Название: Metaheuristics and Reinforcement Techniques for Smart Sensor Applications
Автор: Adwitiya Sinha, Manju, Samayveer Singh
Издательство: CRC Press
Год: 2025
Страниц: 253
Язык: английский
Формат: pdf (true), epub
Размер: 14.5 MB

This book discusses the fundamentals of wireless sensor networks,and the prevailing method and trends of smart sensor applications. It presents analytical modelling to foster the understanding of network challenges in developing protocols for next-generation communication standards.

Metaheuristic algorithms are optimization techniques that draw inspiration from natural and abstract concepts to solve complex problems. Unlike exact algorithms, which aim for optimal solutions, metaheuristics prioritize speed and adaptability, making them suitable for addressing computationally challenging problems with large solution spaces. These algorithms play a vital role in various fields, including combinatorial optimization, Machine Learning, and operations research. In the realm of WSNs, metaheuristic algorithms are instrumental in optimizing routing protocols. WSNs comprise nodes with limited computational resources, energy constraints, and often operate in dynamic environments. Efficient data routing in WSNs is critical for conserving energy, extending network lifetime, and ensuring reliable data delivery.

Genetic Algorithms (GA) for CH selection play a pivotal role in the efficiency and performance of wireless sensor networks. The GA algorithm employs evolutionary principles to strategically choose CHs that are responsible for efficient and reliable data transmission in the network. An effective utilization of the GA for the selection process of CHs helps extend the network’s lifespan, enhance energy efficiency, and improve data transmission reliability.

• Presents an overview of the low-power sensor, network standards, design challenges and sensor network simulation
• Focusses on clustering, methods available for wireless sensor networks to tackle energy hole problems, load balancing and network lifetime enhancements
• Discusses enhanced versions of energy models enriched with energy harvesting
• Provides an insight into coverage and connectivity issues with genetic meta-heuristics, evolutionary models and reinforcement methodologies designed for wireless sensor networks
• Includes a wide range of sensor network applications and their integration with social networks and neural computing.

The reference book is for researchers and scholars interested in Smart Sensor applications.




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