The study introduces an efficient data aggregation technique for smart agriculture by leveraging Blockchain technology and a novel method referred to as the “cluster head sleep schedule.” The primary objective is to enhance the data collection process within a large-scale agricultural setting where multiple sensors continually generate vast amounts of data while monitoring and safeguarding crops from pest attacks. The proposed method involves the segmentation of sensors into clusters, each led by a designated cluster head responsible for collecting data from its constituent members deployed in the field to monitor pest attacks and promptly report any issues to the management. To curtail data redundancy, the study employs a fuzzy matrix to group nodes based on high-similarity data. This approach enables the selective suspension of certain nodes while others remain active. The data received from these nodes undergoes analysis using a fuzzy similarity matrix for clustering, ensuring that only unique data is transmitted to the base station. Redundant nodes from all clusters are identified and placed in a sleep mode, thus conserving energy and prolonging the network’s lifespan. This sleep scheduling mechanism is implemented subsequent to data redundancy reduction, facilitating immediate pest attack control in agriculture. By implementing these techniques, smart agriculture stands to benefit from optimized energy utilization and reduced costs associated with monitoring and pest control, thereby fostering sustainable and efficient operations. The cluster head is responsible for storing the data on a base station positioned at the network’s edge, allowing for local processing and prompt communication of pest attack information to the farmer for immediate action. Moreover, this edge system stores the data on a Blockchain network for future analysis and serves as a guideline for pest attack control in the pesticide industry, thereby enhancing data security and immutability. In addition to these advantages, the research also emphasizes the importance of controlling pest attacks to enhance crop production in the field, ultimately contributing to the country’s economic growth. Simulation results affirm that the proposed approach leads to notable cost reductions, decreased energy consumption, improved crop production, precise crop monitoring to prevent pest attacks, and a prolonged network lifespan. These outcomes underscore the effectiveness of this approach within the context of smart agriculture and its role in enhancing the monitoring system for smart agriculture and bolstering security through Blockchain technology.

This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. Our approach integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing to create a robust and dependable medical ecosystem. In our proposed system, patients’ health data is securely managed using a combination of elliptic curve cryptography-based identity-based cryptosystems and edge nodes, ensuring both privacy and integrity. These edge nodes, designed for low-power and short-range communication, play a pivotal role in in-vivo data collection and monitoring within the human body. The DLT model at the core of our framework utilizes peer-to-peer networks, enabling seamless information exchange while eliminating the need for centralized servers. We emphasize public edge DLTs, such as Ethereum, to ensure accessibility and data ownership for all stakeholders. Furthermore, our system incorporates a hybrid machine learning model for early detection and prediction of security threats, enhancing overall system efficiency. Our findings demonstrate a remarkable 99.7% accuracy in classification using this approach. In conclusion, this framework’s multidisciplinary approach bridges the gap between healthcare, edge computing, and DLT, promising real-time data processing, enhanced security, and privacy preservation. With the rise of the Internet of Things, this innovation holds the potential to transform the future of healthcare technology.

Cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual Studio Net framework environment is increased. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud.

Over the past few years, there has been a notable surge in the integration of Blockchain technology into supply chain management systems. This integration holds the promise of enhanced transparency, security, and efficiency in monitoring the movement of goods and services. This study presents a novel approach aimed at fortifying privacy and accuracy within blockchain-based supply chain management systems. The methodology integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) units with secure multi-party computation (MPC) and differential privacy techniques as a hybrid model. The objective is to safeguard the confidentiality of transaction data while enabling precise detection of media tampering. Performance evaluation revolves around three key aspects: accuracy, privacy preservation, and computational efficiency. In terms of accuracy assessment, the proposed hybrid approach is benchmarked against traditional machine learning algorithms including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Random Forest. Results indicate superior performance, with the proposed hybrid method achieving an accuracy of 0.95, outperforming conventional algorithms. Precision, recall, and F1-score metrics further confirm the effectiveness of the approach in accurately identifying media tampering instances. Privacy preservation capabilities are evaluated through differential privacy techniques, revealing the method’s ability to inject controlled noise into the data to protect individual privacy. Results demonstrate varying levels of privacy preservation across different settings, highlighting the trade-off between privacy and data utility. Computational efficiency is also scrutinized, considering the additional overhead introduced by privacy preservation mechanisms and secure MPC protocols. While there is a slight increase in computational time, the proposed approach maintains reasonable training and inference times, ensuring practical applicability in real-world scenarios.