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Abstract
Introduction
Result and Discussion
Conclusion and Future Direction
References
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Engineering
Control and Systems Engineering

Smart IoRT Networks AI-Powered Swarm Robotics

Profile image of Sri Harsha GrandhiSri Harsha Grandhi

2025, Publication Publication 100% 12 Sri_Harsha_Grandh 13 of 27 B282 Sri_Harsha_Grandh,Basava_Ramanjaneyulu_Gudivaka,Rajya_Lakshmi_Gudivaka,Raj_Kumar_Gudivaka,Dinesh_Kumar_Reddy_Basani F282 Journal of IoT in Social, Mobile, Analytics, and Cloud Journal of IoT in Social, Mobile, Analytics, an...

https://doi.org/10.36548/JISMAC.2024.4.004
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Abstract

The smart Internet of Robotic Things (IoRT) networks integrate swarm robotics, TimeSensitive Networking (TSN), Light Detection and Ranging (LiDAR) mapping, microcontrollers, and thermal imaging, producing adaptive and efficient ecosystems. The proposed study addresses a real-life problem across most industries, offering an autonomous, scalable, and optimized resource solution. It integrates swarm robotics for task coordination, TSN for low-latency communication, LiDAR for environmental mapping, and microcontrollers for energy efficiency along with thermal imaging capabilities for anomaly detection. Combining all these components a comprehensive AI architecture was developed to improve the performance in complex scenarios of IoRT. The research aims to develop scalable IoRT solutions that incorporate swarm robotics for coordination, TSN for deterministic communication, LiDAR for accurate mapping, energy-efficient microcontrollers for sustainability, and thermal imaging for anomaly detection in mission-critical tasks while being adaptable, reliable, and efficient. The proposed system attains 93% accuracy, 91% scalability, 94% F1 score, 95% efficiency, and 93% anomaly detection, which is beyond traditional methods. It addresses IoRT challenges with enhanced real-time adaptability, energy efficiency, and scalability to ensure optimal performance in applications like healthcare and disaster response. The integrated IoRT framework employs cutting-edge technologies and thus tackles dynamic challenges with precision and accuracy. Since it outperforms the traditional methods in different metrics, it establishes scalable, sustainable solutions for various real-time IoRT applications. The adaptability towards resources gets improved in more complex ecosystems.

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