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Zhou, Z., Zhand, C., Wang, J., Gu, B., Mumtaz, S., Rodriguez, J. and Zhao, X. (2019) Energy-Efficient Resource Allocation for Energy Harvesting-Based Cognitive Machine-to-Machine Communications. IEEE Transactions on Cognitive Communications and Networking, 5, 595-607.
https://doi.org/10.1109/TCCN.2019.2925025

has been cited by the following article:

• TITLE: Machine-to-Machine Collaboration Utilizing Internet of Things and Machine Learning

  AUTHORS: Mohammed Misbahuddin, Abul Kashem Mohammed Azad, Veysel Demir

  KEYWORDS: Search and Rescue, Image Processing, Navigation Systems, Autonomous Systems, and Object Detection

  JOURNAL NAME: Advances in Internet of Things, Vol.13 No.4, October 30, 2023

  ABSTRACT: Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.