Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps.

5G support for Industrial IoT Applications - Challenges, Solutions, and Research gaps

The development of technologies associated with the fourth industrial revolution is rapid. Construction 4.0 represents the architecture, engineering, construction and operations industries exploration of new technologies, equivalent to Industry 4.0 for the manufacturing industry. These concepts address multiple perspectives besides the technological, such as management and processes. The purpose of this study was to investigate to what extent research regarding construction projects addresses information and communication, automatisation or industrialisation technologies. A scoping review was the method used to perform a quantitative analysis of over two thousand journal papers published from 2015 onwards. The results show that new technologies are addressed separately, while synergy studies are uncommon. Longitudinal analyses show that there was no significant increase in journal papers concerning new technologies from 2015 to 2019. Information and communication was the search criterion with the least number of papers found. The environmental perspective of new technologies was present but the least common from 2019 to 2020. Hence, this review shows that there is an extensive research gap regarding Construction 4.0 technologies in the context of construction projects. Studies regarding synergy and environmental effects of new technologies should increase to start the progress towards a successful entry into the fourth industrial revolution.

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Quantitative review of construction 4.0 technology presence in construction project research

Computer vision-based construction progress monitoring

The world is currently undergoing a new industrial revolution characterized by the digitization and automation of industry through the use of Information and Communication Technologies (ICTs). The construction sector is one of the largest sectors of the industry. Most of the tasks associated with this sector are carried out at worksites that are defined by their dynamism, decentralization, temporality, and the intervention of a large number of workers, subcontractors, machinery, equipment, and materials. These characteristics make this sector a great challenge for the implementation of ICTs. In this paper, the benefits of the use of the Fifth-Generation (5G) of mobile networks in the construction industry are presented. To that end, first, the digitization and automation needs of the sector are jointly analyzed, establishing different use cases and identifying the requirements of each one. Second, the main characteristics of 5G that address these use cases are identified. Third, a global framework for the application of 5G technology to the construction industry is proposed. Finally, an overview of some directions for future work are provided.

https://www.mdpi.com/2079-9292/10/14/1713/pdf

5G for Construction: Use Cases and Solutions

This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things (IoT) and uses a deep learning method for stock forecasting In order to solve the problems such as low accuracy and training complexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature selection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock Considering its future potential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the feature set for prediction based on 17 technical indexes which are commonly used in stock market The optimal feature set is decided via FS to reduce the dimension of data and the training complexity The LSTM algorithm is used to forecast closing price of stock The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the accuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction

/pdf/stock-forecasting-model-fs-lstm-based-on-the-5g-internet-of-40tmoxa0ow.pdf

Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

-Progress monitoring is one of the essential tasks while executing a construction project. Effective monitoring will lead to an accurate and timely analysis of the project’s progress which is required to make vital decisions for project control. On the other hand, inefficient and delayed updates regarding the project’s progress, which is estimated by comparing the as-built status with the as-planned status, will lead to time and cost overruns. Automated progress monitoring techniques are preferred over the conventional manual data entry method as the latter is time-consuming and complex, especially if the project scope is vast. Numerous tools and technologies are being used for progress monitoring of construction projects. Therefore, it is necessary to systematically classify and evaluate them based on their advantages and limitations for successful and appropriate implementation. Hence, this article identifies several progress monitoring methods and classifies them based on the technology they use to support progress monitoring. Then they are evaluated by highlighting their advantages and limitations. Several qualitative and quantitative factors affecting the selection of these technologies for implementation have also been identified. In future, a framework for objectively identifying the project-specific technology will be developed.

Varun Kumar Reja

Papers

A Systematic Classification and Evaluation of Automated Progress Monitoring Technologies in Construction