Optimizing IoT-Based Smart Grids with AI and Blockchain: A New Approach for Real-Time Energy Management

oai:ojs2.ijerams.org:article/3The appearance of the Internet of Things (IoT) and the advancement of the energy systems provided a chance to the emergence of the smart grids which allow to arrange the effective management of the energy in real-time. Despite the smart grids being useful when the energy consumption distribution is optimized, they demand substantial challenges in terms of their scalability, security and effective analysis of millions of participated devices-generated data. Blockchain and Artificial Intelligence (AI) have been advanced as the remedy to such concerns. Blockchain can be effectively combined with AI in the work of smart grids with its decentralized immutable traceability and ability to forecast, suggest, and identify anomalies, and with data security, transparency, and integrity, on another level, making smart grids capable of functioning autonomously and efficiently. The feasibility of integrating AI and Blockchain to optimise smart grids on a platform of IoT with the view of employing them in the real-time management of energy is discussed in the paper. The study gives the benefits of using the AI in predictive maintenance, management and management of demand, and demand-response forecasting and demand-response loads, and the Blockchain in safe transaction relating to safe exchange of data and transparent transaction. A conceptual scheme of the way these technologies could be applied in smart grid systems is proposed, and after it, the discussion of the potential use of these technologies, their challenges, and solutions is presented. Based on the study, smart grids that rely on the IoT will be more efficient, secure, and scalable and combining them with the AI and the Blockchain may offer a chance of attaining sustainability in energy management within smart cities

Blockchain-Integrated Machine Learning for Autonomous IoT Networks: A Paradigm Shift in Data Security

With the introduction of the idea of the IoT (the Internet of Things), new opportunities and the issues that come along with it in the area of data safety are created. The greater the number of devices connected to the Internet of Things network, the more complex and bulky the field of securing data becomes. The Blockchain and Machine Learning (ML) technology can provide a solution to that end in order to eliminate such challenges. Blockchain offers privacy, transparency and scalable features through which data integrity is improved, and the ML brain is a solution to facilitate autonomous decision and detecting anomalies inside the IoT network. In this paper, the author explains how Blockchain and ML may be used to create a secure and stable IoT ecosystem. It talks about how the integration can maximize data security and the optimum resources and scalability of independent operations of the networks IoT. The study also speaks about the possible benefits, difficulties and the future consequences of such paradigm shift on the security of the data. Through its critical investigation, the following paper is going to provide an outline of future studies and potential application of Machine Learning uses Blockchain in self-managing internet-of-things networks

Exploring Blockchain-Based Trust Models in IoT-Driven Healthcare Systems: A Machine Learning Approach

The IoT devices are experiencing growth in the healthcare industry as they are being used to track the health status of patients, to treat them and also to perform other functions. However, the IoT-based healthcare system has enormous problems regarding data security, confidentiality, and trust, especially when salient medical information is being transferred across various networks. One of the potential answers to these hiccups is the blockchain technology that is not centralized, immutable and transparent. Together with that, trust models could be enhanced by utilising the capabilities of Machine Learning (ML), through predictive analytics, anomaly detection, and real-time decision-making, which will further increase the security and efficiency of the IoT healthcare system. The article explains the process of the integration of such tools as Blockchain-based trust models, and Machine Learning used in IoT-powered healthcare environments. This research paper aims at developing an efficient solution on how to protect the IoT health networks, open data sharing, and develop confidence between the devices and the healthcare provider and, patients. Using the case studies and simulations, we represent how Blockchain and ML can be applied jointly so that to develop safe, effective, and scalable health applications. The paper also disadvantages and provides a glimpse of what will be done on the future research of using the technologies to enhance the delivery of the healthcare

AI-Powered Blockchain for Decentralized IoT Applications: Enhancing Security and Efficiency in Edge Computing

The Internet of Things (IoT) disrupted all the industries that allowed controlling the processes of all businesses in the real-time regime using automatization and optimization. Security issues, centralized control as well as inefficient processing of data on the other hand may face the IoT networks. On edge computing, this is aggravated by the fact that the data being processed is done near the source to minimize latency and bandwidths utilized by the IoT devices. This can be solved because the decentralized blockchain technology is able to offer security and transparency to the process of using the IoT. Besides it, the Artificial Intelligence (AI) had an opportunity to enhance the quality of decision-making and create opportunities to predict and identify anomalies. The paper will address the combination of AI and Blockchain as one of the ways of making the decentralized IoT system much safer and faster in an edge computing environment. The strategy therefore helps in ensuring that the IoT applications will be secure and more productive whereby; BlockChain will be used to handle the data securely and; AI will be used to process and make real-time decisions on how the data will be used. The paper suggests the theoretical framework considering the presence of such technologies in the decentralised IoT platforms and the possible implications to the security, scalability as well as the effectiveness. The possible future applications and trends as well as the real world applications are provided, especially of the smart cities, healthcare and industrial IoT

A Novel Machine Learning Algorithm for Enhancing Blockchain Consensus Mechanisms in IoT-Enabled Smart Cities

The need to have more effective and secure Blockchain consensus mechanisms has been brought about by the fact that the Internet of Things (IoT) devices have become increasingly deployed in the smart cities. Though Blockchain brings a decentralized, non-reversible platform to implement the IoT applications, the conventional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS) are questionable in its scalability, energy expense, and latency particularly in the IoT systems with extremely large number of devices. To address these challenging concerns in IoT-powered smart cities, this paper proposes a new Machine Learning (ML) algorithm that would generate the best Blockchain consensus mechanism. The ML algorithm is dynamically flexible with time, and modifies the consensus strategy based on the real-time network health, way in which the IoT appliances behave, and load due to transactions giving the solution more scalability, which is more energyefficient and permits to handle transactions at a faster rate. The proposed approach is addressed on the simulated case of a smart city where data, such as those transmitted by smart meters, traffic control sensors, and environmental sensors, are collected on a realtime basis using the IoT technology. The experimental results confirm that the integrated combination of ML and the Blockchain agreement protocols provides the opportunity of refining the functioning IoT systems in the smart cities in relation to the resources optimization, the reduction of latency, the safety of transaction, and the transparency. With the aid of the Internet of Things, the study introduces an easy answer to the issue of scalability and efficiency that the current models of Blockchain pose to smart cities framework in the sense of being able to scale

Blockchain and IoT Integration for Secure and Transparent Supply Chain Management

Even the tone of proposing the idea of Internet of Things (IoT) to the supply chain management process has already made quite a difference in the sense of promising a more efficient process in the operations process, inventory management and actual tracking of various goods in real time. But with regards to data security, data privacy and data transparency of the data, the question of the data appears in the case of IoT system in the supply chain. The block chain technology has been one of the solutions to such issues as it is an immutable transparent and decentralised technology. As it is a collaborative process, data integrity, traceability, transparent and secure transaction along the supply chain with use of IoT and the Blockchain can be contributed even by the organization. The paper has raised the issue of the combination of the Blockchain and IOT into the supply chain management especially its secure and transparent supply chain management and how blockchain can be used as a means of mesh-up of exchanges, the provenance of products and security of regulatory compliance. The condition of the application of the IoT devices in the context of the data collection and data transfer and the method that Blockchain can be employed in the context of the security of the collected data and its real-time transparency is also stated in the paper. The case studies and simulation represented by the document demonstrate that, it is possible to combine both Blockchain and IoT in a bid to enhance an effectiveness of the operations, and eliminate frauds, as well as allow general visibility of the supply chain. It also looks through the problems that are stepped on along with the implementation of such collective actions, and it is basing on it that answers are given to the questions on how to win the problems