# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 3 Transmission Line Monitoring Using WSN in Smart Grids
### Scenarios  
After a brief introduction of the considered environment, let us now move on to the discussion about miscellaneous WSN deployments and routing algorithms, which we prepared for our simulations and developed using QoP-ML. We proposed different scenarios, and analyzed them in terms of transmission time delay and security. As proved in [2], the linear network model proposed for transmission line monitoring in Figure 3 can not deliver information in a timely fashion. For this reason, instead of focusing only on a linear network model, we decided to analyze some other methods of data delivery as well. Instead of using hop-by-hop communication all over the line (Scenario 5), we proposed to divide all the nodes into smaller groups (called clusters) and route information to cluster heads, such that the cluster heads will communicate directly with the substation (Scenarios 1, 2, 3 and 4). A node that does not connect directly to the cluster head, should send its information in a hop-by-hop manner to the cluster head within its group. The selection of the cluster head is determined by its physical position on an electric pole. The node, which is located on the most appropriate position on a pole (in terms of, for instance, the lack of environmental interferences) is always chosen as a cluster head. The routing algorithm has the information about connections between sensor devices and the quality of paths used by devices for sending packets such that they can reach the substation. It also knows about which of them is the cluster head. We denoted the number of all nodes by \(\varphi\), while the number of hops within a single group (cluster) is denoted as \(\eta\). Then, the number of groups on power line (or, the number of direct links to base station) is given as:  
\[\kappa=\lceil\frac{\varphi}{(\eta\times 2)}\rceil\text{ where }\eta\,>\,0\,, \tag{2}\]  
and, in our case, \(\eta=[1,\ldots,23]\cap\{45\}\). Since the number of electric poles within the power line is fixed, we can assume that \(\varphi\) is constant and equal to 45. Thus, using the Formula (2), it is fairly easy to calculate \(\kappa\) (see Table 3). As mentioned before, \(\kappa\) values represent the number of wireless, cellular links, which the relay nodes (designated cluster heads) use to send collected data directly to the base station. Below we describe each of the prepared scenarios in greater detail. _Scenario 1_ Developing the first scenario, we examined the linear network model, assuming that there are 45 poles in total, and the power line has only 2 direct links to the substation. In this type of network (Figure 3), where the data traverses in a hop by hop manner, a relay node not directly connected to the substation sends its information to its neighbour relay, that is closer to the substation. For instance, the relay node on 42 nd pole, would send its data to 43 rd pole, which can then send its own data, together with the data from pole 42, to pole 44. After reaching the 44 th pole, the relay node on this pole sends the data to the substation, which transmits it to the gateway. In order to improve data delivery, we decided to divide considered power line in two sub-lines, where relay nodes on poles 0-21 send the information to the substation on the left side of the power line, while sink nodes on poles from 22 to 44 transmits the data to the second base station, located at the right end of the power line. Due to the fact that the distance between the sensor and the relay node is quite small, so a short-range communication technology suffices, we simply omitted this value in our simulations. _Scenario 2_ In this scenario, we consider a situation where every relay node on each of 45 poles has direct connection to the substation. Because there are no intermediary relay nodes between the relay node on a pole and the substation, logically this type of deployment should have the smallest delay time of all (Figure 5).