Computational Intelligence in Wired and Wireless Networks
1st Edition
Will be published approx. on 13. October 2026
Book
Hardback
248 pages
978-1-032-93152-4 (ISBN)
Description
The text examines how computational intelligence can address the complex challenges and opportunities that arise in both wired and wireless networks. It discusses topics including fault tolerance and self-healing mechanisms, edge and fog computing, machine learning in wireless networks, quality of experience, and integration of emerging technologies.
This book:
Introduces theoretical concepts, and demonstrates how computational intelligence techniques can be applied to solve real-world problems in network optimization, security, quality of experience, and resource allocation.
Discusses emerging technologies such as 5G and beyond, edge computing, and machine learning in the context of wired and wireless networks.
Presents the integration of machine learning concepts, including deep learning and reinforcement learning, in the context of wireless networks.
Highlights the use of computational intelligence techniques in enhancing network optimization by providing adaptive, self-learning, and efficient solutions.
Covers design, management, and optimization of networks using machine learning, deep learning, and reinforcement learning.
It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer science and engineering, telecommunications, and information technology.
This book:
Introduces theoretical concepts, and demonstrates how computational intelligence techniques can be applied to solve real-world problems in network optimization, security, quality of experience, and resource allocation.
Discusses emerging technologies such as 5G and beyond, edge computing, and machine learning in the context of wired and wireless networks.
Presents the integration of machine learning concepts, including deep learning and reinforcement learning, in the context of wireless networks.
Highlights the use of computational intelligence techniques in enhancing network optimization by providing adaptive, self-learning, and efficient solutions.
Covers design, management, and optimization of networks using machine learning, deep learning, and reinforcement learning.
It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer science and engineering, telecommunications, and information technology.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, and Undergraduate Advanced
Illustrations
16 s/w Photographien bzw. Rasterbilder, 43 s/w Zeichnungen, 14 s/w Tabellen, 59 s/w Abbildungen
14 Tables, black and white; 43 Line drawings, black and white; 16 Halftones, black and white; 59 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-032-93152-4 (9781032931524)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ankit Gambhir | Neha Jain | Gaurav Aggarwal
Computational Intelligence in Wired and Wireless Networks
E-Book
approx. 09/2026
Taylor & Francis
€73.99
Not yet available
Ankit Gambhir | Neha Jain | Gaurav Aggarwal
Computational Intelligence in Wired and Wireless Networks
E-Book
approx. 09/2026
Taylor & Francis
€73.99
Not yet available
Persons
Ankit Gambhir is serving as Dean (Academics) at Trinity Institute of Innovation in Professional Studies, Greater Noida, affiliated with Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India. He holds a Ph.D. from GGSIPU, New Delhi, and an M.Tech. (Gold Medalist) in Communication Engineering along with a B.Tech. in Electronics and Communication Engineering. His research interests include optimization and computational intelligence techniques applied to wireless sensor networks, information security, and nature-inspired optimization algorithms. Dr. Gambhir has published several research papers in international journals indexed in Web of Science and Scopus and has presented his work at national and international conferences.
Neha Jain is an Associate Professor at IILM University, Greater Noida, India. She holds a Ph.D. from Guru Gobind Singh Indraprastha University, New Delhi, an M.Tech. degree from Indira Gandhi Institute of Technology, Delhi, and a B.Tech. degree from the University of Rajasthan. With more than fourteen years of teaching experience, her research interests include computer networks, wireless sensor networks, cloud computing, artificial intelligence, and the Internet of Things. She has published patents and research articles in SCI-indexed journals and has contributed to national and international conferences as an author, reviewer, and session chair. Dr. Jain has also served as an editor for journals and conference proceedings and is actively involved in academic capacity-building initiatives, including faculty development programmes and workshops. She is a Mentor of Change with the Atal Innovation Mission (AIM), NITI Aayog, Government of India.
Gaurav Aggarwal is an Assistant Professor in the Department of Information Technology and Engineering at Amity University, Tashkent, Uzbekistan. He received his B.Tech. degree in Instrumentation from Kurukshetra University, India, in 2006, followed by an M.Tech. degree in Computer Science and Engineering from Kurukshetra University in 2008. He completed his Ph.D. in 2019 from The NorthCap University, Gurugram, India, with a focus on speech signal processing and machine learning. His research interests include signal processing, machine learning, and cognitive sciences. Dr. Aggarwal has published over thirty research papers in SCI- and Scopus-indexed journals and conferences and has more than fourteen years of experience in teaching and research.
Neha Jain is an Associate Professor at IILM University, Greater Noida, India. She holds a Ph.D. from Guru Gobind Singh Indraprastha University, New Delhi, an M.Tech. degree from Indira Gandhi Institute of Technology, Delhi, and a B.Tech. degree from the University of Rajasthan. With more than fourteen years of teaching experience, her research interests include computer networks, wireless sensor networks, cloud computing, artificial intelligence, and the Internet of Things. She has published patents and research articles in SCI-indexed journals and has contributed to national and international conferences as an author, reviewer, and session chair. Dr. Jain has also served as an editor for journals and conference proceedings and is actively involved in academic capacity-building initiatives, including faculty development programmes and workshops. She is a Mentor of Change with the Atal Innovation Mission (AIM), NITI Aayog, Government of India.
Gaurav Aggarwal is an Assistant Professor in the Department of Information Technology and Engineering at Amity University, Tashkent, Uzbekistan. He received his B.Tech. degree in Instrumentation from Kurukshetra University, India, in 2006, followed by an M.Tech. degree in Computer Science and Engineering from Kurukshetra University in 2008. He completed his Ph.D. in 2019 from The NorthCap University, Gurugram, India, with a focus on speech signal processing and machine learning. His research interests include signal processing, machine learning, and cognitive sciences. Dr. Aggarwal has published over thirty research papers in SCI- and Scopus-indexed journals and conferences and has more than fourteen years of experience in teaching and research.
Content
1. Computational Intelligence in Wired and Wireless Networks: Enhancing MQTT Scalability in IoT-Based e-Healthcare Systems. 2. Cutting-edge Computational Intelligence Techniques in Security and Anomaly Detection. 3. Computer Simulated Behaviour to Improve Real-Time Quality of Experience (QoE). 4. Augmenting Edge Intelligence Networks with MADRL: Versatile solution to Load Balancing and Resource Optimization. 5. Design, Management and Optimization of Networks Using Machine Learning, Deep Learning and Reinforcement Learning. 6. Reinforcement Learning Based Optimized Search for Extraterrestrial Intelligence (SETI). 7. Bringing IoT and Healthcare in Networks: Internet of Medical Things (IoMT). 8. Design and Management of Adaptive Treatment Strategies in Healthcare using Reinforcement Learning. 9. Real-time Dehazing Technique using Improved Dark Channel Prior and Gaussian Pyramid Operator. 10. Computational Intelligence to Enhance Real-Time Quality Global Health Security.