Machine learning for drone-enabled IoT networks: opportunities, developments, and trends

Contributor(s):

Material type: Text

TextSeries: Advances in Science, Technology & Innovation (ASTI)Publication details: Cham Springer 2025Description: ix, 207 pISBN:

9783031809606

Subject(s):

DDC classification:

006.31 HAS

Summary: This book aims to explore the latest developments, challenges, and opportunities in the application of machine learning techniques to enhance the performance and efficiency of IoT networks assisted by aerial unmanned vehicles (UAVs), commonly known as drones. The book aims to include cutting edge research and development on a number of areas within the topic including but not limited to: •Machine learning algorithms for drone-enabled IoT networks •Sensing and data collection with drones for IoT applications •Data analysis and processing for IoT networks assisted by drones •Energy-efficient and scalable solutions for drone-assisted IoT networks •Security and privacy issues in drone-enabled IoT networks •Emerging trends and future directions in ML for drone-assisted IoT networks (https://link.springer.com/book/10.1007/978-3-031-80961-3)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 HAS (Browse shelf(Opens below))	1	Available		009083

Table of contents:
Front Matter
Pages i-ix
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Unmanned Aerial Vehicles Enabled IoT Platform for Effective Disaster Management
R. Anjana Devi, V. Harshini Amutha, Priya Thiagarajan
Pages 1-17
Exploring Machine and Deep Learning for Crop Detection and Yield Prediction in Drone-Enabled IoT Networks
Youness Hnida, Mohamed Adnane Mahraz, Ali Yahyaouy, Ali Achebour, Jamal Riffi, Hamid Tairi
Pages 19-38
Drone Swarm Coordination Using Machine Learning in IoT Networks
Divyesh Divakar, Kanmani, A. V. Supriya
Pages 39-64
Security and Privacy Issues in Drone-Enabled IoT Networks
Roopashree Nayak, Jennifer C. Saldanha, Sumiksha Shetty, A. B. Smitha, Ragesh Raju
Pages 65-80
Enhancing Capabilities and Security Features of Drone Networks Through Machine Learning: A Comprehensive Overview
Dipta Gomes, Manzurul Hasan, Supta Richard Philip
Pages 81-105
Unlocking the Potential of Machine Learning for Enhanced Security in Drone-Enabled IoT Networks
M. S. Bhargavi, P. Hemavathi, Chowdam Jaganath, Kilari Narendra
Pages 107-120
Machine Learning Models for Drone Security: Cognitive Versus Cyber Intelligence for Safety Operations
Simeon Okechukwu Ajakwe, Christianah Amaka Okoloegbo, Jae Min Lee, Dong Seong Kim
Pages 121-139
Cost–Benefit Analysis of Sensing and Data Collection with Drones for IoT Applications
Harshit Mishra, Divyanshi Mishra, Ankit Kumar Tiwari, Deep Chand Nishad
Pages 141-168
Economic Analysis of AI Integration in Internet of Drones (IoD)
Harshit Mishra, Shripati Dwivedi
Pages 169-190
Emerging Trends and Future Directions in ML for Drone-Assisted IoT Networks
M. Praveen Kumar, A. B. Abhishek, S. Pavanalaxmi, T. Kripa, Roopashree Nayak
Pages 191-207

[https://link.springer.com/book/10.1007/978-3-031-80961-3]

This book aims to explore the latest developments, challenges, and opportunities in the application of machine learning techniques to enhance the performance and efficiency of IoT networks assisted by aerial unmanned vehicles (UAVs), commonly known as drones. The book aims to include cutting edge research and development on a number of areas within the topic including but not limited to: •Machine learning algorithms for drone-enabled IoT networks •Sensing and data collection with drones for IoT applications •Data analysis and processing for IoT networks assisted by drones •Energy-efficient and scalable solutions for drone-assisted IoT networks •Security and privacy issues

in drone-enabled IoT networks •Emerging trends and future directions in ML for drone-assisted IoT networks

(https://link.springer.com/book/10.1007/978-3-031-80961-3)

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