Machine-to-Machine Collaboration Utilizing Internet of Things and Machine Learning  

作　　者：Mohammed Misbahuddin Abul Kashem Mohammed Azad Veysel Demir College Mohammed Misbahuddin;Abul Kashem Mohammed Azad;Veysel Demir College(College of Engineering and Engineering Technology, Northern Illinois University, DeKalb, USA)

机构地区：[1]College of Engineering and Engineering Technology, Northern Illinois University, DeKalb, USA

出　　处：《Advances in Internet of Things》2023年第4期144-169,共26页物联网（英文）

摘　　要：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.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.

关 键 词：Search and Rescue Image Processing Navigation Systems Autonomous Systems and Object Detection 

分 类 号：TN9[电子电信—信息与通信工程]