# Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
## 3 Green Communication in the Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
### Hybrid Intelligent Energy Optimization
#### 3.3.1 Adapted Fuzzy Logic Interference System for Green Communication  
In the first optimization phase, a tentative number of CHs is selected based on three parameters in an IoT network: residual energy (\(I_{te}\)), node density (\(I_{nd}\)), distance to edge node (\(I_{de}\)). The reason behind the selection of these parameters as follow: \(I_{te}\) relate to IoT node becoming a CH having enough energy to successfully execute the operation of clustering. \(I_{nd}\) relates to the number of neighboring IoT nodes corresponding to the selected node as CH, if \(I_{nd}\) is in sparse of nature to the CH, then communication cost increases. Moreover, the worst-case cluster member's nodes exhaust all energy in the transmission of observed information because it is far from CH. \(I_{de}\) defining the CH should be at an optimal position so that it manages the communication cost both from cluster member's nodes and edge nodes.  
The linguistic variable for mamdani type FLIS as input: \(I_{te}\) = {below (BW), upright (UT), great (GT)}, \(I_{nd}\) = {sparse (SE), average (AE), compact (CT)} and \(I_{de}\) = {nearby (NB), midway (MD), far (FR)}. These parameters are fed into FLIS and in turn output probability as a chance calculated for an IoT node to become CH or not. The linguistic variable of the output probability chance as follow: \(I_{ch}=\){beginner (BR), naive (NE), amateur (AR), strong (SG), hard (HD), harder (HR), hardest (HT)}. The extreme linguistic variables are denoted by trapezoidal membership functions (MFs), and the middle ones are from triangular MFs, as shown in Figure 2a-d. The If-Then rules (input \(3^{3}=27\)) for evaluation of output probability are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rule**} & & **If** & \multicolumn{2}{c}{**Then**} & \multicolumn{2}{c}{**If**} & \multicolumn{2}{c}{**Then**} \\ \cline{2-10}  & \(I_{te}\) & \(I_{nd}\) & \(I_{de}\) & \(I_{ch}\) & & \(I_{tr}\) & \(I_{nd}\) & \(I_{de}\) & \(I_{ch}\) \\ \hline
1. & BW & SE & NB & BR & 15. & UT & AE & FR & AR \\
2. & BW & SE & MD & BR & 16. & UT & CT & NB & SG \\
3. & BW & SE & FR & NB & 17. & UT & CT & MD & SG \\
4. & BW & AE & NB & NE & 18. & UT & CT & FR & AR \\
5. & BW & AE & MD & BR & 19. & GT & SE & NB & HD \\
6. & BW & AE & FR & BR & 20. & GT & SE & MD & HD \\
7. & BW & CT & NB & NE & 21. & GT & SE & FR & SG \\
8. & BW & CT & MD & NE & 22. & GT & AE & NB & HT \\
9. & BW & CT & FR & BR & 23. & GT & AE & MD & HR \\
10. & UT & SE & NB & NE & 24. & GT & AE & FR & HD \\
11. & UT & SE & MD & AR & 25. & GT & CT & NB & HT \\
12. & UT & SE & FR & NE & 26. & GT & CT & MD & HR \\
13. & UT & AE & NB & SG & 27. & GT & CT & FR & HD \\
14. & UT & AE & MD & AR & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy logic rules.  
Figure 1: Block diagram of HIOA.  
The FLIS evaluates the output probability in four steps as follows: (i) Fuzzification--this step creates MFs of the crisp input variables according to intersection point. (ii) Fuzzy rule-base--all 27 If-Then rules executed parallel on the given three input variables to generate single output. This can be done by using a fuzzy minimum AND operator from the selection of three input MF parameters. (iii) Aggregation--as there is multiple output value generated corresponding to 27 rules, to aggregate as one single output, maximum union operator OR is used. (iv) Defuzzification--the center of area is used for defuzzification of aggregated value into a crisp output. After the selection of CHs process, the remaining nodes join the nearest CHs through the join message.