Intelligent Network Design Driven by Big Data Analytics, Iot, AI and Cloud Computing

ISBN 10: 1839535334 / ISBN 13: 9781839535338

Verlag: INSTITUTION OF ENGINEERING & T, 2022

Beschreibung

KlappentextThis book shows how innovations in network analytics, IoTs and cloud computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of s.

Bestandsnummer des Verkäufers 628690967

Bibliografische Details

• Titel: Intelligent Network Design Driven by Big Data Analytics, Iot, AI and Cloud Computing
• Verlag: INSTITUTION OF ENGINEERING & T
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 1839535334
• ISBN-13: 9781839535338
• Einband: Gebunden
• Zustand: New
• Verkäuferkataloge:
  • Importe

Über diesen Titel

Inhaltsangabe

As enterprise access networks evolve with a larger number of mobile users, a wide range of devices and new cloud-based applications, managing user performance on an end-to-end basis has become rather challenging. Recent advances in big data network analytics combined with AI and cloud computing are being leveraged to tackle this growing problem. AI is becoming further integrated with software that manage networks, storage, and can compute.

This edited book focuses on how new network analytics, IoTs and Cloud Computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of service and quality of experience (QoS/QoE) and to improve network performance. From big data and AI analytical techniques for handling the huge amount of data generated by IoT devices, the authors cover cloud storage optimization, the design of next generation access protocols and internet architecture, fault tolerance and reliability in intelligent networks, and discuss a range of emerging applications.

This book will be useful to researchers, scientists, engineers, professionals, advanced students and faculty members in ICTs, data science, networking, AI, machine learning and sensing. It will also be of interest to professionals in data science, AI, cloud and IoT start-up companies, as well as developers and designers.

Über die Autorinnen und Autoren

Sunil Kumar is an associate professor of Computer Science and Engineering at Amity University, Noida campus, India. His research interests include computer networks, distributed systems, wireless sensor networks, SDN, and big data. He is industry CCNA & CCNP certified. He is a member of the IET, CSTA, IAER, IAENG. He holds a PhD in energy optimization in distributed wireless sensor networks from Amity University, Noida India.

Glenford Mapp is an associate professor at Middlesex University, London, UK. His primary expertise is in the development of new technologies for mobile and distributed systems such as service platforms, cloud computing, network addressing and transport protocols for local environments. He had previously worked for AT&T Cambridge Laboratories for ten years. He received his PhD in computer science from the University of Cambridge, UK.

Korhan Cengiz is an assistant professor of electrical and electronics engineering at Trakya University, Turkey. His research interests include computer networks, big data, wireless sensor networks, wireless communications, routing protocols, statistical signal processing, indoor positioning systems, power electronics and machine learning. He is an associate editor of Interdisciplinary Sciences: Computational Life Sciences, handling editor of Microprocessors and Microsystems, and associate editor of IET Electronics Letters, IET Networks, amongst others.