Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities

© 2024 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.Peer reviewe

Taxi Services Revenue Optimization through Reinforcement Learning: A Deep Q Network Approach with Advanced Data Visualization

This study investigates the burgeoning challenges faced by urban taxi services, focusing on optimizing the operational efficiency of taxi drivers amidst the increasing urban congestion and pollution. The primary aim is to enhance drivers' earnings while promoting sustainable urban mobility. The research introduces an innovative framework that assimilates real-world taxi trip data to provide drivers with strategic insights on trip selection, thereby augmenting their income and operational efficiency. The methodology harnesses advanced data processing techniques to translate trip data into actionable insights. A comprehensive analysis is conducted to evaluate various operational parameters, such as trip distances, financial transactions, and service patterns. The findings are presented using sophisticated visualization tools, which illustrate the efficacy of the recommended strategies in improving the overall taxi service framework. The outcome of the research is a set of data-driven recommendations that empower taxi drivers with knowledge to make informed decisions. This not only promises a direct enhancement of their livelihood but also contributes to alleviating traffic and pollution by streamlining taxi operations. The implications of this research extend to informing policy-making, with the potential to shape future urban transport strategies that align with the global objectives of sustainable development and environmental preservation

Taxi Revenue Optimization with Deep Q-Learning and Enhanced Data Visualization

This study investigates the burgeoning challenges faced by urban taxi services, focusing on optimizing the operational efficiency of taxi drivers amidst the increasing urban congestion and pollution. The primary aim is to enhance driver's earnings and livelihood while promoting sustainable urban mobility. The research introduces an innovative framework that assimilates comprehensive real-world taxi trip data to provide drivers with strategic insights on trip selection. A comprehensive analysis is conducted to evaluate various operational parameters, such as trip distances, financial transactions, and service patterns. The findings are presented using sophisticated visualization tools, which illustrate the efficacy of the recommended strategies in improving the overall taxi service framework. The outcome is a set of data-driven recommendations that empower taxi drivers with knowledge to make informed decisions using Deep Q-Learning. This contributes to alleviating traffic and pollution by streamlining taxi operations, with further implications extending to global policy-making for shaping sustainable transport policies and environmental preservation.</p

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-