This 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 service and quality of experience (QoS/QoE) and to improve network performance.
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.
- Chapter 1: Introduction to intelligent network design driven by big data analytics, IoT, AI and cloud computing
- Chapter 2: Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks
- Chapter 3: An intelligent verification management approach for efficient VLSI computing system
- Chapter 4: Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques
- Chapter 5: Accurate management and progression of Big Data Analysis
- Chapter 6: Cram on data recovery and backup cloud computing techniques
- Chapter 7: An adaptive software-defined networking (SDN) for load balancing in cloud computing
- Chapter 8: Emerging security challenges in cloud computing: an insight
- Chapter 9: Factors responsible and phases of speaker recognition system
- Chapter 10: IoT-based water quality assessment using fuzzy logic controller
- Chapter 11: Design and analysis of wireless sensor network for intelligent transportation and industry automation
- Chapter 12: A review of edge computing in healthcare Internet of things: theories, practices and challenges
- Chapter 13: Image Processing for medical images on the basis of intelligence and biocomputing
- Chapter 14: IoT-based architecture for smart health-care systems
- Chapter 15: IoT-based heart disease prediction system
- Chapter 16: DIAIF: Detection of Interest Flooding using Artificial Intelligence-based Framework in NDN android
- Chapter 17: Intelligent and cost-effective mechanism for monitoring road quality using machine learning
- Chapter 18: Conclusion
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.
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