# Energy Efficiency of IoT Networks for Environmental Parameters of Bulgarian Cities
## 4 Results and Discussion
This makes the location of the nodes close to the base station and therefore increases the amount of data exchanged between them. A zoomed example for T1 is given. The changes are in the range near 0.0006 J. Figure 6 shows the level of power consumption in wireless sensor networks, depending on the number of rounds. The greater the number of nodes in the network, the greater the energy consumption. For example, T2 has 59 nodes and, compared to the others, has the highest values of energy consumption. Although some networks also have a large number of nodes, their energy consumption is lower due to the shorter transmission distances. Figure 7 shows the number of packets received (N\({}_{\text{p}}\)) in the wireless sensor networks in relation to the number of rounds. As can be seen from the graph, the number of packets received increases as more rounds mean more data are transmitted to the base station. With the number of rounds, the number of received packets remains high enough. It can be seen  
Figure 4: Average level of energy consumption for WSN by cities. that different plots are overlapped. The six curves are close to each other, showing that for the six WSNs, there is no clear trend showing that the number of packet losses on a long path is higher than that on a short path, otherwise the nodes should receive more packets with a smaller number of rounds. An analysis of the principle components has been made. Table 4 shows the normed values (in the interval \([0,1]\)) of the WSN characteristics presented above. It can be seen that the normalized value of Np in all cases is Np = 0. This is because, as the graph shows, the  
Figure 5: Remaining energy levels for WSN by cities. Figure 6: Energy consumption levels for WSN by cities. Figure 7: Number received packets of WSN by cities. six curves for this characteristic are close to each other. The number of columns is 6 and the number of rows is 5. By rows, the data can be reduced to a maximum of four principal components, and by columns to a maximum of five principal components. The first three principal components were chosen because they describe a variance in the data of over 95%. The results are shown in Figure 8. PCA analysis is used to show the cities, grouped by their specific WSN energy consumption parameters. There is a grouping of cities, depending on energy consumption in the WSN. The coastal cities T1 and T2 are in a common group, where the remaining energy level in the network has the highest values. The smaller cities T3 and T4 are in a common group and their total energy consumption is the best compared to other cities. Networks with approximately the same radii in cities T5 and T6 are in a common group and their average level of energy consumption has the best characteristics compared to other cities. The number of received packets remains in the center of the coordinate system, which shows that it is the same for all cities. From the analysis of variants for wireless sensor networks for six cities in Bulgaria, it was found that at distances up to 2 km and a number of nodes over 30, higher values of energy consumption in the wireless sensor network are obtained. To reduce energy consumption, it is necessary to apply optimized routing protocols. [PERSON] et al. [PERSON] et al. (2019) offer such a protocol/algorithm called distance-based dynamic duty-cycle allocation (DBDDCA). Through this protocol, nodes located at great distances from the base station transmit for shorter periods of time, which reduces the energy consumed in the network as a whole.