A Framework of Internet of Things (IOT) for the Manufacturing and Image Classifaication System

Real-time identification of industrial processes and real-time image classification are necessary for recent days to reduce the excess power consumption and detect water pollution respectively. Usually, robotic technology in manufacturing industries is a costly initiative; thus, researchers are searching for a low-cost and effective loT (Internet of Things) technology that will detect and measure real time-condition of industrial machines. Moreover, the loT technology is useful in image classification as well for detecting water pollution. This research has explained the benefits and cost-effectiveness of available loT along with a comparison between image classification techniques. A primary quantitative survey method has been carried out to collect practical information based on the judgement of 60 respondents. Thereafter, the ‘Purposive’ sampling technique has been applied to discuss the primary data and uphold a significant judgement. Findings and analysis suggested that WSN (Wireless Sensor Network) is a cheaper technology that can be integrated into small- and large-scale manufacturing industries. Image classification loT has been identified for detecting water pollution where texture analysis is cheaper than spatiotemporal analysis.