Smart IoT-Driven Robotic Ecosystems: Merging AI, Automation, and Connectivity
Abbas, Najam (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025120131293
https://urn.fi/URN:NBN:fi:amk-2025120131293
Tiivistelmä
The essay is the conceptualization, development and analysis of a Smart IoT-Driven Robotic Ecosystem (SI- oTRE), an intelligent system which combines the Internet of Things (IoT), Artificial Intelligence (AI), and ro- botic automation. It aims at developing a safe, scaled, and autonoetic platform that can be used to carry out real-time decision-making in various fields like smart cities, precision agriculture, and monitoring the environment. The research is driven by the increased demands of smart, connected infrastructure that is more efficient in its operations, yet fosters cybersecurity and sustainability.
In the study, a modular IoT-robotic architecture, which enables a smooth process of communication, AI- perception, and autonomous control, is designed with the involvement of simulated Python environments. The study compares major communication protocols such as MQTT, LoRaWAN and 5G through a systematic analysis in determining their performance under varying latency, energy and scalability environments. The framework also incorporates machine learning algorithms, including the RAND Forest, Gradient Boosting, and the Support Vector Machines, to analyze traffic and identify anomalies to control an adaptive robot. A set of these models is tested to evaluate accuracy, precision, recall, and latency, providing the understand- ing of the computational efficiency of AI-driven IoT systems.
Simulation experiments are designed to provide the simulation of real-world environments where the IoT devices share sensory data and react to contextual variations. The findings indicate that secure and smart communication is highly effective in boosting the reliability of the data, minimizing the latency, and boost- ing the overall system responsiveness. Security testing conditions, including cyberattack sims, risk assess- ment, and resilience analysis are also discussed in the thesis to ensure that the SIoTRE framework soothes the sustainability of the framework.
This project combines both theoretical innovation and practice of the development of the system by syn- thesizing the findings of current studies and using practical simulations. The results highlight the im- portance of how AI-based IoT eco-systems should evolve the future automation by facilitating sustainable, adaptive and intelligent robotic systems. SIoTRE framework provides a structured basis to further studies in Industry 4.0 which offers a lineage towards federated, secure and energy efficient smart environments that could work independently with little human intervention.
In the study, a modular IoT-robotic architecture, which enables a smooth process of communication, AI- perception, and autonomous control, is designed with the involvement of simulated Python environments. The study compares major communication protocols such as MQTT, LoRaWAN and 5G through a systematic analysis in determining their performance under varying latency, energy and scalability environments. The framework also incorporates machine learning algorithms, including the RAND Forest, Gradient Boosting, and the Support Vector Machines, to analyze traffic and identify anomalies to control an adaptive robot. A set of these models is tested to evaluate accuracy, precision, recall, and latency, providing the understand- ing of the computational efficiency of AI-driven IoT systems.
Simulation experiments are designed to provide the simulation of real-world environments where the IoT devices share sensory data and react to contextual variations. The findings indicate that secure and smart communication is highly effective in boosting the reliability of the data, minimizing the latency, and boost- ing the overall system responsiveness. Security testing conditions, including cyberattack sims, risk assess- ment, and resilience analysis are also discussed in the thesis to ensure that the SIoTRE framework soothes the sustainability of the framework.
This project combines both theoretical innovation and practice of the development of the system by syn- thesizing the findings of current studies and using practical simulations. The results highlight the im- portance of how AI-based IoT eco-systems should evolve the future automation by facilitating sustainable, adaptive and intelligent robotic systems. SIoTRE framework provides a structured basis to further studies in Industry 4.0 which offers a lineage towards federated, secure and energy efficient smart environments that could work independently with little human intervention.