## 3 DMCAP System
### Multi-Channel Ensemble Model and Channel Attribute Classification Model
\[\mathrm{H}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{r}\_{\mathrm{n}_{ \mathrm{ai}}})=\mathrm{H}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{r}\_{ \mathrm{n}_{\mathrm{ai}}})-\mathrm{H}(\mathrm{r}_{\mathrm{n}_{\mathrm{ai}}}) \tag{12}\]  
\[\mathrm{I}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}};\mathrm{r}_{\mathrm{n}_{ \mathrm{ai}}})=\sum_{\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}\mathrm{r}_{ \mathrm{n}_{\mathrm{ai}}}}\mathrm{p}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}, \mathrm{r}_{\mathrm{n}_{\mathrm{ai}}})\cdot\log\frac{\mathrm{p}(\mathrm{t}_{ \mathrm{n}_{\mathrm{ai}}},\mathrm{r}_{\mathrm{n}_{\mathrm{ai}}})}{\mathrm{p}( \mathrm{t}_{\mathrm{n}_{\mathrm{ai}}})\cdot\mathrm{p}(\mathrm{r}_{\mathrm{n}_{ \mathrm{ai}}})} \tag{13}\]  
Similarly, the channel data transfer capacity at \(\tau\) is defined with \(\mathrm{I}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}};\mathrm{r}_{\mathrm{n}_{ \mathrm{ai}}})\) as mentioned in Equation (14). \[\mathrm{C}(\mathrm{I})=\max(\mathrm{I}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}; \mathrm{r}_{\mathrm{n}_{\mathrm{ai}}},\ \tau,\mathrm{c})) \tag{14}\]  
Equations (11)-(14) are denoting the channel entropy variances and determinations of EMLP (Algorithm 2). In this case, let the sensor nodes in an active communication channel are \(\mathrm{t}\_{\mathrm{n}_{\mathrm{ai}}}\) and \(\mathrm{r}\_{\mathrm{n}_{\mathrm{ai}}}\). In this circumstance, \(\mathrm{t}\_{\mathrm{n}_{\mathrm{ai}}}\) denotes the active transmitter and \(\mathrm{r}\_{\mathrm{n}_{\mathrm{ai}}}\) denotes the active receiver. \(\mathrm{E}_{\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{r}_{\mathrm{n}_{ \mathrm{ai}}}}\) denotes joint entropy function related to each active communication pair. \(\mathrm{H}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{t}_{\mathrm{i}})\) and \(\mathrm{I}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{t}_{\mathrm{i}})\) are denoting joint conditional functions of entropy model and information model. According to Equation (11), \(\mathrm{H}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{t}_{\mathrm{i}})\) ensures the data dissemination through other nodes (multi-path) based on logarithmic entropy distribution. ```
Input:\(\mathrm{H}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{r}\_{\mathrm{n}_{ \mathrm{ai}}}),\ \mathrm{I}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}};\mathrm{r}_{\mathrm{n}_{ \mathrm{ai}}}),\mathrm{C}(\mathrm{I})\) Output: Classified results and Entropy Knowledge Base Creation
1: Get all channel entropy determinations
2: Train the EMLP using training data, \(\mathrm{TE}(\mathrm{x},\mathrm{y},\tau)\)
3: Determine the entropy bias rate, \(\varnothing(\mathrm{Ui}-\mathrm{Ui}),\ \varnothing(\mathrm{Ui}-\mathrm{Si})\) and \(\varnothing(\mathrm{Si}-\mathrm{Bi})\)
4: Set minimal bias function as steepest decent for all data samples
5: EMLP backpropagation unit is modelled as below Do for all nodes: \[\frac{\mathrm{d}\omega_{\mathrm{x},\mathrm{y}}}{\mathrm{d}\tau}=-\frac{ \partial\varnothing}{\partial\omega_{\mathrm{x},\mathrm{y}}},\ \frac{\mathrm{d}\delta_{\mathrm{x},\mathrm{y}}}{\mathrm{d}\tau}=-\frac{ \partial\varnothing}{\partial\delta_{\mathrm{x},\mathrm{y}}}\]
6: Update the knowledge base, \(\mathrm{D}(\mathrm{E}(\mathrm{x}_{\mathrm{i}},\mathrm{y}_{\mathrm{i}},\tau))\)
7: Redo for all nodes in WUGSN. ```  
**Algorithm 2** EMLP for Entropy Learning and Classification  
In this case, data shall be fragmented and distributed according to divide and conquer approach since multiple paths are active to destination. The equation is remodeled as indicated to state the relationship between transmitter and forwarding nodes (sink) of multiple paths under logarithmic scale. Equation (12) indicates \(\mathrm{H}(\mathrm{t}_{\mathrm{n}_{\mathrm{ai}}}|\mathrm{r}\_{\mathrm{n}_{ \mathrm{ai}}})\) as a training function of distributed entropy model with neglected local entropy of each sink (forwarding node) since this proposed model uses EMLP to strictly consider channel entropy conditions for each session.