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en:iot-reloaded:systems_thinking_and_design_of_iot_systems [2024/12/10 21:06] – pczekalski | en:iot-reloaded:systems_thinking_and_design_of_iot_systems [2025/05/13 14:08] (current) – [System Dynamics Modeling for IoT Systems] pczekalski |
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===== The need for system-based IoT design methods ===== | ===== The need for system-based IoT design methods ===== |
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The Internet of Things (IoT) is still in its formative phase, presenting a critical window of opportunity to design and implement IoT systems that are scalable, cost-effective, energy-efficient, and secure. These systems must be developed to deliver acceptable Quality of Service (QoS) while meeting essential requirements such as interoperability and seamless integration across different devices and platforms. | The Internet of Things is still in its formative phase, presenting a critical window of opportunity to design and implement IoT systems that are scalable, cost-effective, energy-efficient, and secure. These systems must be developed to deliver acceptable Quality of Service (QoS) while meeting essential requirements such as interoperability and seamless integration across different devices and platforms. |
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Achieving these ambitious design objectives requires a comprehensive, system-based approach that considers the diverse priorities of various stakeholders, including network operators, service providers, regulatory bodies, and end users. Each group brings its requirements and constraints, and balancing these is essential to ensure the system's overall success. | Achieving these ambitious design objectives requires a comprehensive, system-based approach that considers the diverse priorities of various stakeholders, including network operators, service providers, regulatory bodies, and end users. Each group brings its requirements and constraints, and balancing these is essential to ensure the system's overall success. |
To support this, there is a significant need for the development of robust formal methods, advanced tools, and systematic methodologies aimed at designing, operating, and maintaining IoT systems, networks, and applications. Such tools and methods should be capable of guiding the process to align with stakeholder goals while minimising potential unintended consequences. This approach will help create resilient and adaptive IoT ecosystems that meet current demands and are prepared for future technological advancements and challenges. | To support this, there is a significant need for the development of robust formal methods, advanced tools, and systematic methodologies aimed at designing, operating, and maintaining IoT systems, networks, and applications. Such tools and methods should be capable of guiding the process to align with stakeholder goals while minimising potential unintended consequences. This approach will help create resilient and adaptive IoT ecosystems that meet current demands and are prepared for future technological advancements and challenges. |
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System thinking, design thinking, and systems engineering methodologies provide powerful frameworks for developing formal tools for designing and deploying complex IoT systems. These interdisciplinary approaches enable a comprehensive understanding of how interconnected components interact within a larger ecosystem, allowing for the creation of more resilient, efficient, and effective IoT solutions. | Systems thinking, design thinking, and systems engineering methodologies provide powerful frameworks for developing formal tools for designing and deploying complex IoT systems. These interdisciplinary approaches enable a comprehensive understanding of how interconnected components interact within a larger ecosystem, allowing for the creation of more resilient, efficient, and effective IoT solutions. |
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A practical example of leveraging these methodologies can be found in the work referenced in ((M. G. S. Wicaksono, E. Suryani, and R. A. Hendrawan. Increasing rice plant productivity based on IoT (Internet of Things) is needed to realise smart agriculture using a system thinking approach. | A practical example of leveraging these methodologies can be found in the work referenced in ((M. G. S. Wicaksono, E. Suryani, and R. A. Hendrawan. Increasing rice plant productivity based on IoT (Internet of Things) is needed to realise smart agriculture using a systems thinking approach. |
Procedia Computer Science, 197:607–616, 2021.)), where system dynamics tools were applied to design IoT systems for smart agriculture. Researchers constructed causal loop diagrams in this study to map and analyse the intricate interplay between multiple factors impacting rice farming productivity. By visually representing the causal relationships within the agricultural system, they identified key drivers and dependencies that influence outcomes. This insight allowed them to propose an IoT-based smart farming solution to optimise productivity through data-driven decision-making informed by these interdependencies. | Procedia Computer Science, 197:607–616, 2021.)), where system dynamics tools were applied to design IoT systems for smart agriculture. Researchers constructed causal loop diagrams in this study to map and analyse the intricate interplay between multiple factors impacting rice farming productivity. By visually representing the causal relationships within the agricultural system, they identified key drivers and dependencies that influence outcomes. This insight allowed them to propose an IoT-based smart farming solution to optimise productivity through data-driven decision-making informed by these interdependencies. |
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The value of system dynamics and systems engineering tools extends beyond smart agriculture. These methods can simplify the design and analysis of complex IoT systems, networks, and applications across various sectors. They offer a structured way to break down the complexity of interconnected systems, ensuring that the resulting IoT solutions are cost-effective, reliable but also secure, and energy-efficient. This approach ensures that the needs of diverse stakeholders—including developers, network operators, regulatory bodies, and end-users—are met effectively. | The value of system dynamics and systems engineering tools extends beyond smart agriculture. These methods can simplify the design and analysis of complex IoT systems, networks, and applications across various sectors. They offer a structured way to break down the complexity of interconnected systems, ensuring that the resulting IoT solutions are cost-effective, reliable, but also secure, and energy-efficient. This approach ensures that the needs of diverse stakeholders—including developers, network operators, regulatory bodies, and end-users—are met effectively. |
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Moreover, system dynamics tools have proven beneficial in educational contexts, particularly for teaching IoT courses. Educators can help students grasp the complexity of IoT systems and concepts more intuitively by adopting a system-centric approach. This holistic teaching method supports learners in understanding how various components and processes interact within an IoT ecosystem, thereby fostering a deeper comprehension of the subject matter and preparing them for real-world IoT challenges, as demonstrated in the findings of ((N. Silvis-Cividjian. Teaching Internet of Things literacy: A systems engineering approach. | Moreover, system dynamics tools have proven beneficial in educational contexts, particularly for teaching IoT courses. Educators can help students grasp the complexity of IoT systems and concepts more intuitively by adopting a system-centric approach. This holistic teaching method supports learners in understanding how various components and processes interact within an IoT ecosystem, thereby fostering a deeper comprehension of the subject matter and preparing them for real-world IoT challenges, as demonstrated in the findings of ((N. Silvis-Cividjian. Teaching Internet of Things literacy: A systems engineering approach. |
However, it is crucial to incorporate both qualitative and quantitative system dynamics tools to advance IoT systems' design and operational robustness. While causal loop diagrams are practical for modelling qualitative interactions and identifying feedback structures, quantitative methods are needed to simulate and analyse the dynamic behaviour of IoT systems under various conditions. Integrating both approaches makes it possible to model the structure and the real-time, data-driven interactions among different IoT components. | However, it is crucial to incorporate both qualitative and quantitative system dynamics tools to advance IoT systems' design and operational robustness. While causal loop diagrams are practical for modelling qualitative interactions and identifying feedback structures, quantitative methods are needed to simulate and analyse the dynamic behaviour of IoT systems under various conditions. Integrating both approaches makes it possible to model the structure and the real-time, data-driven interactions among different IoT components. |
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This highlights the urgent need to develop a comprehensive, multi-faceted framework that blends system thinking, design thinking, and systems engineering tools. Such an integrated approach would support the end-to-end design, operation, and maintenance of IoT systems, networks, and applications. The goal would be to create systems that align with the objectives of various stakeholders, including developers, service providers, network operators, regulators, and end-users while minimising unintended consequences such as system inefficiencies, vulnerabilities, or user dissatisfaction. | This highlights the urgent need to develop a comprehensive, multi-faceted framework that blends system thinking, design thinking, and systems engineering tools. Such an integrated approach would support the end-to-end design, operation, and maintenance of IoT systems, networks, and applications. The goal would be to create systems that align with the objectives of various stakeholders, including developers, service providers, network operators, regulators, and end-users, while minimising unintended consequences such as system inefficiencies, vulnerabilities, or user dissatisfaction. |
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System thinking enables a broad, interconnected view that helps identify and understand the relationships and dependencies across components. Design thinking ensures that solutions are user-centric, addressing real needs through iterative prototyping and feedback. Systems engineering brings discipline and structure, employing established methodologies and tools to optimise system performance and reliability. | Systems thinking enables a broad, interconnected view that helps identify and understand the relationships and dependencies across components. Design thinking ensures that solutions are user-centric, addressing real needs through iterative prototyping and feedback. Systems engineering brings discipline and structure, employing established methodologies and tools to optimise system performance and reliability. |
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IoT systems can be designed to be technically proficient, adaptable, scalable, and aligned with stakeholder needs by developing a framework that synergises these approaches. This will foster sustainable, resilient IoT ecosystems capable of evolving alongside technological advancements and societal demands, paving the way for a future where IoT seamlessly integrates into everyday life, supporting everything from smart cities to connected healthcare with minimal risk and maximal benefit. | By developing a framework that synergises these approaches, IoT systems can be designed to be technically proficient, adaptable, scalable, and aligned with stakeholder needs. This will foster sustainable, resilient IoT ecosystems capable of evolving alongside technological advancements and societal demands, paving the way for a future where IoT seamlessly integrates into everyday life, supporting everything from smart cities to connected healthcare with minimal risk and maximal benefit. |
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Integrating systems thinking, design thinking, and engineering methodologies into developing IoT systems can significantly enhance their design and implementation. These approaches facilitate the creation of robust, scalable, and efficient IoT solutions tailored to modern applications' complex requirements while addressing the stakeholders' needs. | Integrating systems thinking, design thinking, and engineering methodologies into developing IoT systems can significantly enhance their design and implementation. These approaches facilitate the creation of robust, scalable, and efficient IoT solutions tailored to modern applications' complex requirements while addressing the stakeholders' needs. |
===== Design Thinking in IoT Design Methodologies ===== | ===== Design Thinking in IoT Design Methodologies ===== |
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Design Thinking, a human-centred and innovative methodology, plays a transformative role in developing Internet of Things (IoT) solutions. By focusing on empathy, creativity, and collaboration, Design Thinking allows designers to craft IoT systems that deeply resonate with users, address real-world challenges, and deliver tangible value. This iterative and non-linear approach ensures that solutions remain user-focused while adapting to evolving needs and complexities. Below, we explore the application of Design Thinking to IoT design, breaking down its phases and highlighting its importance. The process is presented in a diagram (figure {{ref>dtiiotdm}}), and each step is described below. | Design Thinking, a human-centred and innovative methodology, plays a transformative role in developing Internet of Things solutions. By focusing on empathy, creativity, and collaboration, Design Thinking allows designers to craft IoT systems that deeply resonate with users, address real-world challenges, and deliver tangible value. This iterative and non-linear approach ensures that solutions remain user-focused while adapting to evolving needs and complexities. Below, we explore the application of Design Thinking to IoT design, breaking down its phases and highlighting its importance. The process is presented in a diagram (figure {{ref>dtiiotdm}}), and each step is described below. |
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<figure dtiiotdm> | <figure dtiiotdm> |
===== Systems Thinking in IoT Design Methodologies ===== | ===== Systems Thinking in IoT Design Methodologies ===== |
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Systems Thinking is a holistic approach to analysing and solving complex problems by understanding a system's relationships, interactions, and interdependencies. In the context of Internet of Things (IoT) design, Systems Thinking becomes crucial because IoT systems are inherently complex, comprising interconnected devices, networks, data flows, and user interactions. By adopting Systems Thinking, IoT designers can address the challenges of scalability, interoperability, and sustainability while ensuring that solutions align with user needs and broader organisational goals. | Systems Thinking is a holistic approach to analysing and solving complex problems by understanding a system's relationships, interactions, and interdependencies. In the context of Internet of Things design, Systems Thinking becomes crucial because IoT systems are inherently complex, comprising interconnected devices, networks, data flows, and user interactions. By adopting Systems Thinking, IoT designers can address the challenges of scalability, interoperability, and sustainability while ensuring that solutions align with user needs and broader organisational goals. |
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**What is Systems Thinking?** | **What is Systems Thinking?** |
===== System Dynamics Modeling for IoT Systems ===== | ===== System Dynamics Modeling for IoT Systems ===== |
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System dynamics is a practical application of Systems Thinking, originally developed at MIT in the 1950s. It provides a framework for understanding and modelling the complex behaviour of systems by emphasising the interconnections, feedback loops, and time delays inherent in such systems. Practitioners and researchers in system dynamics employ various modelling and simulation tools to explore the implications of hypothesised causal relationships and understand system dynamics over time. Sample closed-loop system dynamics modelling methodology is present in figure {{ref>iotsdmcl1}}. | System dynamics is a practical application of Systems Thinking, originally developed at MIT in the 1950s. It provides a framework for understanding and modelling the complex behaviour of systems by emphasising the interconnections, feedback loops, and time delays inherent in such systems. Practitioners and researchers in system dynamics employ various modelling and simulation tools to explore the implications of hypothesised causal relationships and understand system dynamics over time. A sample closed-loop system dynamics modelling methodology is present in figure {{ref>iotsdmcl1}}. |
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<figure iotsdmcl1> | <figure iotsdmcl1> |
{{ :en:iot-reloaded:iot_system_design-page-8.png?600 |Closed-loop System Dynamics Modelling Methodology}} | {{ :en:iot-reloaded:iot_system_design-page-8.png?600 |Closed-loop System Dynamics Modelling Methodology}} |
<caption>Closed-loop System Dynamics Modelling Methodology</caption> | <caption>Closed-loop System Dynamics Modelling Methodology</caption> |
* Battery Energy Systems: Energy content changes during charging and discharging cycles. | * Battery Energy Systems: Energy content changes during charging and discharging cycles. |
* Information Spread: The "population" of users influenced by fake news or disinformation over time. | * Information Spread: The "population" of users influenced by fake news or disinformation over time. |
* Stock changes: Changes of stocks in IoT-controlled production or industrial systems, e.g., changes in liquid level in IoT-controlled industrial system. | * Stock changes: Changes of stocks in an IoT-controlled production or industrial systems, e.g., changes in liquid level in an IoT-controlled industrial system. |
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**2. Causal Structures:**\\ | **2. Causal Structures:**\\ |