## 3 Materials and Methods
### Process Involved in IAOAC Technique  
Once the nodes are deployed in the region of interest, the IAOAC technique is executed. The IAOAC method was developed with the presence of four suitability features, including the energy competence of IoT device nodes, the compactness of cluster nodes, the regular detachment of IoT devices for CHs contained by their sensing series, and the distances of CHs to sinks. The data on fitness parameters are shown in Equation (17).  
Energy competence: The CHs execute many events such as sensing, gathering, aggregation, data broadcast, etc.; thus, CHs intake more energy than other nodes. Hence, they can be vital to determine a fitness function (FF) that shares the load among all of the IoT devices from the system. The suitability limit for effective deployment of system energy is determined as follows:  
\[\begin{gathered} R_{e}=e(n_{i})\\ Avg_{e}=\frac{1}{n}\sum\limits_{i=0}^{n}e(n_{i})\\ f_{1}=CH_{opt}*\frac{R_{e}}{AVg_{e}}=\frac{CH_{opt}*e(n_{i})}{ \frac{1}{n}\Sigma_{i-0}^{n}e(n_{i})}\forall CH_{opt}=5\%\;of\;n,\;e(n_{i})\\ =0.5J\;or\;1.25J\;or\;1.75J\end{gathered} \tag{17}\]  
where \(R_{e}\), \(Avg_{e}\), and \(n_{i}\) refer to the node RE, network average energy, and total amount of IoT device nodes from the system, respectively. \(CH_{opt}\) signifies the optimum percentage of CHs.  
The number of nodes in a cluster is an important parameter to consider when trying to improve the energy efficiency of networks, since it affects the cost of intra-clustertransmissions. The cluster cost function was defined using the supplied function, which meant that the network energy deployment was of a higher quality than before:  
\[f_{2}=max\left(n(CH_{1}),n(CH_{2}),n(CH_{3})n\big{(}CH_{j}\big{)}\right),\ \forall n=2\ to\ 95,\ j=1\ to\ 15 \tag{18}\]  
where \(n\big{(}CH_{j}\big{)}\) refers to the number of IoT devices from the range of the \(j\)th CH \(\big{(}CH_{j}\big{)}\). The value of objective function \(f_{2}\) is higher than any choice of CH, and is utilised in decreasing the energy reduction.  
The regular detachment of IoT devices to the CH within its detection range occurs in intra-cluster transmission, and when IoT devices transmit data to CH. If the CH is away from CM, then IoT devices diminish their energy consumption once the CH is nearer to the member IoT device node; afterward, it deploys minimal energy.  
\[f_{3}=\frac{1}{n_{sr}}\sum_{i=0}^{n_{sr}}dist(CH,\ i)\ \forall dist(CH,\ i)=1 \ to\ 35\ m,n_{sr}=1\ to\ 100 \tag{19}\]  
where \(n_{sr}\) and \(dist(CH,\ i)\) signify the number of IoT devices from a sensing series and the Euclidean distance in node and CH from the perception sequence of individual clusters, respectively. Therefore, the rate of \(f_{3}\) is minimal but the diminishing intra-cluster transmits power.  
Detachment from CH to Base Station(BS): When using CH selection (CHS), however, the distances among the CHs and BSs are quite important. The reason for this is because the supplied function forecasts that if the CHs are located a significant distance from the sink, they will use energy at a rapid rate:  
\[f_{4}=\frac{1}{CH}\sum_{i=0}^{CH}dist(BS,\ CH_{i})\ \forall dist(BS,\ CH_{i})=1 \ to\ 70m,\ CH=1\ to\ 15 \tag{20}\]  
where \(dist(BS,\ CH_{i})\) implies the Euclidean distance between the BS and the \(i\)th CH \((CH_{i})\). Minimising the \(f_{4}\) impartial purpose states that the CHs are not far away from the BS.  
When the \(f_{1},f_{2},f_{3}\), and \(f_{4}\) purpose strictures are measured, the impartial purpose is called FF, which is represented as follows:  
\[FF=Maximize\ Fitness=\alpha*f_{1}+\beta*f_{2}+\gamma*\frac{1}{f_{3}}+\delta* \frac{1}{f_{4}} \tag{21}\]  
where \(\alpha,\beta,\gamma\), and \(\delta\) signify the weight coefficients to \(f_{1},f_{2},f_{3}\), and \(f_{4}\) FF parameters, respectively. The range of weight coefficients is varied between _zero_ and one.