# 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
#### 3.3.2 Adapted Chicken Swarm Genetic Optimization for Green Communication
#### 3.3.4 Operational Procedure for HIOA

An operational period for the presented HIOA algorithm operates in a setup and steady-state period that is repeated in each round. Thus, each IoT node has the chance to play the role of CHs in blanching the energy consumption in the IoT network. Firstly, in the setup period, CH election and binding of IoT nodes subject to cluster formation takes place. To get knowledge about IoT nodes, edge nodes advertise beacon messages over the network. In turn, IoT nodes respond with their ID, co-ordinate and residual energy. Using the RSSI model, IoT nodes calculate their distance from the edge node by finding their co-ordinate \((X_{l},Y_{l})\) as follow:

\[\text{RSSI}=-(10\textit{$g$ log}_{10}\textit{$z_{ie}$}+\text{A}) \tag{13}\]

Figure 3: Workflow of the HIOA Algorithm. where, \(\varphi\) represents the coefficient of signal propagation parameter alias exponent, \(z_{ie}\) is the usual distance between edge node to an IoT node and A denotes the obtained signal strength in one meter distance without obstacle. Further, the edge node uses the presented HIOA for the election of \(K\) number of CHs. Furthermore, edge nodes send messages to each IoT node in the \(K\) pack to inform nodes as CHs. Each IoT node in the pack advertises its role as cluster member CM or CHs containing its ID. Those remaining nodes that do not belong to any pack choose the nearby CH based on minimum communication cost through the join message. Second, in the steady state period, the major goal is to avoid data collisions occurring during the gathering of data by parent CH from its child CM nodes. Then, CHs schedule the data gathering using the TDMA technique. Now, CM sends their observed data to their respective CH that include ID and residual energy. This local information is useful in deciding on the role of CH for the next round in a pack. CH applies a data aggregation algorithm to remove redundancy in the gathered data. Finally, CH sends a packet of fixed size to the edge node with the local information of CM's. ## 4 Results and Discussion

The performance of the HIOA algorithm is compared with a state-of-the-art algorithm using MATLAB 2019A. This section is divided into two parts: (i) CSGO parameter analysis and (ii) network parameter analysis. In addition, 200 IoT nodes are randomly distributed over the IoT network size 150 \(\times\) 150 m\({}^{2}\) with edge node set at the corner position of the network. The initial power of the nodes is 1 J, similar to the first order radio model used in paper [20]. CSO is initializing with a random solution in the search space and applied to minimize the fitness function that yields a single solution with all features selected as the initial solution. For CSO, the maximum generation is set to be 60, the number of roosters is between 0.1 to 0.4 percentages randomly, and the number of hens is between 0.2 to 0.8 randomly. The list of other parameters and their values is listed in Table 3, which is similar to paper [29; 31]. ### CSGO Parameter Analysis

In this subsection, the consequences of four different parameters on the fitness performance of the presented algorithm HIOA are analyzed. These parameters are (i) population size (\(pop_{size}\)) (ii) number of rooster (\(N_{r}\)) (iii) number of hens' (\(N_{h}\)) and (iv) swarm updating frequency \(\theta\). In addition, the number of chicks can be evaluated as \(N_{c}=N-N_{r}-N_{h}\). From the above analysis of the result from Figure 4a-d, we observed that the presented algorithm achieves a better fitness value of about 0.625 within 60 iterations and after that more iteration, the variation in fitness value is negligible. This can be attributed to the reason the presented algorithm evaluates the fitness value on the basis of three optimization