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en:iot-reloaded:iot_reference_architecture [2025/01/04 14:21] – [Data Processing Layer: The Intelligence Hub] pczekalskien:iot-reloaded:iot_reference_architecture [2025/01/04 14:42] (current) – [Application Layer] pczekalski
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-The data processing layer is responsible for aggregating, filtering, analysing, and deriving actionable insights from the data collected by IoT devices. Depending on the application's requirements, this layer can operate at the edge (closer to the devices) or in the cloud.+The data processing layer is responsible for aggregating, filtering, analysing, and deriving actionable insights from the data collected by IoT devices. Depending on the application's requirements, this layer can operate at the edge (closer to the devices) in the fog or the cloud.
  
 **Components** **Components**
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   - Edge Computing Devices: Localised processing units that enable near-real-time data analysis, reducing latency and bandwidth usage.   - Edge Computing Devices: Localised processing units that enable near-real-time data analysis, reducing latency and bandwidth usage.
   - Fog Computing Devices: Components located between the Edge and Cloud, fog computing devices provide distributed computing services that allow advanced data operations on a limited scale and ensure a more flexible approach to IoT data security and processing. They also optimise data transmission through aggregation and preprocessing for the Cloud Platforms.   - Fog Computing Devices: Components located between the Edge and Cloud, fog computing devices provide distributed computing services that allow advanced data operations on a limited scale and ensure a more flexible approach to IoT data security and processing. They also optimise data transmission through aggregation and preprocessing for the Cloud Platforms.
-  - Cloud Platforms: Centralised systems for large-scale data storage, advanced analytics, and machine learning model training.+  - Cloud Platforms: centralised systems for large-scale data storage, advanced analytics, and extensive AI tasks such as machine learning model training.
   - Data Pipelines: Tools for data ingestion, transformation, and integration with enterprise systems. Examples include Apache Kafka and AWS IoT Core.   - Data Pipelines: Tools for data ingestion, transformation, and integration with enterprise systems. Examples include Apache Kafka and AWS IoT Core.
   - AI and Analytics Engines: Algorithms and tools for predictive analytics, anomaly detection, and decision-making.   - AI and Analytics Engines: Algorithms and tools for predictive analytics, anomaly detection, and decision-making.
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 ===== Application Layer ===== ===== Application Layer =====
  
-The User Interaction and Value Creation Layer +The Application Layer is also known as the User Interaction and Value Creation Layer. 
-The application layer transforms processed data into end-user functionalities and value-driven solutions. It consists of software applications, services, and user interfaces that allow users to interact with and benefit from the IoT system.+The Application Layer transforms processed data into end-user functionalities and value-driven solutions. It consists of software applications, services, and user interfaces that allow users to interact with and benefit from the IoT system.
  
 **Components** **Components**
en/iot-reloaded/iot_reference_architecture.1736000493.txt.gz · Last modified: 2025/01/04 14:21 by pczekalski
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