# Energy Efficiency of IoT Networks for Environmental Parameters of Bulgarian Cities
## 3 Material and Methods  
Data for six cities in the Republic of Bulgaria were used. The data for the location of the measuring points were provided by the municipal administration of the respective city. Table 2 shows data on cities with their geographical coordinates and the number of points for measuring environmental parameters. The geographical coordinates are those of the administrative building of the respective city, which is assumed to be the location of the base station in the wireless sensor network. The cities of Shumen and Yambol have the lowest number of measuring points, and the most are in the city of Plovdiv. The distances between nodes and base station were calculated by the Euclidean distance method. The maximum distance of nodes or the radius of the wireless sensor network is specified. The shortest distance is in Burgas, and the longest distances are in Varna and Plovdiv. The average distances to the nodes are indicated. The largest is for T6 (Plowdiv) and the smallest in T1 (Burgas). The number of nodes that are close to the base station is also important. Most are in T1 (9), while in other cities there are only 1. Nearby nodes communicate more often with the base station and therefore their energy consumption is higher than that of others in the network. This increases the overall consumption of the network as a whole. Figure 1 shows maps in scale 1:75,000 and scale of 1 km of the cities in Bulgaria and the location of the measuring points (in blue). Open Street Map maps ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)), accessed on 1 April 2022) were used. Cities with a population of over 100,000 people were selected--Varna, Burgas, andPleven. In these cities, there are measuring networks with up to 5 points, and the other measurements were performed manually or by calculation. The smaller cities in terms of population are Shumen and Yambol. In them, the measurement of the environmental parameters was performed by manual measuring means or by calculation. It is typical for the cities in Bulgaria to have established measuring points, regardless of the method by which the measurement data are obtained. These measurement points were used in the present work as a basis for the simulation of wireless sensor networks, with possibilities for application in IoT. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Designation**} & \multirow{2}{*}{**City**} & \multicolumn{2}{c}{**Geographical Coordinates**} & \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Nodes at** \(\leq 2\) **km**} \\ \cline{3-3} \cline{5-7}  & & **\({}^{\circ}\)N** & & & & **of Nodes** \\ \hline T1 & Burgas & 42.501981 & 27.467896 & 39 & 8 & 3 & 9 \\ \hline T2 & Varna & 43.207884 & 27.906593 & 59 & 12 & 8 & 1 \\ \hline T3 & Shumen & 43.270546 & 26.935378 & 15 & 10 & 8 & 1 \\ \hline T4 & Yambol & 42.484188 & 26.508795 & 15 & 11 & 9 & 1 \\ \hline T5 & Plovdiv & 42.144543 & 24.744737 & 65 & 12 & 8 & 1 \\ \hline T6 & Pleven & 43.408031 & 24.619381 & 36 & 11 & 10 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data for cities in Bulgaria. The distance for transmitting measurement data is described by the following mathematical dependence [21]:  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{\left(4\pi\right)^{2}d^{2}} \tag{1}\]  
where \(P_{t}\) is the transmitted power; \(P_{r}\) is the received power; \(G_{t}\) is the gain of the transmitting antenna; \(G_{r}\) is the gain of the receiving antenna; \(\lambda\) is the wavelength; \(d\) is the distance between the receiving and transmitting parties. Euclidean distance is taken into account. This model assumes that the power received is a function of the distance to transmit and receive wireless signals. When designing a WSN for a specific city, it is necessary to take into account factors such as geographical location, the presence of buildings, trees, and the capabilities of the sensor devices used.