Many of our ordinary things will soon be linked, due to contemporary Internet of Things (IoT) technology. These items will be able to connect and interact with one other as well as their surroundings, automating much of our job. Security, seamless authentication, resilience, and simplicity of maintenance are essential for IoT node communication. Blockchain appears as a possible option to provide these vital features. Blockchain’s decentralised nature has enabled it to address several IoT security, maintenance, and authentication issues. As a consequence, the number of blockchain-based Internet of Things applications has increased dramatically in recent years. This study presents a blockchain-based industrial IOT method using RSA encryption. RSA encryption results in fast encryption.
Expanded data collection and processing at greater rates have come from a growth in the number of devices connected to the internet, which is especially important now that the requirement for real-time action has increased significantly in recent years. It is becoming more difficult to finish data processing within an acceptable time frame as the variety and validity of data continues to grow in volume and quality. Because the data generated is supplied to a variety of different cloud data centres located all over the world, the current cloud architecture is a sub-optimal choice in these situations, according to the researchers. In this approach, implementing machine learning technologies into the present cloud environment may improve the overall efficiency and effectiveness of the cloud environment. In this article, works on machine learning and security in the Internet of Things platform are evaluated.
Graphene nano ribbon field effect transistor is an emerging field of research in VLSI technology beyond 32nm. VLSI main motive is to reduce power consumption and other parameters such as delay, PDP (power delay product) to improve the efficiency. This article discusses the requirement for high-performance applications that use little power. However, employing low-power devices for high-rank applications such as microprocessors, digital signal processors, and static random-access memory (SRAM) is very challenging. In the field of memory design and logical circuit design, it is well recognised that the Decoder plays a crucial role. As part of the planned work in 22nm technology, a technique has been developed for evaluating the parameters Power delay product, Energy delay product, Power consumption, and Delay on 14T and 15T based employing 2 to 4 decoders based on MOSFET and GNRFET. So, the final proposed circuit is obtained by application of decoders with the use of GNRFET while improving the performance parameters.
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