Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 379,67
Währung umrechnenAnzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
EUR 396,54
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: new. Paperback. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Anbieter: AussieBookSeller, Truganina, VIC, Australien
EUR 469,12
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 499,60
Währung umrechnenAnzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: Grand Eagle Retail, Fairfield, OH, USA
EUR 442,83
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
EUR 521,21
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbHardcover. Zustand: new. Hardcover. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Anbieter: Grand Eagle Retail, Fairfield, OH, USA
EUR 578,79
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbHardcover. Zustand: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Anbieter: AussieBookSeller, Truganina, VIC, Australien
EUR 612,31
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbHardcover. Zustand: new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.