Energy Efficient Node Clustering in Sensor Networks - Adriola Faqolli
-20% with code BOOKS
Shipping in 12-18 days
30-day return policy
Nowadays devices with sensing ability have become a widely spread part of our life. They are useful for monitoring different phenomena and making processes of our life easier. These devices produce a large amount of data across wide sensor networks. Therefore, gathering and analysing data of sensor network has become an important issue. This process is challenging because sensor nodes that are part of these ... Full description
You May Also Like
TOP
AI for Good: How Real People Are Using Artificial Intelligence to Fix Things That Matter
-20% with code BOOKS
Out of stock
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-20% with code BOOKS
In stock at our supplier
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-20% with code BOOKS
In stock at our supplier
TOP
AI Engineering: Building Applications with Foundation Models
-20% with code BOOKS
In stock at our supplier
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-20% with code BOOKS
In stock at our supplier
TOP
The Art of Game Design: A Book of Lenses
-20% with code BOOKS
In stock at our supplier
TOP
How To Think About AI: A Guide For The Perplexed
-20% with code BOOKS
In stock at our supplier
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-20% with code BOOKS
In stock at our supplier
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-20% with code BOOKS
In stock at our supplier
TOP
HBR Guide to Generative AI for Managers
-20% with code BOOKS
In stock at our supplier
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-20% with code BOOKS
In stock at our supplier
TOP
The Web Application Hacker's Handbook: Discovering and Exploiting Security Flaws
-20% with code BOOKS
In stock at our supplier
TOP
How Linux Works: What Every Superuser Should Know
-20% with code BOOKS
In stock at our supplier
TOP
Artificial Intelligence: A Modern Approach, Global Edition
-20% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-20% with code BOOKS
In stock at our supplier
TOP
Speak Data: Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers
-20% with code BOOKS
In stock at our supplier
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-20% with code BOOKS
In stock at our supplier
TOP
Mathematics for Machine Learning
Cheng Soon Ong, A. Aldo Faisal, Marc Peter Deisenroth
-20% with code BOOKS
In stock at our supplier
Description
Nowadays devices with sensing ability have become a widely spread part of our life. They are useful for monitoring different phenomena and making processes of our life easier. These devices produce a large amount of data across wide sensor networks. Therefore, gathering and analysing data of sensor network has become an important issue. This process is challenging because sensor nodes that are part of these networks, have limited energy, computational and memory capacity. Limitation in energy especially affects sensor network lifetime. To face these challenges and to make this process more efficient, in this work it is proposed a new algorithm that introduces an energy efficient way for data acquisition in sensor networks. By using clustering techniques from data mining domain, it is suggested a self-organisation of sensor nodes into clusters. This is an important feature that makes this algorithm useful for most of sensor network applications, where nodes operate in an unsupervised way. Only a subset of nodes in the network have direct communication with distant central server. In this way, more energy is saved in communication and longer sensor network lifetime is provided.
More Information
| Author | Adriola Faqolli |
|---|---|
| Publisher | LAP LAMBERT Academic Publishing |
| Release year | 2017 |
| Cover type | Softcover |
| EAN | 9783330344303 |