IoT Reference Architectures (IoTWF vs oneM2M)
Compare the IoTWF and oneM2M architectures in terms of layer structure and design objectives.
IoTWF uses a 7-layer model with a focus on business and security integration across layers; oneM2M defines a horizontal service-layer architecture to support interoperability across platforms and vendors.
What is the role of the abstraction layer in the IoTWF model?
It processes, filters, and converts raw data into meaningful formats for applications, enabling decoupling between data producers and consumers.
What are the primary trade-offs between edge and fog computing?
Edge computing offers ultra-low latency and local decision-making; fog computing offers intermediate processing, better orchestration, and broader context
Design a 4-layer IoT system for smart water monitoring and justify each layer.
Sensors (things layer); wireless transmission (network); local processing gateway (edge/fog); dashboard interface (application layer).
Why is IPv6 essential in large-scale IoT systems, especially in industrial contexts?
It provides a massive address space, enabling unique identification of billions of devices, and supports auto-configuration and mobility.
How do both models support scalability in large-scale deployments?
IoTWF supports vertical scalability with clear separation of functional layers; oneM2M promotes horizontal scalability through service-layer abstraction and standardised APIs.
Describe how the application layer varies across consumer and industrial IoT.
Consumer IoT focuses on user interfaces and apps, while industrial IoT (IIoT) emphasizes analytics, automation, and integration with legacy systems.
Why might fog computing be preferred in a smart city environment?
It handles large-scale data aggregation from distributed sources, balances latency and computation, and provides localized intelligence.
How does topology influence redundancy and reliability in IoT networks?
Mesh topologies offer redundancy and fault tolerance; star topologies are simpler but prone to central point failures.
How does IT/OT convergence improve operational efficiency in industrial IoT?
It bridges data silos, enables predictive maintenance, and fosters real-time analytics, leading to smarter operations.
Why is interoperability critical in IoT, and which model addresses it better?
Interoperability ensures devices and applications can communicate regardless of vendor. OneM2M focuses more directly on this with global standards and open APIs.
Why is the security layer considered cross-cutting in IoTWF?
Because it applies to all other layers — from securing physical devices to ensuring encrypted data transmission and secure application access.
Give an example where edge computing would fail without fog computing.
In autonomous traffic management, edge nodes may lack the regional context fog computing provides for coordination among multiple intersections.
Compare MQTT and CoAP in terms of architectural fit and performance.
MQTT is a publish/subscribe protocol and is ideal for unreliable networks; CoAP is a request/response protocol, suitable for RESTful web integration.
What are the integration challenges when connecting OT sensors to IT cloud platforms?
Protocol mismatch, data format inconsistency, legacy systems, security vulnerabilities, and timing constraints.
In what ways does the business layer in IoTWF influence technical design decisions?
The business layer defines use-case goals and KPIs, guiding which technologies and service levels are required at lower architectural layers.
Explain how the network layer accommodates both constrained and high-power devices.
It supports lightweight protocols like 6LoWPAN and MQTT for constrained devices, while also allowing TCP/IP and Ethernet for more capable systems.
How does fog computing improve scalability in IoT architectures?
By offloading tasks from centralized clouds and allowing distributed nodes to manage localized data flows and analytics.
What factors affect gateway placement in IoT design?
Device density, signal range, latency requirements, power availability, and environmental conditions.
How do cloud and fog computing work together in IT/OT-integrated systems?
Fog computing performs real-time local analysis; cloud systems aggregate broader data for machine learning and strategic decision-making.
Map a smart healthcare IoT solution to the layers of IoTWF.
Things: medical sensors; Network: hospital LAN/Wi-Fi; Data abstraction: middleware; Application: monitoring dashboard; Business: care optimisation; Security: encryption & access control.
How can analytics drive decision-making at the business layer?
By transforming real-time sensor data into insights on trends, anomalies, and efficiencies, enabling businesses to act strategically.
Discuss the implications of fog computing on data privacy.
It enables sensitive data to be processed closer to its source, thereby reducing exposure to external threats and minimising data movement.
How does digitization influence physical network design in an IoT deployment?
It pushes the need for real-time data capture and transmission, shaping protocol choices, edge deployment, and redundancy planning.
Describe a scenario where IT/OT convergence created both value and risk.
In smart manufacturing, real-time telemetry reduced downtime (value), but exposed PLCs to external networks (risk).