We cordially invite you to contribute a book chapter for our edited book entitled "Machine Learning 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.

Submission instructions:

Researchers and practitioners are invited to submit on or before 15 April 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of their proposed chapter. Authors will be notified by 21 April 2024 about the status of their proposals and sent full chapter preparation guidelines. Full chapters are expected to be submitted 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 submit chapter proposal and full chapters via EasyChair at the given link: https://easychair.org/conferences/?conf=ml-drone-iot-01

List of Topics

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

Deadlines

Chapter Proposal Submission: 15 April 2024

Chapter Proposal Decision notification: 21 April 2024

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 

Queries about submissions should be emailed to j.hassan@cqu.edu.au; sara.khalifa@qut.edu.au; prasant.misra@gmail.com