## 5 Experimental Results
### Part I--Results of Weighted Sum Optimization
At first, the weighted sum results of exhaustive/full simulations are presented in Table 3. In the table, since _macMinBE_ must be less than _macMaxBE_, the value of _macMaxBE_ is fixed as the maximum value for _macMinBE_ in each row of the table. The range of weighted sum results are given in each fixed _macMaxBE_ case and the total simulation time is also provided. \begin{table}
\begin{tabular}{c c c c} \hline
**Fixed _macMaxBE_** & **Parameter Combinations** & **Weight Sum Range** & **Simulation Time (min)** \\ \hline
3 & 192 & 0.504349 \(\sim\) 0.812070 & 34.397 \\
4 & 240 & 0.498976 \(\sim\) 0.982988 & 43.622 \\
5 & 288 & 0.497637 \(\sim\) 1.292583 & 53.144 \\
6 & 336 & 0.507912 \(\sim\) 1.747466 & 61.901 \\
7 & 384 & 0.507912 \(\sim\) 1.834899 & 69.886 \\
8 & 432 & 0.507912 \(\sim\) 1.935943 & 76.702 \\
**Total** & 1,872 & 0.497637 \(\sim\) 1.935943 & 339 \\ \hline \end{tabular}
\end{table} 
Table 3: Exhaustive/Full simulation results. The GA optimizations were launched with the settings of 2 eitlecount, 0.8 crossoverfraction, and the roulette based selection process. A larger population size for individuals and more generations can certainly provide better optimization results, but at the cost of time. In this experiment, with the general efficiency and availability of the GA-based framework under the test, four population sizes with only one generation were measured (5/10/20/30 populationsize, 1 generation), since they were able to provide good results. Under each population size case, GA ran 100 times to determine the average results, because different seed values were generated according to the current time in every GA run. Hence, the randomly formed initial parent individuals, the children after crossover and mutation could be different, which led to different final results. Table 4 gives the efficiency information in terms of time and availability information in terms of the statistics probability of results falling into the weighted sum range area. The results show that even with a small population size of 5, the probability of achieving cost values inside 7% of the weighted sum range area is over 95%, while the optimization time is only about 1/264 compared to the exhaustive simulation. When the population size is raised to 10, there is over a 90% probability of achieving the area, which is 3% in the weighted sum range area, while the time is about 1/123 of the exhaustive simulation. Performances are further improved when the population size reaches 20 and 30, with all the results within the 3% of the weight sum area at a small cost of optimization time, which is about 1/64 and 1/42 respectively. On the other hand, for non-expert researchers and users who do not have much knowledge in how the configuration of the unslotted algorithm can affect performances, the default configuration ([_macMinBE=3, macMaxBE=5, macMaxCSMABackoffs=4, macMaxFrameRetries=3_]) of the algorithm could always be the priority of their choice for the comparison. In this experiment, the default configuration weighted sum value is 0.737, so even with 5 population sized GA optimization all solutions can have better performance than this default configuration. Note that if another set of seed values are used, the above results could be different, but not significantly so, according to our tests. In addition, similar efficiency and availability can be achieved for MICAz, MICA2, iHop@Node based networks under the iMASK framework. On the other hand, when a larger scale scenario (e.g., 80 Telos nodes) is considered in iMASK, firstly the optimization time will increase, because the SystemC simulation needs more time for a larger scale network scenario.