We cordially invite you to contribute a book chapter for our edited book
entitled Machine Learning (ML) for Drone-Enabled IoT Networks:
Opportunities, Developments, and Trends, which will be published by
Springer Nature publishers in the Advances in Science, Technology &
Innovation series (Scopus indexed). There is no publication fee.
This edited 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 several 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
powered by machine learning.
· Drone Networks through Collaborative Distributed Machine Learning
for Enhanced Model Inference and Learning Across Edge Devices
· Data analysis and On-board 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
· Smart Cybersecurity Frameworks for IoT Drones
· Machine Learning Models for Drone Security
· AI Integration in Internet of Drones (IoD)
· Machine learning on the edge for drone-enabled IoT networks
· Lightweight ML Models for drone-enabled IoT Networks
· Optimized IoT-Edge Computing Architectures for Drone Networks
· Drone Swarm Coordination Using Machine Learning in IoT Networks
*Submission instructions:*
Researchers and practitioners are invited to submit full chapters by *15
June 2024*. Submitted chapters will be reviewed for a final decision. Authors
may be asked to serve as reviewers for this book, reviewing chapters
submitted by other authors.
Authors are invited to prepare their chapters following the chapter
preparation guidelines, which are available on the submission page as an
attachment. Please submit full chapters via EasyChair using the following
link: https://easychair.org/conferences/?conf=ml-drone-iot-01
Queries can be sent to the following email address:
ml-drone-iot-01@easychair.org
Note: There are no submission or acceptance fees for manuscripts submitted
to this book publication. All manuscripts are accepted based on peer review
outcomes.
*Important Dates*
*Full Chapter submission: 15 June 2024 *
Decision notification: 30 July 2024
Revised submission: 30 August 2024
Camera ready submission: 30 September 2024
Editors:
· Dr Jahan Hassan, Central Queensland University, Australia
· Dr Sara Khalifa, Queensland University of Technology, Australia
· Dr Prasant Misra, Tata Consultancy Services – Research, India
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