scholarly journals Industrial Internet of Things for Mobile Phone Shell Intelligent Detection in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tianyi Zheng ◽  
Bao Peng ◽  
Guofu Zhou
Keyword(s):  
Internet Of Things ◽  
Mobile Phone ◽  
Defect Detection ◽  
Smart Cities ◽  
Detection Methods ◽  
Industrial Internet Of Things ◽  
Data Set ◽  
Open Operation ◽  
Industrial Internet ◽  
Intelligent Detection

Industrial Internet of Things is the core field of smart city. And intelligent detection is an important application field of industrial Internet of Things. Demand of the industrial is particularly urgent. In particular, the defect detection of mobile phone shells (MPS) has always been a common problem for famous mobile phone companies. A compression-free defect detection method (CFDDM) for MPS based on machine vision is proposed in this paper. Firstly, affine transformation is utilized to solve the angle deviation of MPS in different images. Then, edge detection, binarization, and open operation are combined to highlight the edge region based on the results of angle adjustment. It is convenient for region of interest (ROI) extraction and clipping. Finally, the method of gray histogram contrasting is utilized for defect detection according to the results of ROI clipping. And the detection results are obtained. In this paper, MPS data set is utilized for many tests. The results show that the proposed method can effectively detect whether there are defects in MPS data set without image compression. The recognition accuracy is 100%. The recognition time of a single image is about 4.56 s, which is better than other defect detection methods.

A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110599
Author(s):  
Zhong Li ◽  
Huimin Zhuang
Keyword(s):  
Internet Of Things ◽  
Supervised Learning ◽  
Software Defined Networking ◽  
Training Data ◽  
Detection Methods ◽  
Industrial Internet Of Things ◽  
Data Set ◽  
Correct Judgment ◽  
Industrial Internet ◽  
Address Resolution Protocol

Nowadays, in the industrial Internet of things, address resolution protocol attacks are still rampant. Recently, the idea of applying the software-defined networking paradigm to industrial Internet of things is proposed by many scholars since this paradigm has the advantages of flexible deployment of intelligent algorithms and global coordination capabilities. These advantages prompt us to propose a multi-factor integration-based semi-supervised learning address resolution protocol detection method deployed in software-defined networking, called MIS, to specially solve the problems of limited labeled training data and incomplete features extraction in the traditional address resolution protocol detection methods. In MIS method, we design a multi-factor integration-based feature extraction method and propose a semi-supervised learning framework with differential priority sampling. MIS considers the address resolution protocol attack features from different aspects to help the model make correct judgment. Meanwhile, the differential priority sampling enables the base learner in self-training to learn efficiently from the unlabeled samples with differences. We conduct experiments based on a real data set collected from a deepwater port and a simulated data set. The experiments show that MIS can achieve good performance in detecting address resolution protocol attacks with F1-measure, accuracy, and area under the curve of 97.28%, 99.41%, and 98.36% on average. Meanwhile, compared with fully supervised learning and other popular address resolution protocol detection methods, MIS also shows the best performance.

Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

2020 ◽  
Vol 55 ◽  
pp. 102615 ◽  
Author(s):  
Hasan Ali Khattak ◽  
Komal Tehreem ◽  
Ahmad Almogren ◽  
Zoobia Ameer ◽  
Ikram Ud Din ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Management ◽  
Dynamic Pricing ◽  
Smart Cities ◽  
Industrial Internet Of Things ◽  
Industrial Internet

How Can Industrial Internet of Things (IIoT) Improve Enterprise Productivity?

2020 ◽  
pp. 112-133
Author(s):  
Erdinç Koç
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Detailed Knowledge ◽  
Healthcare Industry ◽  
Industrial Internet Of Things ◽  
Time Consumption ◽  
Industrial Internet ◽  
Smart Cars ◽  
Industrial Part

This chapter gives brief information about internet of things (IoT) and then detailed knowledge of industrial internet of things (IIoT). Internet of things applications can be seen in different areas, such as smart cars, smart homes, smart cities, agriculture, healthcare, industry, etc. This study focuses on the industrial part. Industrial internet of things (IIoT) means internet of things (IoT) applications for industrial usage. IIoT give a chance to enterprise for tracking supply chains, monitoring production line operations, and real-time consumption of energy, managing stock, and transportation decisions. This study used case study method for developing theory about IIoT's contribution to enterprise productivity. IIoT applications can be adapted to which operations of the enterprise, and how it will contribute to enterprise productivity is explained in this chapter. The chapter discusses the projects that are within the vision of IIoT but not yet implemented and concludes with suggestions for future studies.

scholarly journals Enabling the Industrial Internet of Things to Cloud Continuum in a Real City Environment

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7707
Author(s):  
Fábio Henrique Cabrini ◽  
Filippo Valiante Valiante Filho ◽  
Pedro Rito ◽  
Albérico Barros Barros Filho ◽  
Susana Sargento ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Industrial Internet Of Things ◽  
Global Industry ◽  
Operating Modes ◽  
City Environment ◽  
Iot Platform ◽  
Industrial Internet ◽  
Living Lab ◽  
And Performance

The Industrial Internet of Things (IIoT) is one of the most demanding IoT applications. The insertion of industries in the context of smart cities and other smart environments, allied with new communication technologies such as 5G, brings a new horizon of possibilities and new requirements. These requirements include low latency, the support of a massive quantity of devices and data, and the need to support horizontal communications between devices at the edge level. To make this feasible, it is necessary to establish an IIoT-to-cloud continuum distributing federated brokers across the infrastructure and providing scalability and interoperability. To attend this type of application, we present the Helix Multi-layered IoT platform and its operating modes. We report and discuss its real-world deployment in the Aveiro Tech City Living Lab in Aveiro, Portugal with functional and performance tests. We tested device-to-device communication across edge and core layers and also interconnected the infrastructure with one in São Paulo, Brazil, replicating the use of a global industry. The successful deployment validates the use of a Helix Multi-layered IoT platform as a suitable backend platform for IIoT applications capable of establishing the IIoT-to-cloud continuum. It also helps for the deployment of other applications in such a domain.

Industrial Internet of Things Security enhanced with Deep Learning Approaches for Smart Cities

2020 ◽  
pp. 1-1
Author(s):  
Naercio Magaia ◽  
Ramon Fonseca ◽  
Khan Muhammad ◽  
Afonso H. Fontes N. Segundo ◽  
Aloisio V. Lira Neto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Smart Cities ◽  
Learning Approaches ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Industrial Internet of Things

2021 ◽  
pp. 133-148
Author(s):  
Sam Goundar ◽  
Akashdeep Bhardwaj ◽  
Safiya Shameeza Nur ◽  
Shonal S. Kumar ◽  
Rajneet Harish
Keyword(s):  
Internet Of Things ◽  
Value Chain ◽  
Smart Cities ◽  
Value Added ◽  
Traffic Patterns ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
The Difference ◽  
Human Operators ◽  
And Control

This chapter focused on the importance and influence of industrial internet of things (IIoT) and the way industries operate around the world and the value added for society by the internet-connected technologies. Industry 4.0 and internet of things (IoT)-enabled systems where communication between products, systems, and machinery are used to improve manufacturing efficiency. Human operators' intervention and interaction is significantly reduced by connecting machines and creating intelligent networks along the entire value chain that can communicate and control each other autonomously. The difference between IoT and IIoT is that where consumer IoT often focuses on convenience for individual consumers, industrial IoT is strongly focused on improving the efficiency, safety, and productivity of operations with a focus on return on investment. The possibilities with IIoT is unlimited, for example, smarter and more efficient factories, greener energy generation, self-regulating buildings that optimize energy consumption, smart cities that can adjust traffic patterns to respond to congestion.

A novel and secure attacks detection framework for smart cities industrial internet of things

2020 ◽  
Vol 61 ◽  
pp. 102343 ◽  
Author(s):  
Kashif Naseer Qureshi ◽  
Shahid Saeed Rana ◽  
Awais Ahmed ◽  
Gwanggil Jeon
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Industrial Internet Of Things ◽  
Industrial Internet

scholarly journals Cloud Based Smart City Services for Industrial Internet of Things in Software-Defined Networking

2021 ◽  
Vol 13 (16) ◽  
pp. 8910
Author(s):  
Himanshi Babbar ◽  
Shalli Rani ◽  
Aman Singh ◽  
Mohammed Abd-Elnaby ◽  
Bong Jun Choi
Keyword(s):  
Internet Of Things ◽  
Load Balancing ◽  
Heuristic Algorithms ◽  
Smart Cities ◽  
Software Defined Networking ◽  
Threshold Value ◽  
Support Network ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
The Impact

The network session constraints for Industrial Internet of Things (IIoT) applications are different and challenging. These constraints necessitates a high level of reconfigurability, so that the system can assess the impact of an event and adjust the network effectively. Software Defined Networking (SDN) in contrast to existing networks segregates the control and data plane to support network configuration which is programmable with smart cities requirement that shows the highest impact on the system but faces the problem of reliability. To address this issue, the SDN-IIoT based load balancing algorithm is proposed in this article and it is not application specific.Quality of service (QoS) aware architecture i.e., SDN-IIoT load balancing scheme is proposed and it deals with load on the servers. Huge load on the servers, makes them vulnerable to halt the system and hence leads to faults which creates the reliability problem for real time applications. In this article, load is migrated from one server to another server, if load on one server is more than threshold value. Load distribution has made the proposed scheme more reliable than already existing schemes. Further, the topology used for the implementation has been designed using POX controller and the results has been evaluated using Mininet emulator with its support in python programming. Lastly, the performance is evaluated based on the various Quality of Service (QoS) metrics; data transmission, response time and CPU utilization which shows that the proposed algorithm has shown 10% improvement over the existing LBBSRT, Random, Round-robin, Heuristic algorithms.

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

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