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Updated: Sep 8, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Smart deployment of IoT-TelosB service care StreamRobot using software-defined reliability optimisation design.

Kennedy Chinedu Okafor1,2, Omowunmi Mary Longe2

  • 1Mechatronics Engineering, Federal University of Technology-Owerri, Nigeria.

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|June 16, 2022
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Summary

This study introduces StreamRobot for reliable predictive analytics in intelligent service care robots, addressing Internet of Things (IoT) vulnerabilities and node failures. The system enhances data transmission and monitoring for critical applications.

Keywords:
Cloud networksFog analyticsInternet of thingsNeural networkSoftware-defined networkStreamRobots

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Area of Science:

  • Robotics and Intelligent Systems
  • Computer Networking and Communications
  • Data Science and Machine Learning

Background:

  • Intelligent service care robots are vital in critical sectors but face challenges like IoT vulnerabilities, node failures, and latency.
  • Existing systems struggle with reliable data transmission and predictive analytics in complex IoT environments.
  • Optimizing data transmission and ensuring system reliability are crucial for the effective deployment of these robots.

Purpose of the Study:

  • To present StreamRobot, a system for reliable predictive analytics and optimized data transmission in intelligent service care robots.
  • To address major drawbacks of IoT platforms, including vulnerabilities, node failures, and computational latency.
  • To enhance the reliability and intelligence of data monitoring and prediction processes in IoT-enabled robotic systems.

Main Methods:

  • Implementation of a software-defined reliable optimization design within the system architecture.
  • Formulation of an edge system model focusing on log-normality distribution, reliability, and stability.
  • Deployment of an OpenFlow-SDN construct for edge-to-fog traffic offloading and utilization of fog detection-to-cloud predictive machine learning (FD-CPML) for real-time stream prediction.
  • Application of two-phase link-state protocols (off-taker and on-demand) and an orphan reconnection trigger mechanism for resilient data transmission.

Main Results:

  • The proposed FD-CPML model demonstrated superior prediction accuracy compared to decision tree and logistic regression, with data stream latency at 26.67%.
  • The system achieved satisfactory linear predictive scalability on the network plane, with results of 36.15%.
  • Reliable communication and intelligent monitoring of node failures were validated through experimental testbeds using TelosB IoT nodes and neural constrained SDN intelligence.

Conclusions:

  • StreamRobot offers a reliable solution for predictive analytics and data transmission in intelligent service care robots.
  • The integration of SDN and edge/fog computing enhances the robustness and efficiency of IoT-enabled robotic systems.
  • The findings support the effective deployment of intelligent robots in mission-critical applications through improved reliability and monitoring capabilities.