IndexIntroductionLiterature ReviewMethodologyMonitoring System via WSNEExperimentation/ModelingCase StudiesResults and DiscussionsConclusionBig data can be generated in manufacturing and automotive sectors using Internet of Things technology ( IoT) where it is possible to generate a myriad of data. Industrial IoT inspires companies to change and adopt a new and emerging data-driven strategy. This article explains how IoT in the 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 Shouldn't Be Banned"? Get an original essay IntroductionIn the fourth industrial revolution, IoT and Big Data play a vital role as manufacturing systems are transforming into digital ecosystems. IoT, the network of interconnected devices that exchange data creating opportunities to increase efficiency, reduce errors and obtain economic benefits. Data exchange and storage in IoT will directly feed big data which can be further used in a useful way. Nowadays, many cars 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 huge volumes, more than a thousand Exabytes per year and is expected to be even more in the coming years. These data-driven strategies will allow companies to optimize costs and errors and therefore increase profits. The Big Data generated will allow the company to work on predictive analysis and increase its competitive advantage in the market. This paper explains how the adoption of IoT in the manufacturing sector will generate industrial Big Data and how this can be further used usefully with some suitable case studies and additionally the cost-optimized and seamless IoT application is explained for SMEs by exploring the high volume of data that can be generated. Literature Review D. Mourtzis (2016) explained the use of Industrial IoT to generate Industrial Big Data and briefly discussed the pros and cons. Finally he concluded and analyzed the quantity and size of data that can be generated and analyzed in a case study of a shop of hundreds of machines. He also mapped OPC-UA to the Open Systems Interconnection (OSI) model built upon it. J. Ben Naylor (2007) used industrial Big Data to advantage by using the lean principle and identifying where and when an error might occur using predictive analytics. Pramudianto F (2015) did work on using IoT in industrial robot control and also monitored the energy consumption of individual robots and optimized using algorithms. The STM32W node platform is used to monitor the movement of the robot. And they concluded the application of big data generated using IoT and further how it can be used beneficially, such as monitoring and optimizing energy consumption. MethodologyBig data generation using IoT in manufacturing sectors: a survey by Batty et. al, predicted that industrial big data would reach a total volume of over 1000 exabytes per year. Comparing the big data generated by IT companies they are much less but they tend to increase in the coming years. For this reason, the data generated using IoT in industries is called “Industrial Big Data” and not “Big Data”. The ultimate goal of IoT adoption in industries is to start smart factories, where individual machinesthey are interconnected with each other and connected to a network. To achieve it, the resource should be connected to the Internet directly or through external adapters. As a result, the machine tool system will be converted and transformed into a cyber machine tool system enriched with the knowledge gained from the collected and analyzed data. And the resource also contains human operators connected via the internet using mobile devices and thus transforming operators into cyber operators. Finally, IT and business tools will be networked. The data collected from the low-level enterprises is very important because this data can be analyzed to get some meaningful information which is used by the higher-level enterprise. One of the main challenges towards this transformation is the design and development of standard and secure communication protocols capable of interfacing existing systems and collecting and exchanging production data. An IoT application, supported by a WSN and designed on a standard industrial communication protocol, is described below, presenting how industrial Big Data can be generated. The monitoring system using the WSNA monitoring tool organized in a wireless sensor network (WSN) is presented. The monitoring instrument consists of a data acquisition (DAQ) device using split-core current transformers (CT) as current sensors, a closed-loop Hall effect current sensor, and a camera. These sensors are selected to create a non-intrusive and easy to install application for monitoring the health of machine tools. The proposed tool is designed as an add-on for commercial machine tools, rather than communicating with the machine controller. This decision is mainly due to the fact that the lifespan of industrial equipment can reach 50 years, so old machinery often lacks the necessary capabilities for connectivity. Therefore, special effort is required to transform each legacy controller into an IoT device. Experimentation/Modeling Implementation of IoTs In this paper, two different case studies have been discussed that use IoT to generate industrial Big Data which can be further analyzed and used in a useful way. Case Studies In the VIT machining process laboratory: Using an IoT sensor such as WSN, you can monitor the energy consumption of individual machines in the laboratory, and if there is an abnormal amount of energy consumption, you can monitor the collected big data . It can also measure optimized process parameters where energy consumption is significantly lower than in the conventional process. For example, the conventional turning process can be performed in 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 cutting fluid . Cutting fluid plays a vital role in dissipating heat in a machining process, so there will always be a loss of energy in the form of heat when cutting fluid is not used. But when using cutting fluid, these energy losses in the form of heat can be reduced. Therefore, by implementing IoT in machines, the amount of energy that can be saved can be easily monitored and calculated. There are 4 lathes and 3 drilling machines and 1 milling machine is the processing laboratory, considering 8 machines in total and the data that can be generated when the machine runs continuously for one day is 2GB. Considering a workshop with 100 machines, the data generated will be equal to 204 GB, which will generate.