# 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  
### Network Model  
In the energy-constraint IoT network, all the smart IoT nodes are deployed in the environment randomly. These sensor nodes form clusters as a Fuzzy logic-based chicken swarm genetic optimization (HIOA) algorithm. Only CH is able to send aggregated data to nearby edge nodes. Finally, edge node transferred data to the cloud network for data storage that can be used for data analytics with the following assumptions about IoT nodes below. All the smart IoT sensor nodes are homogenous in nature in terms of energy and computation capabilities (such as data collection, transmission and data aggregation). All these wireless IoT nodes periodically observe the environment and send data to their nearby CH. Initially, all the IoT nodes start with an equal quantity of energy stored in the battery. Using the received signal strength indicator (RSSI), IoT nodes calculate their distance from edge nodes by finding their co-ordinate (\(X_{t},Y_{t}\)). An equal amount of energy  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocols** & **Issues** & **Techniques** & **Contributions** & **Metrics** & **Limitations** & **Publication Year** \\ \hline GAOC [21] & Selection of the optimum number of CHs & Selection of & Multiple data & residual & Parameter & \\  & Selection of & Genetic & sinks to & energy, node & intra-cluster & \\  & number of & Algorithm & overcome & density and & distance have & \\  & CHs & & hotspot & node & been not & \\ \hline LEACH-FL [27] & Clustering & Fuzzy Logic & Improved & & & \\  & process and & Inference & clustering & & & Left out \\  & routing & System & process & energy & Node density & \\ \hline EC-PSO [28] & Hotspot & Particle & Improved & & & Left out node & \\  & problem & swarm & fitness & residual & distance to & \\  & intelligence & function & & & base station & \\  & & intelligence & function & energy, & causes & \\  & & & & & exhaust more & \\ \hline CRCGA [29] & Load-balance & Genetic & Select & & & \\  & clustering & Genetic & optimal CHs & & & \\  & process with & Algorithm & and best & & & \\  & routing & & route & & & \\ \hline ICSO-LA [30] & Localization & & Prevent from & & & Node delay & \\  & & & IoT nodes to & & & and node & \\  & & & falling into & & & density is not & \\  & error & & local & & & \\  & & & optimum & base station & & \\  & & & & & & \\ \hline SEOANS [31] & Optimize & Cuckoo & Calculation & & & \\  & beam pattern & Search and & method for & & & \\  & in WSNs & CSO & node location & & & \\  & & & & & & \\  & & & & & & \\ \hline Interruptible load & Power & & Solve the & & Green energy & \\  & balance & & interruptible & & & resource and \\  & between & & load & & peak load by & delay & \\  & supply and & & scheduling & & reducing cost & constraint is & \\  & demand & & demand & & neglected & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative study of recent research works.  
is consumed by the symmetrical channel in packet transfer in either direction from A to B or B to A. In addition, IoT nodes can adjust their transmission power for packet transfer according to distance either from the edge node or CH. The operational period is divided into two phases: the setup phase and the steady state phase. A crystal-clear observation of a single transmission period is further explained in Section 3.3.4 Transmission Procedure of HIOA Algorithm.