通过分布式优化推进智能网络的发展

English | 2024 | ISBN: 9798369337394 | 618 pages | True PDF EPUB | 27.91 MB

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.

无线通信和人工智能（AI）的不断进步最近已经将物联网（IoT）网络提升到一种前所未有的连接性和智能水平。这一拓扑在移动边缘计算、智慧城市、智能家居、智能电网以及许多其他智能化应用中得到了鲜明体现。智能网络建立在整合缓存和多代理系统之上，旨在优化数据存储及整个设备的学习过程。然而，这种设计的一个重大缺点在于所有代理都通过中央节点传递状态信息和奖励信息的机制。这种中央节点条件会引发更多的通信开销、潜在的数据泄露以及数据孤岛现象。为了扭转这一趋势，利用分布式优化技术和方法在支持缓存的多代理学习环境中越来越有益。本书通过结合这些竞赛中的最新研究成果将它们整合到一本完整的书中，这本专著可服务于所有对理论和实际感兴趣的学者。本书主要关注智能系统中优化的趋势，并识别了其从制造业到医疗、农业及智慧城市等领域的各种应用。

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