## 5 Results for Evaluating the Proposed Method
### Experiment Results  
The methodology outcomes are compared to the ABC, GA, and PSO algorithms and suggested method in this section. The relative performance for the varied number of iterations is evaluated by computing the objective function and how it functions to optimize the method, as illustrated in Figure 10. The fitness of 100 generations is evaluated in this simulation. The fitness function grows as the generations grow. The fitness quantity among the 30 th and 100 th generations has fallen to its minimum point, 0.48, as can be observed. \begin{table}
\begin{tabular}{c c} \hline _Parameter_ & **Amount** \\ \hline _Number of IoT nodes_ & 10-300 \\ _Number of servers in each IoT node_ & 1–5 \\ _Number of tasks_ & 50-500 \\ _Data Size_ & 10–15 Mb \\ _Computing intensity_ & 300 cycle/bit \\ _Cost_ & 1–105 \\ _Energy_ & 1–10 mj \\ _Processing time_ & 1–10 s \\ \hline _Parameters of the proposed algorithm_ \\ \hline _Iteration_ & 100 \\ _popSize_ & 50 \\ _KELossRate_ & 0.85 \\ _MoleColl_ & 0.50 \\ _InitialKE_ & 0 \\ _alpha_ & 1 \\ _beta_ & 10 \\ _buffer_ & 0 \\ \hline _GA parameters_ \\ \hline _Number of chromosomes (solutions)_ & 100 \\ _Selection operator_ & Roulette wheel \\ _Cutting operator_ & Single point \\ _Probability of crossover_ & 0.8 \\ _Mutation rate_ & 0.1 \\ _Maximum number of generations_ & 100 \\ \hline _PSO parameters_ \\ \hline _Number of particles (solutions)_ & 100 \\ _Inertial weight_ & First0.9 then decrease to 0.4 \\ _CI_ & Rand \(*\) 2 \\ _C2 (C1 + C2 \(\leq\) 4)_ & Rand \(*\) 1.5 \\ _Maximum speed_ & Number of Rand tasks \\ _Maximum number of generations_ & 100 \\ \hline _ABC Parameters_ \\ \hline _Number of bees (solutions)_ & Three times higher the number of IoT nodes \\ _Maximum number of generations_ & 100 \\ _Onlooker_ & 50 \\ _Scout bee_ & 1 \\ _Employed bees_ & 50 \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. The rest of this section describes the analysis of the simulation results and shows simulation diagrams of the load balance degree, energy consumption, and delay time. * _Load Balance Degree_ In the investigation, the nodes in layer 1 have easy accessibility to the IoT gateway, while the nodes from other levels communicate data to the nodes in layer 1 through Zigbee links. Hence, the data traffic at layer 1 is significantly higher than at other levels. Therefore, load balancing at layer 1 is important. We propose a load balance degree (LBD) derived using the standard deviation (SD) of the traffic load and the load average (LA) across all nodes in a layer to assess the load balancing performance according to Equation (2):  
\[\text{LBDY}=[1-(\text{SDY LA})]\times 100\% \tag{2}\]  
where LBD, SD, and LA are illustrated with LBDY, SDY, and LAY in layer \(Y\). SDY = 0 indicates that all nodes in layer \(Y\) possess identical traffic loads. The load balance performance is the best in this situation, and LBDY is 100%. When LBDY is 0, that means SDY equals LDY. It means that because the variance of traffic load in layer \(Y\) equals the average load, this layer's load balancing is inadequate. If LBDY is negative, the load balancing is considerably worse since it means that certain nodes' traffic loads are much higher than the average plus SD. The comparison of the results of the imbalance degree of the state-of-the-art to the proposed method is shown in Figure 11.  
Figure 10: The result for fitness in 100 iterations. * _Energy consumption_ Each mobile contains its own set of CPU cores (\(\rm PL_{j}=\{p|j,1\}\), \(\rm pl_{j,2}\),..., \(\rm pl_{j,n}\)). Equation (3) calculates the energy usage of each core depending on the total CPU cycles (\(\rm C_{ci}\)), voltage supply (\(\rm V_{local,j,k}\)), operating frequency (\(\rm f_{local,j,k}\)), and effective commutative capacitance (\(\rm C_{local,j,k}\)). Equation (3) calculates the IoT device's local dynamic energy consumption throughout each task.