime Campos ◽  
Pankaj Sharma ◽  
Michele Albano ◽  
Luis Lino Ferreira ◽  
Martin Larrañaga
Keyword(s):  
Condition Monitoring ◽  
Open System ◽  
Health Monitoring ◽  
Large Scale ◽  
Condition Based Maintenance ◽  
Monitoring Methods ◽  
Industry Standards ◽  
Framework Approach ◽  
Push Forward ◽  
Predictive Health

This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

scholarly journals A lightweight and secure online/offline cross-domain authentication scheme for VANET systems in Industrial IoT

2021 ◽  
Vol 7 ◽  
pp. e714
Author(s):  
Haqi Khalid ◽  
Shaiful Jahari Hashim ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Fazirulhisyam Hashim ◽  
Muhammad Akmal Chaudhary
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Heterogeneous Wireless Networks ◽  
Communication Cost ◽  
Authentication Scheme ◽  
Cross Domain ◽  
Service Interruption ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Lightweight Authentication

In heterogeneous wireless networks, the industrial Internet of Things (IIoT) is an essential contributor to increasing productivity and effectiveness. However, in various domains, such as industrial wireless scenarios, small cell domains, and vehicular ad hoc networks, an efficient and stable authentication algorithm is required (VANET). Specifically, IoT vehicles deal with vast amounts of data transmitted between VANET entities in different domains in such a large-scale environment. Also, crossing from one territory to another may have the connectivity services down for a while, leading to service interruption because it is pervasive in remote areas and places with multipath obstructions. Hence, it is vulnerable to specific attacks (e.g., replay attacks, modification attacks, man-in-the-middle attacks, and insider attacks), making the system inefficient. Also, high processing data increases the computation and communication cost, leading to an increased workload in the system. Thus, to solve the above issues, we propose an online/offline lightweight authentication scheme for the VANET cross-domain system in IIoT to improve the security and efficiency of the VANET. The proposed scheme utilizes an efficient AES-RSA algorithm to achieve integrity and confidentiality of the message. The offline joining is added to avoid remote network intrusions and the risk of network service interruptions. The proposed work includes two different significant goals to achieve first, then secure message on which the data is transmitted and efficiency in a cryptographic manner. The Burrows Abdi Needham (BAN logic) logic is used to prove that this scheme is mutually authenticated. The system’s security has been tested using the well-known AVISPA tool to evaluate and verify its security formally. The results show that the proposed scheme outperforms the ID-CPPA, AAAS, and HCDA schemes by 53%, 55%, and 47% respectively in terms of computation cost, and 65%, 83%, and 40% respectively in terms of communication cost.

scholarly journals Quality control challenges post covid-19 crisis: an integrated IoT and IoP approach

2022 ◽  
Vol 2161 (1) ◽  
pp. 012001
Author(s):  
Harshit Sharma ◽  
G Sumathi
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Damage Assessment ◽  
Large Scale ◽  
Smart Manufacturing ◽  
Small Scale ◽  
Industrial Iot ◽  
Customer Trust ◽  
Near Future

Abstract The Covid -19 is arguably the biggest pandemic in history and there are a lot of challenges that must be dealt with. One of the biggest challenges post Covid-19 is to tackle quality control challenges. This research paper discusses some of these challenges and solutions using an integrated internet of things (IoT) and internet of protocols (IoP) based approach and further showing its implementation in the industry world and hence, proving to be a solution for damage assessment. With the help of IoT- enabled quality control system, six-sigma rule is also analysed. Post Covid crisis, it is important for every institution to gain back customer trust so quality of materials should be maintained and IoT enables us to do the same. The unification of industrial IoT (IIoT) and industry 4.0 is also discussed as it leads us to understand that this unification is the next evolution of smart manufacturing and digital technologies. This methodology can lead us to accelerated innovation in applications for overcoming the eventual challenges post Covid in the near future. Also, small-scale/large-scale companies making use of the above research methodology can adhere to six-sigma criterion.

scholarly journals Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

2021 ◽  
Author(s):  
Ibrahim Elgendy ◽  
Ammar Muthanna ◽  
Mohammad Hammoudeh ◽  
Hadil Ahmed Shaiba ◽  
Devrim Unal ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Large Scale ◽  
Optimal Solution ◽  
Security Requirements ◽  
Optimization Approach ◽  
Shared Resources ◽  
Data Offloading ◽  
Daily Lives ◽  
Industrial Iot ◽  
Iot Devices

The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and securityaware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curseof-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices.

scholarly journals An Efficient SDN Multicast Architecture for Dynamic Industrial IoT Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hyeong-Su Kim ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Heonyeop Shin ◽  
Won-Tae Kim
Keyword(s):  
Large Scale ◽  
Packet Delay ◽  
Smart Factory ◽  
Point Selection ◽  
Virtual Factory ◽  
Industrial Iot ◽  
Rendezvous Point ◽  
Point To Point ◽  
Switching Method ◽  
Shared Tree

Large-scale industrial IoT services appear in smart factory domains such as factory clouds which integrate distributed small factories into a large virtual factory with dynamic combination based on orders of consumers. A smart factory has so many industrial elements including various sensors/actuators, gateways, controllers, application servers, and IoT clouds. Since there are complex connections and relations, it is hard to handle them in point-to-point manner. In addition, many duplicated traffics are exchanged between them through the Internet. Multicast is believed as an effective many-to-many communication mechanism by establishing multicast trees between sources and receivers. There are, however, some issues for adopting multicast to large-scale industrial IoT services in terms of QoS. In this paper, we propose a novel software-defined network multicast based on group shared tree which includes near-receiver rendezvous point selection algorithm and group shared tree switching mechanism. As a result, the proposed multicast mechanism can reduce the packet loss by 90% compared to the legacy methods under severe congestion condition. GST switching method obtains to decreased packet delay effect, respectively, 2%, 20% better than the previously studied multicast and the legacy SDN multicast.

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