# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 4 Experimental Results and Discussion  
To evaluate the sensor network deployment, which is described in Section 3, the BA and POS algorithms are developed and compared using three parameters in this section. To conduct the numerical experiment, two health and environmental application have been addressed: monitoring water quality and monitoring medical equipment in a hospital. In these scenarios, finding optimal positions of the sensor nodes so that the coverage and lifetime are maximized is so challengeable. Monitoring water pollution or water quality of water resources is the top priority issue for sustainable development these days. WSN is the suitable solution to address the issue. Due to the high cost of placement and battery change of the sensors in the water resource especially for underwater resource, it is necessary to deploy WSN with the minimum number of sensors and maximizing the coverage and lifetime. Another scenario is monitoring a wheelchair in a hospital. As the number of medical equipment such as wheelchairs are limited in a hospital, it is important to monitor this equipment. Hospitals are sensitive areas so that constructions (placing and changing the sensor nodes) are not possible at any time and at any place, due to creating pollution. Hence, to deploy WSN for monitoring medical equipment it is important to find not only the optimal positions of the sensors but also the coverage and lifetime of the network. These scenarios indicate the importance of GIS-based optimization algorithms for WSN deployment. For these scenarios, the experiment area is simulated with 1000 m \(\times\) 1000 m region where it is required to deploy a fixed number of sensor nodes. In this experiment study, the same parameters were considered for all sensor nodes are considered as for both algorithms. In addition, the parameters of the objective function (Equation (13)) are set same for both algorithms: \(\alpha\) = 0.6; \(\beta\) = 0.4. The value of \(C_{R}\) in this numerical study is the area of the simulated region, 10\({}^{6}\) m\({}^{2}\). \begin{tabular}{l l l l l l l l l l} \(n\) = 20; & \(R_{c}\) = 200 m; & \(E_{dcc}\) = 50 nJbit; \\ \(E\) = 5 j; & \(R_{s}\) = 300 m; & \(\epsilon_{amp}\) = 100 pJbit; \\ \end{tabular}   
The major step of applying evolutionary algorithms is algorithm design for solving the WSN deployment in health and environmental applications. Parameter calibration, for instance population size and number of iterations to choose right parameter values, is an important kind of algorithm design since the parameter values have an effect on the performance of the algorithms [86; 87; 88]. One of the methods for the parameter calibration is the parameter tuning in which the parameter values are chosen in the initialization step and don't change when the algorithm is running [86; 88]. Therefore, two algorithms were implemented with different parameter values in the simulated region to calibrate the parameters. The BA parameters that are needed to be set are the number of iteration, employed bees, better bees, elite bees, onlooker bees and neighborhood search. For PSO algorithm, the number of iteration and particles as well as inertia weight, acceleration coefficient and constant parameter are needed to be calibrated. The results of the implementation of BA and PSO algorithms with different values are presented in Tables 1 and 2, respectively. Table 1 indicates that by increasing the searching distance of the better bees (dbp), the fitness values were not getting better, but when that of the best bees (nep) was increased until value 10, the fitness values were changed toward a better value. This issue was contrary to the number of onlooker bees. In other words, increasing the number of onlooker bees for elite and better bees (nep and nbp) leads to better fitness values. Finally, row 20, which has better fitness value, was selected as the parameter values of BA.