Manisha Bhende – författare

With a specific focus on energy efficiency, Optimizing IoT Networks examines the application of machine learning to enhance resource allocations in IoT networks.It discusses various algorithms, including neural networks and reinforcement learning, to optimise resource use and improve network performance. It addresses challenges such as the dynamic behaviour of IoT devices and the need for real-time decision-making. It discusses optimisation methods used alongside machine learning to enhance resource allocation efficiency.

• Provides a foundational understanding of IoT network architecture and the importance of efficient resource allocation

• Discusses complexities in resource allocation due to dynamic device behaviour and varying data traffic patterns

• Covers key machine learning concepts and algorithms relevant to optimising resource allocation in IoT networks

• Emphasises the significance of energy efficiency in IoT networks and its impact on resource allocation strategies

• Explores algorithms such as clustering, regression, and reinforcement learning for effective resource allocation

The book is designed for researchers, practitioners, and scholars in computer science and technology who are interested in or actively working on optimising IoT networks.

With a specific focus on energy efficiency, Optimizing IoT Networks examines the application of machine learning to enhance resource allocations in IoT networks.It discusses various algorithms, including neural networks and reinforcement learning, to optimise resource use and improve network performance. It addresses challenges such as the dynamic behaviour of IoT devices and the need for real-time decision-making. It discusses optimisation methods used alongside machine learning to enhance resource allocation efficiency.

• Provides a foundational understanding of IoT network architecture and the importance of efficient resource allocation

• Discusses complexities in resource allocation due to dynamic device behaviour and varying data traffic patterns

• Covers key machine learning concepts and algorithms relevant to optimising resource allocation in IoT networks

• Emphasises the significance of energy efficiency in IoT networks and its impact on resource allocation strategies

• Explores algorithms such as clustering, regression, and reinforcement learning for effective resource allocation

The book is designed for researchers, practitioners, and scholars in computer science and technology who are interested in or actively working on optimising IoT networks.

Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers.

Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems.

Audience

Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.