Internet of Things (IoT) is growing at an exponential rate but the area of privacy and security in IoT still remains unexplored. The existing algorithms or methods are mainly centralized and hence they are vulnerable due to their single point authentication topology. As it has been estimated that by 2020 there will be more 'things' than people on this earth the problem of security becomes a major concern in IoT networks, as a person having control to an IoT network will be able to control a large portion of an organization. Blockchain has recently been used to provide security to peer-to-peer networks.
Blockchains are computationally expensive, heavyweight and are considered unsuitable for IoT architecture. In this paper a new lightweight and secure architecture for IoT by using Ethereum Blockchain retaining most of its security providing powers is proposed. Since Blockchain is decentralized it solves the single point authentication problem existing in IoT networks. A Smart Home System as a representative case study has been implemented for broader IoT applications. The two parameters measured are temperature and intrusion detection. The proposed model tackles some more challenges that exist in IoT networks. The Qualitative evaluation of the proposed architecture highlights how it tackles various attacks.
At present usage of computational intelligence became the ultimate need of the heavy engineering industries. Digitization can be achieved in these sectors by scanning the hard copy images. When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem. In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data. This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method. Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation. Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other. By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as mis-spelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.
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