## 5 Simulation Scenarios
### Case Study 1: Interactions in a Square Area
#### 5.1.1 Simulations Using Random Walk/SLAW  
Figure 4 represents the average energy consumption by node, using OLSR and OLSRp, when these nodes move according to a Random Walk model. These simulations consider several densities of nodes (_i.e._, 10, 20, 30 or 40 nodes in the area) and also node speeds (_i.e._, 1, 2, 4 and 6 m per second). The results obtained for HWSN deployed in this open area, indicate that the use of the predictor helps reduce energy consumption by 31% (average). Considering the average energy consumption we see four groups classified according to the number of nodes participating in the network. Clearly this consumption increases with the number of nodes in the area, which provides an initial insight about a possible answer to the RQ1 (_How does the node density affect the energy consumption?_). This is an expected result, since increasing the node density will increase the number of links among nodes; therefore each participant will have to route an extra traffic that impacts on the energy consumption. Although this behavior is present when using both protocols (_i.e._, OLSR and OLSRp), the participation of the predictor helps reduce the slope of the energy consumption. This means that OLSRp improves the performance of the HWSN (from an energy consumption point of view) in this scenario, and such improvement increases with the node density. The node speed in this open area seems to have negligible impact on the average energy consumption when OLSR is used. However, its impact is more interesting when using OLSRp. Moreover, the dispersion of the energy consumption decreases when the node speed increases. These results provide a preliminary answer to the RQ2 (_How does the speed of network nodes affect their energy consumption?_). Considering the maximum and minimum values of energy consumption we can see that the predictor tends to reduce the peaks, probably saving TC messages of nodes that are highly connected (e.g., central nodes of the HWSN) and that involve low mobility. Moreover, the percentage of this reduction seems to be directly related to the dispersion level of the energy consumption in each scenario. The larger the energy consumption dispersion, the larger the energy savings produced by the predictor. Similar results are shown in the simulations when the nodes use a SLAW mobility pattern (Figure 5). In this case the node speed is not a variable of the motion model, due to the nodes moving according to their recent story [52]. Therefore, they do not abruptly change their direction or speed. For that reason their energy consumption is considerably less than in the Random Walk model. Nodes in SLAW tend to move together and keep the network topology quite stable, thus they save TC messages (and energy). It provides an initial answer to the RQ3 (_How does the mobility pattern of the nodes affect the energy consumption?_). Figures 6 and 7 indicate how the network degree affects the average energy consumption of the nodes in the previously presented interaction scenarios. This uncertainty was specified as RQ4. The results show that the network degree seems to be related to the number of nodes deployed in the area, which is not surprising due to the fact that we are considering a square area. Moreover, energy consumption behaves slightly different when considering Random Walk model and SLAW model. On the one hand, the average energy consumption increases linearly with the network degree when nodes move following a SLAW model. On the other hand, the average consumption grows exponentially when nodes assume a Random Walk model. The nodes that follow a SLAW model tend to have a network degree higher than those adhering to a Random Walk mobility model. This can be explained because in SLAW the nodes tend to make small changes in their direction and speed, therefore they do not use the whole available space (_i.e_., they are less scattered in the physical scenario) [52]. On the contrary, when nodes use a Random Walk model, the network tends to be larger and have less links.