Torrent details for "Sinha A. Metaheuristics and Reinforcement Techniques for Smart Sensor Apps 2025 [andryold1]"    Log in to bookmark

wide
Torrent details
Cover
Download
Torrent rating (0 rated)
Controls:
Category:
Language:
English English
Total Size:
8.43 MB
Info Hash:
0dd47c48ccc581e6c51e74d22794f4d1ca654358
Added By:
Added:  
08-09-2024 14:35
Views:
58
Health:
Seeds:
25
Leechers:
2
Completed:
186
wide




Description
wide
Externally indexed torrent
If you are the original uploader, contact staff to have it moved to your account
Textbook in PDF format

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

  User comments    Sort newest first

No comments have been posted yet.



Post anonymous comment
  • Comments need intelligible text (not only emojis or meaningless drivel).
  • No upload requests, visit the forum or message the uploader for this.
  • Use common sense and try to stay on topic.

  • :) :( :D :P :-) B) 8o :? 8) ;) :-* :-( :| O:-D Party Pirates Yuk Facepalm :-@ :o) Pacman Shit Alien eyes Ass Warn Help Bad Love Joystick Boom Eggplant Floppy TV Ghost Note Msg


    CAPTCHA Image 

    Anonymous comments have a moderation delay and show up after 15 minutes