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Machine learning for environmental monitoring in wireless sensor networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for environmental monitoring in wireless sensor networks/ Parikshit N. Mahalle, Dattatray G. Takale, Sachin Sakhare, Ganesh B. Regulwar, editors.
other author:	Mahalle, Parikshit N.
Published:	Hershey, Pennsylvania :IGI Global, : 2025.,
Description:	1 online resource (xxiv, 471 p.) :ill.
[NT 15003449]:	Preface -- Acknowledgment -- Chapter 1. Fundamentals of Wireless Sensor Network -- Chapter 2. Fundamentals of Wireless Sensor Networks: Wireless Sensor Networks Techniques -- Chapter 3. Data Collection and Preprocessing for Environmental Monitoring Using Wireless Sensor Networks -- Chapter 4. Ethical and Security Measures for Environmental Data Collection -- Chapter 5. Edge Computing: A Guide to Achieving Sustainable Living -- Chapter 6. Environmental Impact Assessment Applied With SMS Technology for Wastewater Treatment -- Chapter 7. Real-Time Soil Moisture Monitoring Using Wireless Sensor Networks for Precision Agriculture -- Chapter 8. Analyzing Various Grape Diseases and Detection Techniques by Monitoring Environmental Parameters Through IoT and Machine Learning Approaches -- Chapter 9. An Intelligent Alarm System to Detect and Control Railroad Crossings Using Wireless Sensor Networks: RailAlarm -- Chapter 10. Smart Solutions for Climate Resilience Harnessing Machine Learning and Sustainable WSNs -- Chapter 11. Overview of Fault Diagnosis in Wireless Sensor Network -- Chapter 12. Centralized and Distributed Approach: A Comparative Analysis of Fault Diagnosis Approaches in Wireless Sensor Networks -- Chapter 13. Cluster Head Selection Based on ACO in Vehicular Ad-Hoc Networks -- Chapter 14. Energy Optimization of Routing Protocol in Wireless Sensor Network -- Chapter 15. Significance of Blockchain Technology in Industrial Applications With an Emphasis on Security Considerations -- Chapter 16. Integration of IoT and Quantum Computing: Revolutionizing Manufacturing -- Chapter 17. Study on Application of Artificial Intelligence and Machine Learning in the Banking Sector for Fraud Detection and Prevention -- Chapter 18. Predicting Analytics for Dynamic Mobility Patterns in Mobile Wireless Networks Using Cutting-Edge Method -- Compilation of References -- About the Contributors -- Index.
Subject:	Wireless sensor networks. -
Online resource:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-3940-4
ISBN:	9798369339411

Machine learning for environmental monitoring in wireless sensor networks
Machine learning for environmental monitoring in wireless sensor networks[electronic resource] /Parikshit N. Mahalle, Dattatray G. Takale, Sachin Sakhare, Ganesh B. Regulwar, editors. - Hershey, Pennsylvania :IGI Global,2025. - 1 online resource (xxiv, 471 p.) :ill. - Advances in computer and electrical engineering (ACEE) book series. - Advances in computer and electrical engineering (ACEE) book series..

Includes bibliographical references and index.

Preface -- Acknowledgment -- Chapter 1. Fundamentals of Wireless Sensor Network -- Chapter 2. Fundamentals of Wireless Sensor Networks: Wireless Sensor Networks Techniques -- Chapter 3. Data Collection and Preprocessing for Environmental Monitoring Using Wireless Sensor Networks -- Chapter 4. Ethical and Security Measures for Environmental Data Collection -- Chapter 5. Edge Computing: A Guide to Achieving Sustainable Living -- Chapter 6. Environmental Impact Assessment Applied With SMS Technology for Wastewater Treatment -- Chapter 7. Real-Time Soil Moisture Monitoring Using Wireless Sensor Networks for Precision Agriculture -- Chapter 8. Analyzing Various Grape Diseases and Detection Techniques by Monitoring Environmental Parameters Through IoT and Machine Learning Approaches -- Chapter 9. An Intelligent Alarm System to Detect and Control Railroad Crossings Using Wireless Sensor Networks: RailAlarm -- Chapter 10. Smart Solutions for Climate Resilience Harnessing Machine Learning and Sustainable WSNs -- Chapter 11. Overview of Fault Diagnosis in Wireless Sensor Network -- Chapter 12. Centralized and Distributed Approach: A Comparative Analysis of Fault Diagnosis Approaches in Wireless Sensor Networks -- Chapter 13. Cluster Head Selection Based on ACO in Vehicular Ad-Hoc Networks -- Chapter 14. Energy Optimization of Routing Protocol in Wireless Sensor Network -- Chapter 15. Significance of Blockchain Technology in Industrial Applications With an Emphasis on Security Considerations -- Chapter 16. Integration of IoT and Quantum Computing: Revolutionizing Manufacturing -- Chapter 17. Study on Application of Artificial Intelligence and Machine Learning in the Banking Sector for Fraud Detection and Prevention -- Chapter 18. Predicting Analytics for Dynamic Mobility Patterns in Mobile Wireless Networks Using Cutting-Edge Method -- Compilation of References -- About the Contributors -- Index.

"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."--

Mode of access: World Wide Web.

ISBN: 9798369339411Subjects--Topical Terms:

1086182
Wireless sensor networks.
Subjects--Index Terms:

Agriculture.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: TK7872.D48 / M33 2024eb

Dewey Class. No.: 681.2

Machine learning for environmental monitoring in wireless sensor networks
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245 0 0 $a Machine learning for environmental monitoring in wireless sensor networks $h [electronic resource] / $c Parikshit N. Mahalle, Dattatray G. Takale, Sachin Sakhare, Ganesh B. Regulwar, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2025.
300 $a 1 online resource (xxiv, 471 p.) : $b ill.
490 1 $a Advances in computer and electrical engineering (ACEE) book series
504 $a Includes bibliographical references and index.
505 0 $a Preface -- Acknowledgment -- Chapter 1. Fundamentals of Wireless Sensor Network -- Chapter 2. Fundamentals of Wireless Sensor Networks: Wireless Sensor Networks Techniques -- Chapter 3. Data Collection and Preprocessing for Environmental Monitoring Using Wireless Sensor Networks -- Chapter 4. Ethical and Security Measures for Environmental Data Collection -- Chapter 5. Edge Computing: A Guide to Achieving Sustainable Living -- Chapter 6. Environmental Impact Assessment Applied With SMS Technology for Wastewater Treatment -- Chapter 7. Real-Time Soil Moisture Monitoring Using Wireless Sensor Networks for Precision Agriculture -- Chapter 8. Analyzing Various Grape Diseases and Detection Techniques by Monitoring Environmental Parameters Through IoT and Machine Learning Approaches -- Chapter 9. An Intelligent Alarm System to Detect and Control Railroad Crossings Using Wireless Sensor Networks: RailAlarm -- Chapter 10. Smart Solutions for Climate Resilience Harnessing Machine Learning and Sustainable WSNs -- Chapter 11. Overview of Fault Diagnosis in Wireless Sensor Network -- Chapter 12. Centralized and Distributed Approach: A Comparative Analysis of Fault Diagnosis Approaches in Wireless Sensor Networks -- Chapter 13. Cluster Head Selection Based on ACO in Vehicular Ad-Hoc Networks -- Chapter 14. Energy Optimization of Routing Protocol in Wireless Sensor Network -- Chapter 15. Significance of Blockchain Technology in Industrial Applications With an Emphasis on Security Considerations -- Chapter 16. Integration of IoT and Quantum Computing: Revolutionizing Manufacturing -- Chapter 17. Study on Application of Artificial Intelligence and Machine Learning in the Banking Sector for Fraud Detection and Prevention -- Chapter 18. Predicting Analytics for Dynamic Mobility Patterns in Mobile Wireless Networks Using Cutting-Edge Method -- Compilation of References -- About the Contributors -- Index.
520 3 $a "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."-- $c Provided by publisher.
538 $a Mode of access: World Wide Web.
650 0 $a Wireless sensor networks. $3 1086182
650 0 $a Wireless sensor nodes. $3 3512381
650 0 $a Machine learning. $3 533906
653 $a Agriculture.
653 $a Air Quality.
653 $a Biodiversity.
653 $a Climate Change.
653 $a Data Collection Methods and Tools.
653 $a Energy Efficiency.
653 $a Environmental Monitoring.
653 $a Machine Learning.
653 $a Remote Sensing.
653 $a Security.
653 $a Sensor Networks.
653 $a Sustainability.
653 $a Water Resources.
653 $a Wildlife Monitoring.
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Mahalle, Parikshit N. $3 3379506
700 1 $a Regulwar, Ganesh B. $3 3793518
700 1 $a Sakhare, Sachin. $3 3793519
700 1 $a Takale, Dattatray G. $3 3793520
710 2 $a IGI Global. $3 1361470
776 0 8 $i Print version: $z 9798369339404
830 0 $a Advances in computer and electrical engineering (ACEE) book series. $3 3793521
856 4 0 $u https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-3940-4