The Industrial Internet of Things (IIoT) has revolutionized industrial processes, offering automation and data-driven decision-making. However, this interconnectedness brings new security challenges, especially in crucial infrastructure sectors. Traditional security measures are inadequate, leading to the exploration of innovative solutions. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature. This paper proposes a Hybrid Blockchain-Based Authentication Mechanism for IIoT, combining Delegated Proof of Stake (DPoS) and Elliptic Curve Cryptography (ECC). The hybrid architecture utilizes public and private blockchains to ensure scalability, efficiency, and security. Lightweight consensus algorithms, DPoS, are incorporated to optimize performance, while ECC provides efficient cryptographic techniques suitable for IIoT environments. An interoperable framework facilitates seamless integration with existing infrastructure, ensuring regulatory compliance and compatibility. Decentralized identity management further enhances security and privacy. Results and analysis demonstrate the effectiveness of the proposed solution, positioning hybrid blockchain architecture as the most suitable approach for enhancing security in IIoT environments.

This paper proposes Blockchain-Enhanced Multi-Factor Authentication (BEMFA) to address the limitations of existing authentication mechanisms in the Industrial Internet of Things (IIoT). BEMFA combines multi-factor authentication (MFA) with blockchain technology to ensure robust, scalable, and tamper-resistant security tailored to IIoT environments. This method dynamically manages roles and permissions, detects malicious devices, and ensures data integrity and authenticity. Our results demonstrate that BEMFA significantly enhances security, addressing critical access control challenges and mitigating risks posed by malicious devices while maintaining data integrity.

The need for electrical energy is increasing, especially in rural areas, so building an environmentally friendly energy power plant such as a micro hydro generator can be used to produce electrical energy. This PLTMH planning aims to help the community produce environmentally friendly electrical energy and overcome frequent power outages. The research discusses determining the type of turbine, water discharge, turbine power and generator power at (PLTMH). The water discharge (Q) value of 2.67 m3/sec is the water discharge during the rainy season. From the calculation results, it can generate 25.65 kW of electrical energy, the type of turbine used is an open flume propeller type turbine, the planning of a micro-hydro power plant with a turbine power capacity of 60 kW and a generator of 50 kW greatly influences the power that will be produced by the turbine, which This means that the greater the water discharge, the greater the power generated by the turbine, conversely, if the water discharge is small, the power produced by the turbine is relatively small.
 
 

Data transmission efficiency is crucial in wireless sensor networks (WSNs), where limited battery life and signal reliability are significant concerns. This research explores various machine learning algorithms aimed at optimizing data transmission in WSNs, focusing on reducing energy consumption and enhancing network stability. Simulation results indicate marked improvements in efficiency, making WSNs more viable for long-term deployment across diverse environments.

Renewable energy systems, particularly those incorporating solar and wind power, are prone to stability issues due to the intermittent and fluctuating nature of these energy sources. This paper investigates several nonlinear control techniques—namely, adaptive control, sliding mode control, and fuzzy logic control—that are designed to improve stability in renewable energy systems. Through simulations, we demonstrate how these methods can effectively reduce fluctuations and maintain stable energy output, offering a robust approach to enhancing the reliability of renewable energy systems.

As electric vehicles (EVs) gain popularity, understanding the demand for EV charging infrastructure becomes essential for effective urban planning. This paper develops mathematical models to predict EV charging demand based on various factors, including population density, traffic patterns, and energy consumption data. The models provide valuable insights for city planners regarding the optimal placement and capacity of EV charging stations to meet future demand, facilitating the transition to a more sustainable urban environment.

Battery storage systems are essential for stabilizing renewable energy grids, especially for sources like solar and wind power that are inherently variable. This study evaluates various battery technologies and their effectiveness in storing and redistributing energy within renewable energy grids. Through simulations and analysis of performance metrics, the study offers insights into optimal operating conditions for different battery types, highlighting their role in enabling sustainable and stable energy distribution.

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.
 
 

The Industrial Internet of Things (IIoT) faces various challenges in ensuring secure communication, authentication, and data integrity due to its distributed nature and evolving threat landscape. To address these issues, this paper proposes the integration of blockchain authentication as a robust solution to enhance security and reliability in IIoT networks. By leveraging Federated Learning with blockchain technology, the proposed solution aims to improve authentication mechanisms by training models across multiple edge devices, increasing fault tolerance, and adaptability while reducing the risk of single points of failure. The use of blockchain technology ensures a tamper-proof and transparent ledger for securely storing authentication data and model updates, enhancing security and integrity in IIoT networks. The results and analysis demonstrate that the integration of Federated Learning and blockchain technology effectively addresses interoperability issues, performance optimization concerns, and security vulnerabilities within IIoT networks, offering a more efficient, secure, and scalable authentication alternative.