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| en:iot-reloaded:systems_thinking_and_design_of_iot_systems [2024/12/03 16:21] – [System Dynamics Modeling for IoT Systems] 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. Increased productivity of rice plants based on IoT (internet of things) is needed to realize 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 and 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. |
| In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), Montreal, QC, Canada, 2015. IEEE.)). | In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), Montreal, QC, Canada, 2015. IEEE.)). |
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| While numerous IoT-based systems are being individually developed and tested by practitioners and researchers, these efforts often fall short of addressing the practical reality that IoT systems must ultimately interact with each other and human users. This interconnectedness underscores the need for a holistic, system-centric design methodology to effectively manage IoT systems' complexity and interdependencies. The design of these systems should move beyond isolated functionalities to consider the broader ecosystem in which they operate, including human interaction, cross-system communication, and scalability. | While numerous IoT-based systems are being individually developed and tested by practitioners and researchers, these efforts often fall short of addressing the practical reality that IoT systems must ultimately interact with each other and human users. This interconnectedness underscores the need for a holistic, system-centric design methodology to manage IoT systems' complexity and interdependencies effectively. The design of these systems should move beyond isolated functionalities to consider the broader ecosystem in which they operate, including human interaction, cross-system communication, and scalability. |
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| Several studies have ventured into leveraging methods and tools to design IoT systems—for example, research referenced in ((M. G. S. Wicaksono, E. Suryani, and R. A. Hendrawan. Increased productivity of rice plants based on iot (internet of things) is needed to realize smart agriculture using a system thinking approach. Procedia Computer Science, 197:607–616, 2021.)) utilised causal loop diagrams to study the intricate interactions between different systems and stakeholders, identifying key feedback loops influencing productivity. This approach provided actionable insights and recommendations on improving efficiency and performance within specific applications, such as smart agriculture. Using causal loop diagrams in such studies highlights the importance of visualising and understanding complex IoT ecosystems' relationships and feedback mechanisms. | Several studies have ventured into leveraging methods and tools to design IoT systems—for example, research 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. Procedia Computer Science, 197:607–616, 2021.)) utilised causal loop diagrams to study the intricate interactions between systems and stakeholders, identifying key feedback loops influencing productivity. This approach provided actionable insights and recommendations on improving efficiency and performance within specific applications, such as smart agriculture. Using causal loop diagrams in such studies highlights the importance of visualising and understanding complex IoT ecosystems' relationships and feedback mechanisms. |
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| However, it is crucial to incorporate both qualitative and quantitative system dynamics tools to advance the design and operational robustness of IoT systems. 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. By integrating both approaches, it becomes 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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| 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. | 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. |
| **Structured System Development** | **Structured System Development** |
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| In IoT design, linear thinking enables the structured development of systems by organizing tasks into sequential phases (figure {{ref>IoTSDM3}}): | In IoT design, linear thinking enables the structured development of systems by organising tasks into sequential phases (figure {{ref>IoTSDM3}}): |
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| <figure IoTSDM3> | <figure IoTSDM3> |
| - Selecting Hardware: Choose sensors, actuators, and devices that align with the objectives. | - Selecting Hardware: Choose sensors, actuators, and devices that align with the objectives. |
| - Designing Network Architecture: Establish connectivity protocols and infrastructure for seamless data transfer. | - Designing Network Architecture: Establish connectivity protocols and infrastructure for seamless data transfer. |
| - Developing Applications: Implement data analysis, visualization, and device control software. | - Developing Applications: Implement data analysis, visualisation, and device control software. |
| - Testing and Deployment: Validate system functionality before deployment and monitor post-deployment performance. | - Testing and Deployment: Validate system functionality before deployment and monitor post-deployment performance. |
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| **Troubleshooting and Optimization** | **Troubleshooting and Optimisation** |
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| Linear methodologies simplify troubleshooting in IoT systems. For example, diagnosing connectivity issues can follow a logical sequence (figure {{ref>IoTSDM4}}): | Linear methodologies simplify troubleshooting in IoT systems. For example, diagnosing connectivity issues can follow a logical sequence (figure {{ref>IoTSDM4}}): |
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| <figure IoTSDM4> | <figure IoTSDM4> |
| {{ :en:iot-reloaded:iot_system_design-page-4.png?600 |Linear Thinking in IoT Design Methodologies - Troubleshooting and Optimization Flow}} | {{ :en:iot-reloaded:iot_system_design-page-4.png?600 |Linear Thinking in IoT Design Methodologies - Troubleshooting and Optimisation Flow}} |
| <caption>Linear Thinking in IoT Design Methodologies - Troubleshooting and Optimization Flow</caption> | <caption>Linear Thinking in IoT Design Methodologies - Troubleshooting and Optimisation Flow</caption> |
| </figure> | </figure> |
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| - Check the device functionality. | - Check the device functionality. |
| - Verify network configurations. | - Verify network configurations. |
| - Analyze communication protocols. | - Analyse communication protocols. |
| - Inspect backend systems and applications. | - Inspect backend systems and applications. |
| - Integration of IoT Systems | - Integration of IoT Systems |
| - Complexity Management: IoT systems often involve interconnected components where feedback loops and dynamic interactions make linear methodologies insufficient. | - Complexity Management: IoT systems often involve interconnected components where feedback loops and dynamic interactions make linear methodologies insufficient. |
| - Inflexibility: Linear thinking may struggle to adapt to evolving requirements or unforeseen changes during development. | - Inflexibility: Linear thinking may struggle to adapt to evolving requirements or unforeseen changes during development. |
| - Limited Innovation: Focusing solely on predefined steps can hinder creative problem-solving, often needed in IoT for innovative use cases. | - Limited Innovation: Focusing solely on predefined steps can hinder creative problem-solving, which is often needed in IoT for innovative use cases. |
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| **Complementing Linear Thinking with Non-Linear Approaches** | **Complementing Linear Thinking with Non-Linear Approaches** |
| ===== 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> |
| - Observing User Behavior: Studying how users engage with their environment, existing devices, and technologies. | - Observing User Behavior: Studying how users engage with their environment, existing devices, and technologies. |
| - Conducting Interviews and Surveys: Gathering qualitative insights to uncover user needs, motivations, and pain points. | - Conducting Interviews and Surveys: Gathering qualitative insights to uncover user needs, motivations, and pain points. |
| - Analyzing Context-Specific Challenges: For IoT, this could mean understanding how users interact with connected devices in smart homes, healthcare, or industrial settings. | - Analysing Context-Specific Challenges: For IoT, this could mean understanding how users interact with connected devices in smart homes, healthcare, or industrial settings. |
| - Building Empathy Maps: Visual tools to document user behaviours, emotions, and thought processes. | - Building Empathy Maps: Visual tools to document user behaviours, emotions, and thought processes. |
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| - Data Privacy and Security: Designing user-centric IoT solutions must address data protection and compliance concerns. | - Data Privacy and Security: Designing user-centric IoT solutions must address data protection and compliance concerns. |
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| Design Thinking is an invaluable methodology for IoT design. It enables teams to create solutions prioritising users while addressing technical and business challenges. Its iterative and collaborative nature ensures that IoT systems remain adaptable, innovative, and effective. By integrating empathy, creativity, and feedback into the design process, Design Thinking helps organisations deliver IoT solutions that resonate deeply with users and stand out in a competitive landscape. | Design Thinking is an invaluable methodology for IoT design. It enables teams to create solutions that prioritise users while addressing technical and business challenges. Its iterative and collaborative nature ensures that IoT systems remain adaptable, innovative, and effective. By integrating empathy, creativity, and feedback into the design process, Design Thinking helps organisations deliver IoT solutions that resonate deeply with users and stand out in a competitive landscape. |
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| ===== Systems Thinking in IoT Design Methodologies ===== | ===== Systems Thinking in IoT Design Methodologies ===== |
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| Systems Thinking is a holistic approach to analyzing 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 organizational 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?** |
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| Systems Thinking views an IoT system as an integrated whole rather than isolated components. It emphasizes: | Systems Thinking views an IoT system as an integrated whole rather than isolated components. It emphasises: |
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| - Interconnections: Understanding how different devices, networks, and software interact. | - Interconnections: Understanding how different devices, networks, and software interact. |
| - Feedback Loops: Identifying how system outputs affect inputs, creating dynamic behaviours. | - Feedback Loops: Identifying how system outputs affect inputs, creating dynamic behaviours. |
| - Emergent Properties: Recognizing that the whole system often exhibits behaviours and capabilities that individual components cannot achieve alone. | - Emergent Properties: Recognising that the whole system often exhibits behaviours and capabilities that individual components cannot achieve alone. |
| - Context Awareness: Considering the system’s environment, including social, economic, and technological factors. | - Context Awareness: Considering the system's environment, including social, economic, and technological factors. |
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| For IoT, Systems Thinking ensures that solutions are robust, scalable, and adaptable to changing environments. | For IoT, Systems Thinking ensures that solutions are robust, scalable, and adaptable to changing environments. |
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| * Incorporate mechanisms to gather feedback from users and devices to adapt the system dynamically. | * Incorporate mechanisms to gather feedback from users and devices to adapt the system dynamically. |
| * Example: A smart irrigation system uses feedback from soil moisture sensors to optimize water usage based on weather patterns. | * Example: A smart irrigation system uses feedback from soil moisture sensors to optimise water usage based on weather patterns. |
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| **Focus on Context and Environment** | **Focus on Context and Environment** |
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| * Analyze how external factors, such as regulatory changes, technological advancements, and user behaviour, impact the IoT system. | * Analyse how external factors, such as regulatory changes, technological advancements, and user behaviour, impact the IoT system. |
| * Example: An industrial IoT system must account for varying factory conditions, such as temperature, humidity, and power fluctuations. | * Example: An industrial IoT system must account for varying factory conditions, such as temperature, humidity, and power fluctuations. |
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| Figure {{ref>iotstd3}} presents a workflow for the systems thinking approach for IoT design methodologies. Details are discussed below. | Figure {{ref>iotstd3}} presents a workflow for the systems thinking approach for IoT design methodologies. Details are discussed below. |
| <figure iotstd3> | <figure iotstd3> |
| {{ :en:iot-reloaded:iot_system_design-page-7.png?600 |}} | {{ :en:iot-reloaded:iot_system_design-page-7.png?600 |Systems Thinking in IoT Design Methodologies}} |
| <caption>Systems Thinking in IoT Design Methodologies</caption> | <caption>Systems Thinking in IoT Design Methodologies</caption> |
| </figure> | </figure> |
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| **Define the System’s Purpose and Boundaries** | **Define the System's Purpose and Boundaries** |
| * Clearly articulate the IoT system’s goals and scope. | * Clearly articulate the IoT system's goals and scope. |
| * Identify system boundaries to determine what lies within the system (devices, users, data flows) and outside (external regulations, competing systems). | * Identify system boundaries to determine what lies within the system (devices, users, data flows) and outside (external regulations, competing systems). |
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| Example: For a smart factory, the purpose might be to optimize production efficiency, and the boundaries might include connected machinery, inventory systems, and supply chain interactions. | Example: For a smart factory, the purpose might be to optimise production efficiency, and the boundaries might include connected machinery, inventory systems, and supply chain interactions. |
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| **Identify Components and Stakeholders** | **Identify Components and Stakeholders** |
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| * Catalog the IoT system’s physical and digital components (e.g., sensors, actuators, cloud platforms, edge devices). | * Catalog the IoT system's physical and digital components (e.g., sensors, actuators, cloud platforms, edge devices). |
| * Identify all stakeholders, including users, developers, IT administrators, and external partners. | * Identify all stakeholders, including users, developers, IT administrators, and external partners. |
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| **Map Interconnections and Data Flows** | **Map Interconnections and Data Flows** |
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| * Use tools such as system diagrams, flowcharts, or digital twins to visualize how components interact. | * Use tools such as system diagrams, flowcharts, or digital twins to visualise how components interact. |
| * Analyze the data flow between devices, gateways, cloud systems, and end-users. | * Analyse the data flow between devices, gateways, cloud systems, and end-users. |
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| Example: A connected vehicle system requires mapping interactions between GPS devices, onboard diagnostics, traffic data servers, and driver interfaces. | Example: A connected vehicle system requires mapping interactions between GPS devices, onboard diagnostics, traffic data servers, and driver interfaces. |
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| ** Analyze Feedback Loops** | ** Analyse Feedback Loops** |
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| * Identify positive and negative feedback loops to understand system dynamics. | * Identify positive and negative feedback loops to understand system dynamics. |
| * Design for self-correcting mechanisms that prevent system instability. | * Design for self-correcting mechanisms that prevent system instability. |
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| Example: In a smart thermostat, a feedback loop might ensure that when the temperature exceeds a set point, cooling systems are activated, and adjustments are logged for future optimization. | Example: In a smart thermostat, a feedback loop might ensure that when the temperature exceeds a set point, cooling systems are activated, and adjustments are logged for future optimisation. |
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| **Consider Scalability and Interoperability** | **Consider Scalability and Interoperability** |
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| * Treat security and privacy as systemic properties rather than add-ons. | * Treat security and privacy as systemic properties rather than add-ons. |
| * Evaluate vulnerabilities across the entire IoT ecosystem, including devices, networks, and cloud platforms. | * Evaluate vulnerabilities across the IoT ecosystem, including devices, networks, and cloud platforms. |
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| Example: In healthcare IoT, secure patient data transmission requires end-to-end encryption, secure APIs, and robust access control mechanisms. | Example: In healthcare IoT, secure patient data transmission requires end-to-end encryption, secure APIs, and robust access control mechanisms. |
| * Use iterative design to adapt to changing needs and technologies. | * Use iterative design to adapt to changing needs and technologies. |
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| Example: A smart logistics platform might adjust its route optimization algorithms based on real-time traffic patterns and delivery delays. | Example: A smart logistics platform might adjust its route optimisation algorithms based on real-time traffic patterns and delivery delays. |
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| **Benefits of Systems Thinking in IoT Design** | **Benefits of Systems Thinking in IoT Design** |
| - Enhanced Resilience: By understanding interdependencies, designers can create systems that withstand failures and adapt to changing conditions. | - Enhanced Resilience: By understanding interdependencies, designers can create systems that withstand failures and adapt to changing conditions. |
| - Scalability: Systems Thinking helps design IoT architectures that can grow seamlessly with increased demand. | - Scalability: Systems Thinking helps design IoT architectures that can grow seamlessly with increased demand. |
| - Improved Efficiency: Holistic optimization ensures that resources like bandwidth, power, and computational capacity are used effectively. | - Improved Efficiency: Holistic optimisation ensures that resources like bandwidth, power, and computational capacity are used effectively. |
| - Innovation: By analyzing emergent behaviours, Systems Thinking can uncover novel opportunities for functionality and value. | - Innovation: By analysing emergent behaviours, Systems Thinking can uncover novel opportunities for functionality and value. |
| - Sustainability: Considering environmental and social impacts ensures IoT solutions align with broader sustainability goals. | - Sustainability: Considering environmental and social impacts ensures IoT solutions align with broader sustainability goals. |
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| - Balancing Focus: Maintaining a high-level perspective while addressing detailed technical issues can be challenging. | - Balancing Focus: Maintaining a high-level perspective while addressing detailed technical issues can be challenging. |
| - Dynamic Environments: IoT systems often operate in rapidly changing contexts, requiring frequent reassessment and adaptation. | - Dynamic Environments: IoT systems often operate in rapidly changing contexts, requiring frequent reassessment and adaptation. |
| Stakeholder Alignment: Ensuring that all stakeholders understand and agree on the system's purpose and design can be challenging. | Stakeholder Alignment: It can be challenging to ensure that all stakeholders understand and agree on the system's purpose and design. |
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| Systems Thinking is an indispensable methodology for IoT design, offering a comprehensive framework to tackle the inherent complexity of interconnected systems. By focusing on interdependencies, feedback loops, and the broader context, Systems Thinking enables designers to create robust, scalable, and user-focused IoT solutions. Its emphasis on holistic analysis and adaptability ensures IoT systems meet current needs and evolve gracefully with emerging challenges and opportunities. | Systems Thinking is an indispensable methodology for IoT design, offering a comprehensive framework to tackle the inherent complexity of interconnected systems. Systems Thinking enables designers to create robust, scalable, and user-focused IoT solutions by focusing on interdependencies, feedback loops, and the broader context. Its emphasis on holistic analysis and adaptability ensures IoT systems meet current needs and evolve gracefully with emerging challenges and opportunities. |
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| ===== 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 emphasizing 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 hypothesized 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> |
| System dynamics provides a modelling framework for analysing the complex interactions between IoT systems. IoT systems consist of multiple interconnected components (such as sensor networks, data processing units, communication infrastructures, management systems, and stakeholders like policymakers and users) that work together to achieve the diverse goals of the stakeholders as shown in Figure {{ref>iotsdmcl1}}. Each IoT system comprises numerous interdependent parts interacting to perform their intended functions, and any modification in one part can affect the overall system performance. The effectiveness of IoT systems relies on the seamless interaction of all constituent components. However, these interactions, including stakeholder involvement, may lead to unintended consequences. Therefore, a system-centric approach is critical for designing and operating IoT systems to meet design objectives and address the expectations of all stakeholders. | System dynamics provides a modelling framework for analysing the complex interactions between IoT systems. IoT systems consist of multiple interconnected components (such as sensor networks, data processing units, communication infrastructures, management systems, and stakeholders like policymakers and users) that work together to achieve the diverse goals of the stakeholders as shown in Figure {{ref>iotsdmcl1}}. Each IoT system comprises numerous interdependent parts interacting to perform their intended functions, and any modification in one part can affect the overall system performance. The effectiveness of IoT systems relies on the seamless interaction of all constituent components. However, these interactions, including stakeholder involvement, may lead to unintended consequences. Therefore, a system-centric approach is critical for designing and operating IoT systems to meet design objectives and address the expectations of all stakeholders. |
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| The stakeholders involved may have conflicting priorities. For example, the main goal of system users might be to optimize operational efficiency, while the aim of technology developers could be to maximize data integration capabilities, and policymakers may focus on ensuring privacy, security, and environmental sustainability. Using the Systems Thinking framework, these stakeholders can apply tools such as causal loop diagrams to map the interconnections, feedback loops, and relationships (including nonlinear and causal dependencies) within the IoT ecosystem. Additionally, stock-and-flow models can be employed to simulate resource utilization (e.g., data processing capacity or energy consumption) and to monitor accumulations such as system load or greenhouse gas emissions in IoT-supported applications. These models enable the creation of predictive frameworks that management teams or policymakers can leverage to design interventions, ensuring that the goals of diverse stakeholders are met effectively and sustainably. | The stakeholders involved may have conflicting priorities. For example, the main goal of system users might be to optimise operational efficiency, while the aim of technology developers could be to maximise data integration capabilities, and policymakers may focus on ensuring privacy, security, and environmental sustainability. Using the Systems Thinking framework, these stakeholders can apply tools such as causal loop diagrams to map the interconnections, feedback loops, and relationships (including nonlinear and causal dependencies) within the IoT ecosystem. Additionally, stock-and-flow models can be employed to simulate resource utilisation (e.g., data processing capacity or energy consumption) and to monitor accumulations such as system load or greenhouse gas emissions in IoT-supported applications. These models enable the creation of predictive frameworks that management teams or policymakers can leverage to design interventions, ensuring that the goals of diverse stakeholders are met effectively and sustainably. |
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| **System Dynamics Modeling Framework**\\ | **System Dynamics Modeling Framework**\\ |
| * 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:**\\ |
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| **3. Delays:**\\ | **3. Delays:**\\ |
| Recognizing that the effects of actions or interventions often manifest after a time lag may impact decision-making. | Recognising that the effects of actions or interventions often manifest after a time lag may impact decision-making. |
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| **4. Perceptions:**\\ | **4. Perceptions:**\\ |
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| **7. Policies:**\\ | **7. Policies:**\\ |
| Rules and protocols, such as energy management policies or data prioritization schemes, govern decisions. | Rules and protocols, such as energy management policies or data prioritisation schemes, govern decisions. |
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| **8. Incentives:**\\ | **8. Incentives:**\\ |
| Motivations that drive individual or system-level actions, such as minimizing energy use or optimizing throughput. | Motivations that drive individual or system-level actions, such as minimising energy use or optimising throughput. |
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| **Defining Dynamics in IoT Systems** | **Defining Dynamics in IoT Systems** |
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| The system's dynamics are represented through graphs over time, capturing the variation of key variables and performance metrics as the system evolves. These graphs help to visualize: | The system's dynamics are represented through graphs over time, capturing the variation of key variables and performance metrics as the system evolves. These graphs help to visualise the following: |
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| * How specific interventions or policies influence the system. | * How specific interventions or policies influence the system. |
| * Variations in performance metrics such as latency, throughput, or energy consumption. | * Variations in performance metrics such as latency, throughput, or energy consumption. |
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| By leveraging simulation results, we aim to plot and analyze these variations, providing actionable insights into how IoT systems behave under different conditions. | By leveraging simulation results, we aim to plot and analyse these variations, providing actionable insights into how IoT systems behave under different conditions. |
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| **Why System Dynamics for IoT Systems?** | **Why System Dynamics for IoT Systems?** |
| * Predict unintended consequences of policy changes. | * Predict unintended consequences of policy changes. |
| * Enhance system resilience through robust design. | * Enhance system resilience through robust design. |
| * Optimize performance metrics such as energy efficiency, data flow, and service reliability. | * Optimise performance metrics such as energy efficiency, data flow, and service reliability. |
| * Improves the monitoring and control of industrial systems or critical infrastructures. | * Improves the monitoring and control of industrial systems or critical infrastructures. |
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