Data Fusion Architecture in IoT Networks

Multi-sensor data fusion is the process of merging data from multiple sources, including reinforcement learning techniques.

Multi-data fusion is more specific than multi-sensor data fusion, as it only deals with data from multiple sensors.

Multi-data fusion is classified based on known context (specific features, conditions, and targets) and input modality type (same or various types).

Multi-data fusion relies solely on supervised learning techniques.

The difficulty of multi-data fusion lies in building an interpreted fusion model based on multiple features and conditions within a given context.

Data fusion in smart systems plays a minor role in the success of the Internet of Things (IoT).

All data sources have the same target, organized architecture, input, context, and output in multi-data fusion.

The input modality type in multi-data fusion can classify the outcomes as direct and indirect fusion.

Direct sensory data fusion refers to the fusion of heterogeneous data from multiple sensors.

Multi-data fusion faces challenges related to data nature, such as homogeneous data, perfect data, and certain data.