Wireless Sensor Networks And Ecological Monitoring
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Wireless Sensor Networks and Ecological Monitoring
Author: Subhas C Mukhopadhyay
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-02-11
This book presents the state of the art technologies and solutions to tackle the critical challenges faced by the building and development of the WSN and ecological monitoring system but also potential impact on society at social, medical and technological level. This book is dedicated to Sensing systems for Sensors, Wireless Sensor Networks and Ecological Monitoring. The book aims at Master and PhD degree students, researchers, practitioners, especially WSN engineers involved with ecological monitoring. The book will provide an opportunity of a dedicated and a deep approach in order to improve their knowledge in this specific field.
Machine Learning for Environmental Monitoring in Wireless Sensor Networks
Today, data fuels everything we do in a highly connected world. However, traditional environmental monitoring methods often fail to provide timely and accurate data for effective decision-making in today's rapidly changing ecosystems. The reliance on manual data collection and outdated technologies results in gaps in data coverage, making it challenging to detect and respond to environmental changes in real time. Additionally, integration between monitoring systems and advanced data analysis tools is necessary to derive actionable insights from collected data. As a result, environmental managers and policymakers face significant challenges in effectively monitoring, managing, and conserving natural resources in a rapidly evolving environment. Machine Learning for Environmental Monitoring in Wireless Sensor Networks offers a comprehensive solution to the limitations of traditional environmental monitoring methods. By harnessing the power of Wireless Sensor Networks (WSNs) and advanced machine learning algorithms, this book presents a novel approach to ecological monitoring that enables real-time, high-resolution data collection and analysis. By integrating WSNs and machine learning, environmental stakeholders can gain deeper insights into complex ecological processes, allowing for more informed decision-making and proactive management of natural resources.
Introduction to Wireless Sensor Networks
Explores real-world wireless sensor network development, deployment, and applications Presents state-of-the-art protocols and algorithms Includes end-of-chapter summaries, exercises, and references For students, there are hardware overviews, reading links, programming examples, and tests available at [website] For Instructors, there are PowerPoint slides and solutions available at [website]