Table of contentsIntroductionLiterature reviewMethodologyMonitoring system via WSNExperimentation/modelingCase studiesResults and discussionsConclusionBig Data can be generated in the manufacturing and automotive sectors by using Internet of Things (IoT) technology where the generation of a myriad of data is possible. Industrial IoT is driving businesses to change and adopt new, emerging data-driven strategies. This article explains how IoT in manufacturing and automotive sectors will generate and store industrial big data. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayIntroductionIn the fourth industrial revolution, IoT and big data play a vital role as manufacturing systems transform into digital ecosystems. IoT, the network of interconnected devices that exchange data and thus create opportunities to increase efficiency, reduce errors and economic benefits. Data exchange and storage in IoT will directly feed into Big Data which can then be used in useful ways. Nowadays, many automobiles and machine components are equipped with IoT sensors to generate big data. In modern and advanced industries, data generated by IoT sensors is already received in a huge volume, which exceeds thousands of exabytes per year, and it is predicted to be even more so in the coming years. These data-driven strategies will allow companies to optimize their costs, errors and thus increase their profits. The big data generated will enable the company to work on predictive analytics and increase competitive advantage in the market. This paper explains how the adoption of IoT in manufacturing will generate industrial Big Data and how it can be usefully used with appropriate case studies. Additionally, the optimized and non-disruptive IoT application for SMEs is explained by exploring the high volume of data that can be generated.Literature ReviewD. Mourtzis (2016) explained the use of industrial IoT to generate industrial big data and briefly discussed the advantages and disadvantages. It finally concluded and analyzed the quantity and size of data that can be generated and analyzed in a case study of a workshop with around a hundred machines. He had also carried out the mapping of OPC-UA with the Open Systems Interconnection (OSI) model developed from it. J. Ben Naylor (2007) used industrial Big Data beneficially by using the Lean principle and identifying where and when an error might occur using predictive analytics. Pramudianto F (2015) carried out work on the use of IoT. in the control of industrial robots and also monitored the energy consumption of individual robots and optimized it using algorithms. The STM32W node platform is used to monitor the robot's movement. And they concluded the application of big data generated by the use of IoT and explained how it can be used in beneficial ways, such as monitoring and optimizing energy consumption. Methodology Generating big data using the 'IoT in manufacturing sectors: a survey conducted by Batty et. al, predicted that industrial big data would reach a total volume of more than 1,000 exabytes per year. If we compare the big data generated by IT companies, it is very little, but it tends to increase in the coming years. For thisFor this reason, the data generated by the use of IoT in industries is called “Industrial Big Data” and not “Big Data”. The ultimate goal of adopting IoT in industries is to launch smart factories, in which individual machines are interconnected with each other and connected to a network. To achieve this, the resource must be connected to the Internet directly or through external adapters. , the machine tool system will be converted and transformed into a cyber machine tool system enriched with the knowledge acquired from the data collected and analyzed. And the resource also contains human operators connected via the Internet using mobile devices and thus converting operators into cyber operators. And finally, IT and business tools will be connected to the network. Data collected from low-level companies is very important because it can be analyzed to obtain meaningful information that will be used by the higher-level company. One of the main challenges of this transformation is the design and development of standard and secure communication protocols, capable of interfacing existing systems and collecting and exchanging manufacturing data. An IoT application, supported by a WSN and built on a standard industrial communications protocol, is described below, showcasing how industrial Big Data can be generated. A monitoring system via the WSNA monitoring tool organized in a wireless sensor network (WSN) is presented. The monitoring tool consists of a data acquisition device (DAQ) that uses split-core current transformers (CT) as current sensors, a closed-loop Hall effect current sensor, as well as than a camera. These sensors are selected to create a non-intrusive and easy-to-install application to monitor the condition of machine tools. The proposed tool is designed as a complement to commercial machine tools, rather than communicating with the machine controller. This decision is mainly motivated by the fact that the lifespan of industrial equipment can reach 50 years and therefore old machines often do not have the required connectivity capabilities. Therefore, special efforts are required to transform each existing controller into an IoT device.Experimentation/ModelingImplementation of IoTs in Two different case studies have been discussed in this paper which uses IoT to generate industrial big data that can be analyzed in more detail and used in useful ways. way.Case StudiesIn the VIT machining process laboratory: Using an IoT sensor like WSN, the energy consumption of individual machines in the laboratory can be monitored and if an abnormal amount of energy consumption takes place, it can be monitored from the collected big data. It can also measure the optimized process parameter in which the energy consumption is significantly lower compared to the conventional process. For example, a conventional turning process can be carried out on a lathe connected to an IoT sensor and from which big data has been recorded, so that the energy consumption of the lathe can be calculated with and without the use of turning fluid. cut. Cutting fluid plays a vital role in dissipating heat in a machining process. There will therefore always be a loss of energy in the form of heat when the cutting fluid is not used. But when cutting fluid is used, these energy losses in the form of heat can be reduced. Thus, by implementing IoT in machines, the amount of energy that can.