Advancing Intelligent Networks Through Distributed Optimization
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Advancing Intelligent Networks Through Distributed Optimization
The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire device’s learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems’ optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities.
Machine Learning, Predictive Analytics, and Optimization in Complex Systems
Author: John Joseph, Ferdin Joe
language: en
Publisher: IGI Global
Release Date: 2025-06-27
The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists.
Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World
The rapid evolution of AI, data science, and emerging technologies is reshaping industries, economies, and societal interactions on an unprecedented scale. These advancements drive innovation in areas such as healthcare, finance, transportation, and environmental sustainability, offering transformative solutions while also raising critical ethical and regulatory challenges. As AI systems become more integrated into processes, multidisciplinary collaboration is essential to ensure transparency, fairness, and accountability. The responsible development and deployment of AI will determine its long-term impact, shaping a future where technology aligns with human values and social progress. Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World examines the technical basis, ethical issues, social repercussions, and prospects of AI-driven progress. It guides readers through the complex and ever-changing world of AI-driven transformation, preparing them to contribute to a future where technology serves humanity responsibly. Covering topics such as blockchain, machine learning, and social media, this book is an excellent resource for academicians, researchers, industry professionals, policymakers, business leaders, entrepreneurs, students, educators, and more.