SMART AND SUSTAINABLE APPROACHES FOR OPTIMIZING PERFORMANCE OF WIRELESS NETWORK Explores the intersection of sustainable growth, green computing and automation, and performance optimization of 5G wireless networks
Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks explores how wireless sensing applications, green computing, and Big Data analytics can increase the energy efficiency and environmental sustainability of real-time applications across areas such as healthcare, agriculture, construction, and manufacturing.
Bringing together an international team of expert contributors, this authoritative volume highlights the limitations of conventional technologies and provides methodologies and approaches for addressing Quality of Service (QOS) issues and optimizing network performance. In-depth chapters cover topics including blockchain-assisted secure data sharing, smart 5G Internet of Things (IoT) scenarios, intelligent management of ad hoc networks, and the use of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques in smart healthcare, smart manufacturing, and smart agriculture.
- Covers design, implementation, optimization, and sustainability of wireless and sensor-based networks
- Discusses concepts of sustainability and green computing as well as their relevance to society and the environment
- Addresses green automation applications in various disciplines such as computer science, nanoscience, information technology (IT), and biochemistry
- Explores various smart and sustainable approaches for current wireless and sensor-based networks
- Includes detailed case studies of current methodologies, applications, and implementations
Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real-time Applications is an essential resource for academic researchers and industry professionals working to integrate sustainable development and Information and Communications Technology (ICT).
Sherin Zafar, PhD, Assistant Professor, Department of Computer Science, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India.
Mohd Abdul Ahad, PhD, Assistant Professor, Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India.
Syed Imran Ali, PhD, Lecturer, University of Technology and Applied Sciences, Al Musannah Sultanate of Oman.
Deepa Mehta, PhD, Senior Data Scientist, Great Learning.
M. Afshar Alam, PhD, Vice Chancellor Jamia Hamdard, New Delhi, India.
