FPGA Implementation of Secure Block Creation Algorithm for Blockchain Technology

Blockchain technology is essential to secure storage, authenticate data, and protect information from being misused and exploitation. Traditional methods of securing data using cryptographic algorithms include hashing functions like SHA-0, SHA-1, which have limitations like excess computational time, collision attacks, scalability, backtracking to retrieve the original message, etc. Using a combination of RSA and SHA together allows us to create a block on an FPGA, which when combined with other blocks establishes an encrypted Blockchain, which overcomes such limitations. Synthesis and implementation of the encrypted block have been compared and analyzed on Virtex-4, Virtex-5, and Spartan-6 FPGA boards. Based on the resource requirement like the slice registers, LUT-FF pairs and memory, Virtex-5 was chosen. Complete security is achieved as the hashing process is irreversible and backtracking of data is not possible. Previous problems of strengthening security, backtracking, excessive memory usage, and zero collision attacks are addressed and solved.</jats:p

IoT Enabled Sustainable Automated Greenhouse Architecture with Machine Learning Module

In recent years, the information system has laid a profound foundation in agriculture with greenhouse development, leading to accelerated growth. The green infrastructure thus built is easily accessible remotely using the intelligent system of Internet of Things (IoT). In this proposed work, an IoT-based environment is designed, developed, and implemented with sensors which are connected to the laptop/computer or a mobile phone with Internet. Further to save electricity, a separate control unit is built which provides the devices an energy efficient way of functioning. Thus, information regarding growth of the plants, moisture content in the soil, energy consumed by each smart appliances in the farm, etc., is collected using data acquisition. The data thus gathered is then segregated depending on the applications and sent to the Firebase cloud. To monitor the environmental parameters within the greenhouse, we have used a cloud-based data collection mechanism. Interfacing the dashboard with the cloud platform, it is possible to analyze the power consumed by the system using the data present. When a discontinuity occurs with data missing for about an hour, the missing data is filled with the help of previous data automatically. The maximum temperature within the greenhouse is set as 28°C, and the soil moisture content threshold is set between 50% and 80%. An artificial environment is thus created to improve the crop yield per square meter on continuous monitoring of climatic parameters resulting in an optimal environment.</jats:p

Secure IoT Healthcare Architecture with Deep Learning-Based Access Control System

The existing healthcare system based on traditional management involves the storage and processing of large quantities of medical data. The incorporation of the Internet of Things (IoT) and its gradual maturation has led to the evolution of IoT-enabled healthcare with extraordinary data processing capability and massive data storage. Due to the advancement in the Industrial Internet of Things (IIoT), the resulting system is aimed at building an intelligent healthcare system that can monitor the medical health of the patient by means of a wearable device that is monitored remotely. The data that is gathered by the wearable IoT module is stored in the cloud server which is subject to privacy leakage and attacks by unauthorized users and attackers. To address this security issue, an IoT-based deep learning-based privacy preservation and data analytics system is proposed in this work. Data is collected from the user, and the sensitive information is segregated and separated. Using a convolutional neural network (CNN), the health-related information is analyzed in the cloud, devoid of users’ privacy information. Thus, a secure access control module is introduced that works based on the user attributes for the IoT-Healthcare system. A relationship between the users’ trust and attributes is discovered using the proposed work. The precision, recall, and F1 score of the proposed CNN classifier are achieved at 95%. With the increase in the size of the training set, higher performance is attained. When data augmentation is added, the system performs better without data augmentation. Further, the accuracy of around 98% is achieved with an increased user count. Experimental analysis indicates the robustness and effectiveness of the proposed system with respect to low privacy leakage and high data integrity.</jats:p