# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 4 Data Fusion
Individual sensor data identification is achieved by applying the identity declaration function \((g)\) over the feature extraction outcome \((P)\). Finally, \(Q\) is the outcome of joint identification in the direct fusion approach, using the function \(JID_{declaration}(. )\) and outcome \(Q\). * _Feature extraction followed by fusion_: In this approach, the sensor's data features are extracted initially, followed by feature-based data association. These associations facilitate the fusion of the data based on the feature's association and identity declaration. Finally, feature-based data fusion and identity declaration result in the sensor's joint identify declaration and data integration. This approach is formally presented as shown in Equations (19)-(22). \[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]0 (19)  
\[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]1  
\[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]2  
\[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]3  
Here, \(R\) is the outcome of feature extraction from each sensor. \(H(. )\) is a function to integrate feature-level data fusion. Finally, the data fusion outcome \(Q\) for the joint identification declaration is computed by applying a parallel integration function \(I(. )\) over the feature-level fusion outcome (\(U\)) and identity declaration outcome (V). * _Feature extraction followed by identity declaration and fusion for high-level inferences or decisions_: In this approach, the sensor's data features are extracted initially. After that, individual sensor data identities are declared from extracted features. These unique identities help in data association or in searching the required data. A unique identity-based data association is used for declaration-level fusion and identity declaration. Both of these processes result in a joint identity declaration. This approach is formally presented as shown in Equations (23)-(28). \[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]4 (23)  
\[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]5  
\[L(\alpha;\beta)\propto P(\alpha|\beta) \tag{10}\]6  
The multi-sensor data fusion approach works in a multi-layered architecture as well. In multi-layered architectures [57], multi-sensory data fusion or integration approaches work at different layers. For example, the multi-layered components of the data fusion architecture, proposed by Joint Directors of Laboratories (JDL) [58], are briefly explained as follows. * _Object Refinement_: This is considered as layer-1 processing in the JDL process model. Here, sensor-level data are combined to achieve better reliability, accuracy, position estimation, velocity measurement, attribute evaluations and identity exploration. * _Situation Refinement_: This is a layer-2 concept in the JDL process model. According to this layer, a dynamic process is applied to describe the present relationships among various entities considered in the experimentation. A list of associated events in the context of the environment has been prepared as well, as follows. * _Threat Refinement_: This is a layer-3 concept in the JDL process model. In this layer, present situations are explored in such a way that future trends can be observed. For example, the current military and defense deployment can give an idea about what the implications will be of enemy threats. Who will become a friend or foe? What opportunities will arise if operations are planned soon or over a long time? etc. * _Process Refinement_: This is a layer-3 concept in the JDL process model. In this layer, a meta-process is executed. In this meta-process, the overall data fusion processes for assessing and improving the real-time system or sub-system performances are performed. The layering process used for data fusion in the JDL architecture, the source-preprocessing, database management systems, and human-computer interaction (HCI) are essential components. Source-preprocessing manages the resources and related data for any redundancy, duplicity or loss.