Calculate, predict, improve and visualise availability of IoT Systems
Pinter, Karim (2024)
2024
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024101026320
https://urn.fi/URN:NBN:fi:amk-2024101026320
Tiivistelmä
This thesis addresses the challenge of achieving availability goals in IoT (Internet of Things) systems. With the increasing integration of IoT devices into various domains, ensuring high availability becomes crucial for maintaining system functionality and reliability. The back-ground of the study involves identifying methods to meet availability targets set for IoT sys-tems.
The primary goal of this research is to develop a methodology for calculating, predicting, improving, and visualizing the availability of IoT systems. To achieve this goal, Monte Carlo simulations and Failure Mode and Effect Analysis (FMEA) risk analysis have been employed as the primary research methodologies. The theoretical background encompasses concepts of system availability, reliability engineering and risk assessment, providing a comprehensive foundation for the research approach.
Through the application of Monte Carlo simulations, the study generates availability predic-tions for IoT systems under various scenarios and conditions. FMEA risk analysis is utilized to identify potential failure modes and their effects on system availability, allowing for proac-tive mitigation strategies to be developed. The results reveal insights into the factors influ-encing system availability, highlight critical failure modes, and prioritize improvement efforts. Additionally, visualizations of availability data offer intuitive representations of system per-formance, aiding in decision-making processes.
Based on the findings, a development proposal is presented, emphasizing the integration of FMEA risk analysis into the ongoing monitoring and improvement processes. By incorpo-rating risk assessment into availability predictions and visualizations, organizations can bet-ter understand the potential impact of failures and prioritize resource allocation for risk miti-gation measures.
In conclusion, this thesis contributes to the understanding and management of availability in IoT systems. By leveraging Monte Carlo simulations, FMEA risk analysis, and visualizations, it offers a comprehensive approach to assess, predict, and enhance system availability. The proposed development strategy underscores the importance of integrating risk assessment into availability management practices for achieving and sustaining high levels of availability in IoT environments.
The primary goal of this research is to develop a methodology for calculating, predicting, improving, and visualizing the availability of IoT systems. To achieve this goal, Monte Carlo simulations and Failure Mode and Effect Analysis (FMEA) risk analysis have been employed as the primary research methodologies. The theoretical background encompasses concepts of system availability, reliability engineering and risk assessment, providing a comprehensive foundation for the research approach.
Through the application of Monte Carlo simulations, the study generates availability predic-tions for IoT systems under various scenarios and conditions. FMEA risk analysis is utilized to identify potential failure modes and their effects on system availability, allowing for proac-tive mitigation strategies to be developed. The results reveal insights into the factors influ-encing system availability, highlight critical failure modes, and prioritize improvement efforts. Additionally, visualizations of availability data offer intuitive representations of system per-formance, aiding in decision-making processes.
Based on the findings, a development proposal is presented, emphasizing the integration of FMEA risk analysis into the ongoing monitoring and improvement processes. By incorpo-rating risk assessment into availability predictions and visualizations, organizations can bet-ter understand the potential impact of failures and prioritize resource allocation for risk miti-gation measures.
In conclusion, this thesis contributes to the understanding and management of availability in IoT systems. By leveraging Monte Carlo simulations, FMEA risk analysis, and visualizations, it offers a comprehensive approach to assess, predict, and enhance system availability. The proposed development strategy underscores the importance of integrating risk assessment into availability management practices for achieving and sustaining high levels of availability in IoT environments.